├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── NEWS ├── R └── mitch.R ├── README.md ├── data ├── genesetsExample.RData ├── k36a.RData ├── k9a.RData ├── myImportedData.RData ├── myList.RData ├── resExample.RData └── rna.RData ├── inst ├── CITATION ├── extdata │ ├── k36a.tsv │ ├── k9a.tsv │ ├── new.hgnc.table.rds │ ├── rna.tsv │ └── sample_genesets.gmt ├── mitch.Rmd └── script │ ├── README.txt │ └── example_datagen.R ├── man ├── genesetsExample.Rd ├── gmt_import.Rd ├── k36a.Rd ├── k9a.Rd ├── mitch.Rd ├── mitch_calc.Rd ├── mitch_import.Rd ├── mitch_plots.Rd ├── mitch_report.Rd ├── myImportedData.Rd ├── myList.Rd ├── network_genes.Rd ├── networkplot.Rd ├── resExample.Rd └── rna.Rd ├── tests └── test-mitch.R └── vignettes ├── infiniumMethArrayWorkflow.Rmd └── mitchWorkflow.Rmd /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: mitch 2 | Title: Multi-Contrast Gene Set Enrichment Analysis 3 | Version: 1.21.3 4 | Authors@R: c( 5 | person(given = "Mark", 6 | family = "Ziemann", 7 | role = c("aut","cre","cph"), 8 | email = "mark.ziemann@gmail.com", 9 | comment = c(ORCID="0000-0002-7688-6974")), 10 | person(given="Antony", 11 | family="Kaspi", 12 | role = c("aut","cph")) 13 | ) 14 | Description: mitch is an R package for multi-contrast enrichment analysis. At 15 | it’s heart, it uses a rank-MANOVA based statistical approach to detect sets 16 | of genes that exhibit enrichment in the multidimensional space as compared 17 | to the background. The rank-MANOVA concept dates to work by Cox and Mann 18 | (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for 19 | pathway analysis of profiling studies with one, two or more contrasts, 20 | or in studies with multiple omics profiling, for example proteomic, 21 | transcriptomic, epigenomic analysis of the same samples. mitch is perfectly 22 | suited for pathway level differential analysis of scRNA-seq data. 23 | We have an established routine for pathway enrichment of Infinium Methylation 24 | Array data (see vignette). 25 | The main strengths of mitch are that it can import datasets easily from 26 | many upstream tools and has advanced plotting features to visualise these 27 | enrichments. 28 | Depends: R (>= 4.4) 29 | Suggests: 30 | stringi, 31 | testthat (>= 2.1.0), 32 | HGNChelper, 33 | IlluminaHumanMethylation450kanno.ilmn12.hg19, 34 | IlluminaHumanMethylationEPICanno.ilm10b4.hg19 35 | Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r, kableExtra, dplyr, network 36 | License: CC BY-SA 4.0 + file LICENSE 37 | Encoding: UTF-8 38 | URL: https://github.com/markziemann/mitch 39 | BugReports: https://github.com/markziemann/mitch 40 | LazyData: false 41 | RoxygenNote: 7.3.2 42 | biocViews: GeneExpression, GeneSetEnrichment, SingleCell, Transcriptomics, Epigenetics, Proteomics, DifferentialExpression, Reactome, DNAMethylation, MethylationArray, DataImport 43 | VignetteBuilder: knitr 44 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Creative Commons Attribution-ShareAlike 4.0 International Public License 2 | By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-ShareAlike 4.0 International Public License ("Public License"). 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If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. 93 | No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. 94 | Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. 95 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(gmt_import) 4 | export(mitch_calc) 5 | export(mitch_import) 6 | export(mitch_plots) 7 | export(mitch_report) 8 | export(network_genes) 9 | export(networkplot) 10 | import(GGally) 11 | import(echarts4r) 12 | import(ggplot2) 13 | import(grDevices) 14 | import(graphics) 15 | import(grid) 16 | import(gridExtra) 17 | import(kableExtra) 18 | import(knitr) 19 | import(parallel) 20 | import(stats) 21 | import(utils) 22 | importFrom(MASS,kde2d) 23 | importFrom(beeswarm,beeswarm) 24 | importFrom(dplyr,bind_rows) 25 | importFrom(dplyr,mutate) 26 | importFrom(gplots,heatmap.2) 27 | importFrom(network,network) 28 | importFrom(plyr,join_all) 29 | importFrom(plyr,ldply) 30 | importFrom(reshape2,melt) 31 | importFrom(rmarkdown,render) 32 | -------------------------------------------------------------------------------- /NEWS: -------------------------------------------------------------------------------- 1 | CHANGES IN VERSION 1.19.4 2 | ------------------------ 3 | o New networkplot visualisation has been added to help interpret enrichment results. 4 | 5 | CHANGES IN VERSION 1.19.3 6 | ------------------------ 7 | o Fixed a breaking bug in mitch_plots() for 2D analysis. 8 | 9 | CHANGES IN VERSION 1.15.5 10 | ------------------------ 11 | o The default number of cores has been set to 1. 12 | 13 | o An issue with contrasts being identical has been resolved. 14 | 15 | CHANGES IN VERSION 1.15.4 16 | ------------------------ 17 | o A bug with tables in the HTML reports has been fixed. 18 | 19 | o Minor improvements to the gmt_import() function to prevent empty sets. 20 | 21 | CHANGES IN VERSION 1.15.2 22 | ------------------------ 23 | o A new vignette has been included to demonstrate how to conduct enrichment analysis of profiles 24 | generated with HM450K and EPIC arrays. 25 | 26 | CHANGES IN VERSION 1.15.1 27 | ------------------------ 28 | o A change of the gene aggregation algorithm to allow enrichment analysis of infinium methylation analysis. 29 | 30 | CHANGES IN VERSION 1.3.1 31 | ------------------------ 32 | o A fix to the colnames in seurat V4 33 | 34 | CHANGES IN VERSION 1.0.12 35 | ------------------------ 36 | o A fix to problems in >3D plots caused by contrast names 37 | 38 | 39 | CHANGES IN VERSION 1.0.10 40 | ------------------------ 41 | o A fix to problems in plotting sparse scRNA-seq data 42 | 43 | CHANGES IN VERSION 1.0.4 44 | ------------------------ 45 | o A fix to accommodate absolute paths for mitch_report output files 46 | 47 | CHANGES IN VERSION 1.0.2 48 | ------------------------ 49 | o A fix to issues with the interactive charts in the Rmd output 50 | 51 | 52 | CHANGES IN VERSION 0.99.15 53 | ------------------------ 54 | o Making changes in response to bioconductor review 55 | 56 | 57 | 58 | CHANGES IN VERSION 0.2.2 59 | ------------------------ 60 | o Test 61 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # mitch 2 | 3 | mitch is an R package for multi-dimensional enrichment analysis. At it's heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. Mitch is useful for pathway analysis of profiling studies with two to or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. Mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. 4 | 5 | 6 | 7 | ## Installation 8 | 9 | ``` 10 | if(!requireNamespace("BiocManager", quietly = TRUE)) 11 | install.packages("BiocManager") 12 | BiocManager::install("mitch") 13 | 14 | library("mitch") 15 | ``` 16 | 17 | ## Workflow overview 18 | 19 | ### Importing gene sets 20 | 21 | mitch has a function to import GMT files to R lists (adapted from [Yu et al, 2012](https://dx.doi.org/10.1089%2Fomi.2011.0118) in the [clusterProfiler](http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) package). For example: 22 | 23 | ``` 24 | download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", destfile="ReactomePathways.gmt.zip") 25 | unzip("ReactomePathways.gmt.zip") 26 | genesets <- gmt_import("ReactomePathways.gmt") 27 | ``` 28 | 29 | ### Importing profiling data 30 | 31 | mitch accepts pre-ranked data supplied by the user, but also has a function called `mitch_import` for importing tables generated by Limma, edgeR, DESeq2, ABSSeq, Sleuth, Seurat and Muscat. By default, only the genes that are detected in all contrasts are included, but this behaviour can be modified. The below example imports two edgeR tables called "dge1" and "dge2". Where gene identifiers are coded in the row names. 32 | 33 | ``` 34 | x <- list("dge1"=dge1, "dge2"=dge2) 35 | y <- mitch_import(x, DEtype="edger") 36 | ``` 37 | 38 | mitch can do unidimensional analysis if you provide it a single profile as a dataframe. 39 | 40 | ``` 41 | y <- mitch_import(df, DEtype="edger") 42 | ``` 43 | 44 | If the gene identifiers are not given in the rownames, then the column can be specified with the `geneIDcol` parameter like this: 45 | 46 | ``` 47 | y <- mitch_import(df, DEtype="edger", geneIDcol="MyGeneIDs") 48 | ``` 49 | 50 | For edgeR, differential gene expression is scored using the directional nominal p-value. 51 | 52 | S = -log10(p-value) * sgn(logFC) 53 | 54 | For DESeq2, the test statistic is used. 55 | If this is not desired, then users can perform their own custom scoring procedure. 56 | 57 | There are many cases where the gene IDs don't match the gene sets. To overcome this, `mitch_import` also accepts a two-column table that relates gene identifiers in the profiling data to those in the gene sets. 58 | 59 | `?mitch_import` provides more instructions on using this feature. 60 | 61 | ### Calculating enrichment 62 | 63 | The `mitch_calc` function performs multivariate enrichment analysis of the supplied gene sets in the scored profiling data. At its simpest form `mitch_calc` function accepts the scored data as the first argument and the genesets as the second argument. Users can prioritise enrichments based on small adjusted p-values, or by the observed effect size (magnitude of the enrichment score). 64 | 65 | In general, effect size prioritisation will give you more specific and actionable results, that 66 | highlights relatively small gene sets with large magnitude of change. 67 | If you prioritise by significance, you will highlight larger gene sets with more subtle expression 68 | changes. 69 | As a general rule, our group typically presents enrichment results prioritised by effect size after 70 | excluding gene sets with FDR>0.05. 71 | 72 | ``` 73 | res <- mitch_calc(y, genesets, priority="significance") 74 | res <- mitch_calc(y, genesets, priority="effect") 75 | ``` 76 | 77 | In some cases, you may want to prioritise the results based on the diversity of gene expression 78 | differences across multiple contrasts. 79 | This will highlight gene sets that have opposite and diverse s.dist scores across contrasts. 80 | This is particularly interesting for scRNA-seq analysis where you may want to know how different cell 81 | types respond to a stimulus, so you are interested in the gene sets with the biggest variation in 82 | s.dist scores across the contrasts. 83 | 84 | ``` 85 | res <- mitch_calc(y, genesets, priority="SD") 86 | ``` 87 | 88 | Note that SD prioritisation won't work for 1D analysis. 89 | 90 | One `mitch_calc()` is finished, you can peek at the top results with `head` like this: 91 | 92 | ``` 93 | head(res$enrichment_result) 94 | ``` 95 | 96 | By default, `mitch_calc` uses only thread for calculation, but you can speed it up using more 97 | parallel threads, by setting the `cores` option. 98 | We suggest 8-12 parallel threads for returning results the fastest. 99 | Using more than 12 doesn't yield faster results typically. 100 | Keep in mind that the more parallel threads used, the more RAM is required. 101 | 102 | ``` 103 | res <- mitch_calc(y, genesets, priority="significance", cores=4) 104 | ``` 105 | 106 | By default, gene sets with fewer than 10 members present in the profiling data are discarded. This threshold can be modified using the `minsetsize` option. There is no upper limit of gene set size. 107 | 108 | ``` 109 | res <- mitch_calc(y, genesets, priority="significance", minsetsize=5) 110 | ``` 111 | 112 | By default, in downstream visualisation steps, charts are made from the top 50 gene sets, but this can be modified using the `resrows` option. 113 | 114 | ``` 115 | res <- mitch_calc(y, genesets, priority="significance", resrows=100) 116 | ``` 117 | 118 | ### Generate a HTML report 119 | 120 | Can be done like this: 121 | 122 | ``` 123 | mitch_report(res, "myreport.html") 124 | ``` 125 | 126 | Take a look at an [example report](https://github.com/markziemann/mitch_paper/blob/master/figs/myreport.html). 127 | 128 | ### Generate high resolution plots 129 | 130 | In case you want the charts in PDF format, these can be generated as such: 131 | 132 | ``` 133 | mitch_plots(res, outfile="mycharts.pdf") 134 | ``` 135 | 136 | Take a look at an [example plot set](https://github.com/markziemann/mitch_paper/blob/master/figs/mycharts.pdf). 137 | 138 | 139 | ### Network plot 140 | 141 | For one-dimensional analysis, you can generate a network plot to visualise the similarity of the driver gene members for the top (20) up- and downregulated sets meeting a significance cut-off (FDR<0.05). 142 | These cut-offs can be altrered. 143 | 144 | ``` 145 | networkplot(res,FDR=0.05,n_sets=20) 146 | ``` 147 | 148 | See `?networkplot()` for more details. 149 | 150 | If you want to see which genes are contributing to the network chart, use the `network_genes()` function. 151 | 152 | ``` 153 | network_genes(FDR=0.05,n_sets=20) 154 | ``` 155 | 156 | ### scRNA-seq 157 | 158 | This type of data is notoriously sparse, so pseudobulk aggregation is recommended. 159 | Mitch works best if there are >1000 genes detected in each cell type (contrast). 160 | If there are <400 genes present it may cause mitch to run into an error in the MANOVA test due to insufficient degrees of freedom. 161 | It is recommended to run the analysis with all three prioritisation schemes (significance, effect and 162 | SD) to interpret the data. 163 | 164 | ### Infinium DNA methylation data 165 | 166 | Mitch can be applied to Infinium Beadarray methylation data if you have limma results for probes. 167 | Please see our solution workflow on [Bioconductor](https://www.bioconductor.org/packages/devel/bioc/vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.html). 168 | 169 | ### Contrbutions 170 | 171 | If you have questions or need help with applying mitch to your work, please raise an issue. 172 | -------------------------------------------------------------------------------- /data/genesetsExample.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/data/genesetsExample.RData -------------------------------------------------------------------------------- /data/k36a.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/data/k36a.RData -------------------------------------------------------------------------------- /data/k9a.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/data/k9a.RData -------------------------------------------------------------------------------- /data/myImportedData.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/data/myImportedData.RData -------------------------------------------------------------------------------- /data/myList.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/data/myList.RData -------------------------------------------------------------------------------- /data/resExample.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/data/resExample.RData -------------------------------------------------------------------------------- /data/rna.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/data/rna.RData -------------------------------------------------------------------------------- /inst/CITATION: -------------------------------------------------------------------------------- 1 | citEntry(entry="article", 2 | title = "mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data", 3 | author = personList( as.person("Antony Kaspi"), 4 | as.person("Mark Ziemann")), 5 | year = 2020, 6 | journal = "BMC Genomics", 7 | doi = "10.1186/s12864-020-06856-9", 8 | volume = 21, 9 | issue = 1, 10 | pages = 447, 11 | textVersion = 12 | paste("Kaspi, A., Ziemann, M.", 13 | "mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data", 14 | "BMC Genomics 21(1):447 (2020)" ) ) 15 | -------------------------------------------------------------------------------- /inst/extdata/k36a.tsv: -------------------------------------------------------------------------------- 1 | logFC logCPM PValue adj.p.value 2 | FKBP5 1.1916 3.22832 2.14438e-16 3.86113e-12 3 | GAK -1.01804 4.2125 4.59689e-14 2.15924e-10 4 | KLF9 1.29779 2.25663 4.73992e-08 1.34757e-05 5 | TNXB -0.70271 3.49999 7.29617e-08 1.90397e-05 6 | CPED1 0.735761 3.52825 8.006e-08 2.03993e-05 7 | DUSP1 1.1597 2.07258 1.59706e-07 3.55748e-05 8 | ACAP3 -1.41504 2.02057 4.80875e-07 8.18131e-05 9 | MCL1 1.60702 1.35387 5.75398e-07 9.50506e-05 10 | WBP1L 0.910931 2.74217 5.81049e-07 9.56915e-05 11 | LTBP2 -0.815001 3.06065 6.355e-07 0.000103398 12 | TTYH2 -0.793839 3.15817 8.88562e-07 0.000132307 13 | TMEM259 -1.58854 1.44524 1.22482e-06 0.000169645 14 | SYNCRIP 0.945835 2.15156 2.52109e-06 0.000294768 15 | DYNC1H1 -0.788875 3.30405 2.58289e-06 0.000300745 16 | NR4A2 0.775924 3.32481 2.75427e-06 0.000315878 17 | MEF2BNB -1.50821 1.17715 2.88742e-06 0.000327327 18 | CTB-52I2.4 1.02078 2.67654 3.41148e-06 0.000373036 19 | CTD-2003C8.2 1.13027 1.91201 4.36261e-06 0.000445899 20 | HTT -0.483921 4.0538 5.09337e-06 0.000500785 21 | HES1 1.01614 2.1572 5.69932e-06 0.000544061 22 | PK 0.736647 2.79815 6.43259e-06 0.000591255 23 | SCARF2 -1.15858 2.65322 6.55315e-06 0.000597948 24 | MYO15A -0.655289 4.07527 6.56276e-06 0.000598319 25 | HIVEP2 1.01362 2.45503 7.8515e-06 0.000677505 26 | HELZ2 -0.915752 3.14526 9.11676e-06 0.000754157 27 | TMEM175 -0.997653 2.27109 1.23686e-05 0.000948358 28 | GATA2 -1.09543 2.50962 1.4809e-05 0.00107618 29 | RYR1 -0.671248 3.69131 1.52141e-05 0.00109797 30 | PRRC2A -1.36892 0.984216 2.17978e-05 0.00140509 31 | CPEB4 0.789852 2.46039 2.91592e-05 0.00173015 32 | ARFGAP1 -2.03475 0.464708 3.08625e-05 0.00179646 33 | ACTN4 -0.617892 3.50433 3.45234e-05 0.0019446 34 | FES -1.06109 2.31805 3.62006e-05 0.00201076 35 | SEMA3F -0.93175 2.33797 3.73991e-05 0.00205724 36 | ATG4B -0.7635 2.77986 3.80891e-05 0.00208458 37 | ATP1B1 1.60293 0.717362 4.97099e-05 0.00252725 38 | NPEPL1 -0.860641 2.95682 5.933e-05 0.00284623 39 | KLF6 0.79944 2.82203 6.39707e-05 0.0029983 40 | AMDHD2 -1.55674 1.08397 6.67576e-05 0.00309093 41 | AC006480.1 -1.81769 0.661412 6.8744e-05 0.00315609 42 | P2RX6 -1.32938 1.23466 7.44607e-05 0.00333135 43 | HIPK3 0.828092 2.54704 7.75423e-05 0.00342489 44 | FBLN2 -0.599046 3.8314 7.85794e-05 0.00345516 45 | HSPG2 -0.674265 4.7043 8.58629e-05 0.0036752 46 | ATP11A -0.426063 4.55292 8.9099e-05 0.00376008 47 | TRIB1 0.76823 2.40564 9.57752e-05 0.0039553 48 | MSX1 -1.01464 1.89418 0.000117062 0.00454758 49 | FOXO3 0.62967 2.87898 0.000124103 0.00471596 50 | ACTG1 -1.70295 0.607184 0.000126247 0.00476908 51 | SETD5 -0.649437 3.36868 0.000129964 0.00487523 52 | PLEKHA1 0.637117 2.8128 0.00013501 0.00500886 53 | STAT2 -0.883929 1.81369 0.000141965 0.00517798 54 | MASP2 -0.823576 2.12417 0.000142681 0.00519008 55 | DLL4 -1.24365 1.88211 0.000144669 0.00524123 56 | NR1D1 1.07221 1.99805 0.000148136 0.00531691 57 | LINC01372 5.25066 -1.39217 0.000150098 0.00536041 58 | CMYA5 0.618935 2.72673 0.000151458 0.00539668 59 | WDR34 -0.829041 2.23203 0.000156834 0.00553964 60 | CDK12 -0.615913 3.2038 0.000181078 0.00609242 61 | CSRNP1 0.808068 2.36833 0.00018895 0.00625021 62 | PPP2R3B -0.781799 3.34761 0.0001891 0.00625112 63 | TP53INP2 0.7531 2.06058 0.000202812 0.00653467 64 | SUPT6H -0.760734 3.10591 0.000221143 0.00695172 65 | PTPRS -0.73214 3.18588 0.000232172 0.0071932 66 | TRDN 0.720192 2.61732 0.000232463 0.0071981 67 | CHD4 -0.572669 3.3195 0.000270627 0.00796087 68 | TRAF3IP2 0.826676 2.06027 0.000280984 0.00818029 69 | MMRN2 -0.757454 2.21174 0.000331215 0.00914928 70 | C16orf58 -0.925685 1.80842 0.00035864 0.00960477 71 | VGLL2 0.935271 1.87425 0.000358767 0.0096058 72 | ENGASE -0.671796 2.77428 0.000376772 0.00995222 73 | LATS2 0.665576 2.58152 0.000463205 0.0114174 74 | ATF6B -1.11373 0.902588 0.000467283 0.0114786 75 | FAM65A -0.750672 2.22549 0.000468109 0.0114894 76 | DOC2B -0.736188 2.65565 0.000555339 0.0128416 77 | ANXA1 0.675895 2.93442 0.000565746 0.012996 78 | KMT2D -0.564555 3.94137 0.000573231 0.0131207 79 | SOGA1 -0.615357 3.12904 0.000576677 0.0131494 80 | ZNF575 -0.864483 1.51241 0.000582564 0.0132332 81 | PPTC7 0.645953 2.4964 0.000656008 0.0143002 82 | CLEC1A 0.989242 1.32275 0.000825799 0.016631 83 | CCDC85C -0.507933 3.60721 0.00083568 0.0167469 84 | RNF122 0.958968 1.78139 0.000843049 0.0168415 85 | MYH3 -0.666606 2.40504 0.0010983 0.0202034 86 | MME 0.393481 3.6334 0.00111971 0.020458 87 | PRKRIP1 -0.916141 1.8365 0.00112794 0.0205516 88 | RAPGEFL1 -0.685848 2.41121 0.00115452 0.0208961 89 | NOS1 -0.641423 2.66844 0.00117224 0.0210896 90 | CACNG1 0.718073 2.21513 0.0012604 0.0221194 91 | LY6G5C -0.807244 1.77909 0.00127343 0.0222607 92 | KCP -0.651117 2.4866 0.00143662 0.0240796 93 | MYH9 -0.487599 3.80723 0.00145152 0.0242447 94 | BMP2 -0.807154 1.44178 0.00166143 0.0264739 95 | SF3A3 -0.846606 1.86873 0.00174089 0.0273367 96 | MYH14 -0.493312 3.63447 0.00175142 0.0274304 97 | DGCR8 -0.726926 2.65689 0.00185468 0.0285306 98 | DENND3 -0.391152 4.47084 0.00193254 0.0293357 99 | MRTO4 -0.866949 1.36326 0.0019405 0.0293987 100 | HERC2 -0.394688 3.45397 0.00194824 0.0294622 101 | GSS 0.861336 1.97202 0.00194877 0.0294662 102 | DOHH -0.910137 2.05041 0.00202326 0.0301453 103 | POLR2C -1.21182 0.404937 0.00213287 0.0312438 104 | CTNNA3 0.576991 3.00042 0.00217557 0.0316208 105 | RTN2 1.21984 0.928922 0.00221748 0.0319857 106 | CNN2 -0.75485 2.00128 0.00222993 0.0320872 107 | ATXN7L2 -0.693475 2.12476 0.00243899 0.0339454 108 | ANKZF1 -1.29905 0.511685 0.00246554 0.0341518 109 | ERBB3 -0.474996 3.12732 0.00261445 0.0354954 110 | RP11-1100L3.8 -1.64316 0.362407 0.0028538 0.037664 111 | FOXK2 -0.436445 3.39628 0.00289069 0.0380154 112 | LIX1L 0.954057 1.00962 0.00299402 0.0388726 113 | SARDH -0.539354 3.82463 0.0030039 0.0389681 114 | CEBPD 0.606221 1.9885 0.00353033 0.0432033 115 | RP11-446N19.1 0.882193 1.3868 0.003608 0.0437554 116 | PJA2 0.603331 2.08954 0.00364026 0.043989 117 | ACSL3 0.566484 2.73214 0.00371408 0.0445785 118 | MIR143HG -1.2317 0.578626 0.00372893 0.0446601 119 | TMEM104 -0.857756 1.63959 0.00382541 0.0454468 120 | GPR98 0.288654 4.47893 0.00383141 0.0454814 121 | ARID5B 0.52517 2.42832 0.00395257 0.0463308 122 | AC109333.10 -0.892545 1.26378 0.0040002 0.0467303 123 | PIK3R3 -0.776055 1.31003 0.00408148 0.0473217 124 | SLC45A3 -0.623767 2.17102 0.00412872 0.0476698 125 | KIAA0232 0.499513 2.56479 0.00417742 0.0480709 126 | ADAM33 -0.656455 2.91187 0.00440698 0.0497196 127 | TXNIP 0.951575 1.4986 0.00455165 0.050773 128 | KLF10 0.606437 2.28079 0.00455801 0.0508021 129 | ARHGAP27 -0.483951 3.75076 0.00458527 0.0509907 130 | TNFRSF14 -1.02958 1.04576 0.00475976 0.0522529 131 | KLHL15 0.959718 0.724735 0.00476219 0.0522574 132 | SULT1A1 -0.492785 2.57205 0.00524274 0.0556457 133 | NIFK -1.09223 1.3622 0.0053783 0.0566707 134 | L3MBTL1 -0.389267 3.34274 0.00541369 0.0568993 135 | KDM2A -0.48874 3.78647 0.00547271 0.0572194 136 | LUC7L -0.598498 2.27262 0.00589977 0.0601587 137 | C14orf79 -0.733779 2.32171 0.00592753 0.0603286 138 | NR4A1 0.406761 4.1038 0.00611161 0.0614717 139 | ZBTB38 0.283501 4.06957 0.00667181 0.0651313 140 | AAAS -0.678758 2.54298 0.00674615 0.0656063 141 | GUSB -0.680972 2.78108 0.00689074 0.0664739 142 | ASAP2 -0.39053 3.27286 0.00692593 0.0666823 143 | TAP1 -1.79536 0.266301 0.00693175 0.0667246 144 | ACLY -0.542463 2.63477 0.00715173 0.0680378 145 | FAM193B -0.488033 3.19404 0.00718969 0.0682546 146 | SESN2 -0.97137 1.06655 0.00735395 0.0692421 147 | CACNA2D1 0.348804 3.82442 0.00750436 0.0701812 148 | AMT -0.970707 0.57604 0.00776422 0.0717543 149 | MIR133A1HG 1.17489 -0.00419963 0.00777542 0.0718332 150 | LDLR -0.554287 2.94689 0.00786224 0.072283 151 | RAPGEF2 0.306845 4.03634 0.0079261 0.0726229 152 | ADRA2B -0.943755 1.87356 0.00799249 0.0729959 153 | SORBS1 0.365737 4.10422 0.0083474 0.0749698 154 | SEC31B -0.336759 3.48044 0.00837113 0.0750863 155 | FGD3 -0.382589 3.74417 0.00859613 0.0763782 156 | SLC15A4 0.539407 3.15711 0.00873349 0.0770753 157 | ZBTB16 0.48774 3.9826 0.00944502 0.0809643 158 | RPL32 -2.83573 -1.29953 0.00987004 0.0832955 159 | ZZEF1 -0.396078 3.44371 0.00997301 0.0838001 160 | ZFP36L1 0.440543 2.92782 0.0101882 0.0848244 161 | PLEC -0.525066 4.12949 0.0107783 0.0877561 162 | NAGPA -0.834351 1.54175 0.0111392 0.0895008 163 | TPM4 -0.515287 3.0128 0.0111562 0.0895904 164 | RASD1 -0.740975 1.35946 0.0112142 0.0898362 165 | HMGN3 2.91243 -1.17408 0.0115283 0.0914713 166 | PDE11A 0.294214 3.94055 0.0117521 0.0925323 167 | POLG -0.729574 2.03973 0.0117619 0.0925693 168 | SMO -0.463308 2.5467 0.0121967 0.0947388 169 | ANXA3 0.391869 2.87235 0.0122562 0.0950298 170 | WBP1 -2.26695 -0.773431 0.0125381 0.096457 171 | TUBB6 -0.789388 1.11801 0.0127169 0.0973547 172 | ZNF239 -0.529765 2.15641 0.0127932 0.0976624 173 | TTC31 -0.935168 1.20778 0.012813 0.0977926 174 | UBR5 0.29323 3.93517 0.0131024 0.0993626 175 | FAM136A -3.79125 -1.35372 0.0132146 0.0998276 176 | MEOX1 -0.626306 1.5243 0.0134564 0.100959 177 | GRASP -0.471389 2.70727 0.013714 0.102186 178 | SNRNP70 -0.685988 1.89365 0.0137938 0.102519 179 | EPAS1 -0.308878 3.93883 0.0140981 0.103866 180 | CPSF2 -0.785953 1.18971 0.0142109 0.104348 181 | PLEKHA2 -0.37364 3.05739 0.0150232 0.107886 182 | C1orf132 0.538474 2.77992 0.0150452 0.107972 183 | CSNK1D -0.516764 2.73047 0.0151849 0.108592 184 | ZFHX3 0.523497 2.7122 0.0154484 0.109714 185 | SLC25A25 0.568148 2.73402 0.015754 0.111059 186 | ITIH4 -0.544053 2.56149 0.0162145 0.112986 187 | GIMAP6 0.749334 1.31003 0.0169096 0.115893 188 | FAM160A1 0.425361 2.65204 0.0169128 0.115893 189 | OTUD1 0.592211 1.80347 0.0169484 0.116012 190 | TINAGL1 -0.502436 2.31854 0.0169989 0.116229 191 | LBX1-AS1 -0.659623 1.82154 0.0170801 0.116512 192 | RP11-309L24.4 -1.40589 0.507227 0.0171043 0.116629 193 | OBSCN -0.422348 3.72169 0.0171718 0.116949 194 | MAP9 0.855404 0.953178 0.0172934 0.117451 195 | DIP2C -0.323193 3.21058 0.0173371 0.117637 196 | IRF2BP2 1.04814 0.817853 0.0175408 0.118446 197 | SLC12A2 -0.58619 1.55994 0.0180042 0.120481 198 | C8orf4 0.643176 1.42475 0.018005 0.120481 199 | TPT1P12 0.640748 1.3449 0.0185121 0.122494 200 | SUCO 0.384965 3.0444 0.0186822 0.123242 201 | FOS 0.782199 2.26298 0.018699 0.123289 202 | ENAH 0.294145 3.79732 0.0188207 0.123808 203 | LENG8 -0.700747 1.98721 0.0198916 0.128267 204 | NXPE3 0.400673 2.6756 0.0200928 0.129071 205 | SYNPO2 0.473184 2.71022 0.0201735 0.129397 206 | SLCO4A1 -0.634035 1.77154 0.0203336 0.129985 207 | LASP1 -0.548772 2.9079 0.0208134 0.132052 208 | DDX3X 0.746623 1.16338 0.0209711 0.132647 209 | XIRP2 0.45617 2.27907 0.021033 0.132961 210 | BCL3 0.425245 2.56366 0.0212277 0.13366 211 | PYGO1 0.637651 1.87212 0.0212982 0.133932 212 | BACH1 0.362957 3.27411 0.022018 0.136747 213 | ZC3H12A -1.49609 -0.300885 0.0224067 0.138437 214 | STIM2 0.436762 2.65982 0.0227651 0.139899 215 | MCRS1 -0.605453 1.38198 0.0228142 0.140075 216 | FYCO1 0.497274 2.62462 0.0230374 0.140955 217 | GCH1 -0.67065 1.497 0.0231759 0.141562 218 | WAPAL -0.419347 2.63028 0.0237569 0.143661 219 | ZNF692 -0.817115 0.794879 0.0241587 0.14516 220 | MAMSTR 0.515806 2.16361 0.0250816 0.148509 221 | GLULP4 0.603256 1.23406 0.0254835 0.14996 222 | UBXN11 -0.535138 2.4558 0.0254951 0.149996 223 | KITLG 0.488538 2.02366 0.0255288 0.150112 224 | DLL1 -0.677671 1.21023 0.0256004 0.15035 225 | CCDC117 0.59472 1.2374 0.0262981 0.152917 226 | TNKS1BP1 -0.46208 3.13926 0.0263883 0.153223 227 | GTPBP1 -0.495041 2.25895 0.0275785 0.157634 228 | ITCH -0.407338 2.41943 0.0275859 0.157635 229 | SCN7A 0.360087 2.94293 0.0278511 0.158446 230 | IRS2 -2.47773 -1.16318 0.0279535 0.158821 231 | LBR -0.324768 3.21312 0.0279554 0.158821 232 | MSL2 0.601277 1.92884 0.0284734 0.160818 233 | AQP1 -0.361935 2.84618 0.029207 0.163373 234 | FLNB -0.289658 3.92103 0.0293742 0.16385 235 | PAPD7 -0.340222 2.8817 0.0295504 0.1644 236 | RPL7 -1.06435 0.175882 0.0298243 0.165345 237 | PCBP2 -0.515148 2.06074 0.0298871 0.165531 238 | CSRP3 0.472725 2.27235 0.0299678 0.165816 239 | NDUFB7 -0.663011 1.07416 0.0300134 0.165899 240 | CRISPLD2 -0.33179 3.21444 0.0300934 0.166222 241 | SPON2 -0.386803 3.67031 0.031414 0.170851 242 | FRMD8 -0.547102 2.10333 0.0323021 0.173845 243 | FNDC3B 0.27585 3.81547 0.0323545 0.174005 244 | NDRG1 -0.310284 4.09623 0.0324804 0.174413 245 | MLLT4 0.259807 4.01131 0.0326362 0.174963 246 | GOLGA8B -0.502154 2.29567 0.0328344 0.175659 247 | PPP2R1B 0.312531 3.3369 0.0330505 0.176392 248 | METTL17 -0.597152 1.31159 0.0335771 0.178011 249 | LPCAT4 -0.497671 2.09434 0.0336809 0.178414 250 | FLT1 -0.304174 3.33811 0.034806 0.181884 251 | ASH1L -0.643571 1.20497 0.0349935 0.182501 252 | STOM 0.570005 1.47021 0.035529 0.184068 253 | DNAJA1 -0.795996 0.54956 0.0357473 0.184783 254 | RGS5 0.458976 2.60233 0.0358529 0.18511 255 | SEPT1 -1.08264 0.146002 0.0363439 0.186661 256 | DDX3YP3 0.581474 1.56456 0.0367856 0.18816 257 | CYBRD1 -0.594517 1.28526 0.0370199 0.188822 258 | SLC7A2 0.526559 1.67032 0.0377839 0.191104 259 | VASP -0.434184 2.85446 0.0379414 0.191596 260 | AHNAK -0.399532 2.40713 0.0400917 0.198134 261 | AMZ2P1 0.546072 1.78129 0.0402189 0.198484 262 | ALPK3 -0.359525 2.48451 0.0419933 0.203577 263 | TLN2 -0.230955 4.14948 0.0424102 0.204746 264 | LRRC14 -0.974293 0.379401 0.0428866 0.206151 265 | PICALM 0.274855 3.73019 0.0429132 0.206232 266 | ZYX -1.12374 0.208981 0.0431472 0.206778 267 | FOSL2 0.676257 1.41081 0.0431984 0.206959 268 | AC083843.1 -1.45029 -0.843546 0.0432231 0.207023 269 | PPRC1 -0.445071 2.77493 0.0435381 0.207914 270 | ENC1 -0.487117 1.42533 0.043552 0.207944 271 | ITGA5 -0.336535 2.92353 0.043924 0.208981 272 | FBXL14 -0.483114 1.67782 0.0441526 0.209662 273 | CYB561D2 -1.0046 -0.248888 0.0442415 0.209918 274 | TMEM147-AS1 -0.889648 0.132866 0.044402 0.210403 275 | GIGYF2 -0.249376 3.55284 0.0449641 0.211964 276 | LINC00657 0.711906 1.08131 0.0450042 0.212073 277 | GPR137C -0.755234 1.17536 0.0458594 0.214589 278 | NCOA3 -0.429964 2.19768 0.0461006 0.215221 279 | PDE4B 0.214358 4.44993 0.0474993 0.219357 280 | SUV420H1 0.327575 3.09009 0.0475364 0.219404 281 | UBAP1L -0.589501 1.56797 0.0485515 0.222342 282 | IVNS1ABP -0.436543 2.07593 0.0493328 0.224426 283 | EXOC5 -0.568814 2.06149 0.0495793 0.225 284 | CTC-425O23.5 -1.03132 -0.0208092 0.0504935 0.227332 285 | SLC4A7 0.319053 3.09951 0.0510021 0.228622 286 | PPP1R27 1.47332 -0.61441 0.0514768 0.229867 287 | TBCC -3.85561 -1.91961 0.051924 0.231152 288 | SENP2 -0.449221 2.72928 0.052333 0.232179 289 | CDC42EP1 -0.53437 2.37204 0.0533252 0.234912 290 | BBS9 0.25707 3.52352 0.0540297 0.23677 291 | RP11-373D23.2 -1.88349 -0.929491 0.0543462 0.237589 292 | RP11-421F16.3 0.81915 0.50314 0.0544109 0.237766 293 | APOLD1 -0.400564 1.92019 0.0550587 0.239454 294 | G6PD -0.733891 0.601541 0.0551655 0.239811 295 | IPO7 -0.385535 2.73029 0.0559643 0.241941 296 | HSP90AB1 0.5836 1.90987 0.0562549 0.242653 297 | LINC01091 0.445538 2.02092 0.0579166 0.246786 298 | PLK3 -0.525734 1.50599 0.0586927 0.248837 299 | ATP5O -0.62037 1.32313 0.0588527 0.249274 300 | IQGAP1 -0.269556 3.48732 0.0601347 0.252496 301 | HERC1 -0.249237 3.48347 0.0607084 0.254008 302 | APP 0.238906 3.90615 0.0609067 0.254548 303 | UBL3 0.772411 1.33481 0.0612569 0.255334 304 | USP2 0.330984 2.67453 0.0619997 0.257067 305 | IER5 0.472617 1.67974 0.0624554 0.258312 306 | CES3 -0.443931 2.21477 0.0628796 0.259342 307 | PITPNB 0.425517 1.90081 0.0640933 0.262474 308 | HSPH1 0.378316 2.00178 0.0644781 0.263427 309 | DNAJA4 -0.389188 2.19306 0.0651825 0.265075 310 | C1orf127 -0.371825 3.49747 0.0655389 0.265828 311 | BMI1 0.590091 1.51479 0.066084 0.267104 312 | CA8 0.4031 2.14966 0.0660893 0.267104 313 | CLDN5 -0.466388 2.25959 0.0662663 0.267509 314 | NIT1 -0.803583 0.554594 0.0664117 0.267826 315 | ETS2 0.339615 2.45991 0.0671738 0.269611 316 | TMEM132A -0.397468 1.80158 0.0673657 0.27003 317 | SRSF3 -0.399637 1.93421 0.0675546 0.270366 318 | HSPD1 -0.538077 1.29391 0.0675809 0.270431 319 | BOLL 0.494263 2.28883 0.0679753 0.271319 320 | RBM28 -0.351475 2.45723 0.068663 0.272781 321 | PPP1R15A 0.46632 1.63854 0.0690678 0.273692 322 | DNAJC7 -0.48543 2.16347 0.0711253 0.278346 323 | GABARAPL1 0.897451 0.329211 0.0712932 0.27872 324 | RP11-255P5.3 0.478025 1.36464 0.0716375 0.279503 325 | DDX11 -0.302195 2.77275 0.071641 0.279503 326 | IK -0.630863 0.849298 0.0717059 0.279686 327 | GGT5 -0.436469 1.9571 0.0717314 0.279718 328 | RICTOR 0.293805 3.09132 0.072374 0.281084 329 | SEC24A -0.648833 0.662597 0.0733526 0.283357 330 | ZSWIM1 -0.543009 1.23095 0.0747319 0.286442 331 | EML4 0.324554 2.5733 0.0748713 0.286775 332 | POLR2A -0.400646 2.8234 0.075091 0.287161 333 | MKL1 -0.267489 3.32528 0.0756369 0.288394 334 | NRIP1 0.599665 1.08371 0.0761858 0.28967 335 | PSMA4 -0.609356 0.953812 0.0762051 0.289684 336 | KIAA0226 -0.348437 2.7826 0.0763056 0.289892 337 | TNK2 -0.36002 3.75812 0.0764652 0.290193 338 | NRARP -0.706672 1.41687 0.0778596 0.293289 339 | SERPINH1 -0.432517 2.96706 0.0780255 0.293598 340 | GJA4 -0.464083 1.39891 0.07855 0.294791 341 | C1QC -0.508203 1.73477 0.0788839 0.295388 342 | MYO1E -0.291457 2.74333 0.0798212 0.297236 343 | COX7C 0.611042 1.12522 0.0824573 0.302951 344 | SEMA4C -0.373236 2.92425 0.0828156 0.303669 345 | PLEKHG1 0.363525 2.48461 0.0834797 0.305203 346 | KDM6B 0.379012 2.61374 0.0841174 0.306403 347 | TOB1 -1.66883 -1.23246 0.0848699 0.307981 348 | NHSL1 0.281974 3.20879 0.084969 0.308146 349 | HNRNPA0 0.436469 1.57462 0.0854398 0.309182 350 | DUSP6 -0.911742 0.156572 0.0890172 0.316161 351 | ACYP2 0.374756 2.35631 0.0937992 0.325881 352 | RPL27A -0.852607 -0.301664 0.0962418 0.330445 353 | COX7A1 0.508445 1.08223 0.096694 0.331393 354 | GPR116 0.251029 3.2004 0.0970089 0.331933 355 | USHBP1 -0.945735 0.467493 0.0981367 0.334211 356 | OBSL1 -0.522033 1.71602 0.0993738 0.336466 357 | ANTXR2 0.26991 2.82423 0.0997454 0.337149 358 | MRPL27 -1.86708 -0.9733 0.100664 0.339013 359 | CCDC150P1 -0.538636 0.844306 0.100939 0.339421 360 | EPHA2 -0.369556 2.50198 0.101085 0.33967 361 | PIK3CA -0.566315 0.744909 0.101614 0.340726 362 | SOCS6 0.307668 2.59552 0.104542 0.345905 363 | DBP -0.50816 0.706663 0.104946 0.3466 364 | CHORDC1 -0.549501 1.07236 0.105207 0.347087 365 | RP11-463O12.5 -1.43345 -0.587983 0.106103 0.348558 366 | TAL1 -0.396324 2.21201 0.10652 0.349301 367 | NES -0.418295 1.60247 0.108027 0.35216 368 | GEM 0.418138 1.88691 0.108488 0.352996 369 | EFR3B -0.282876 2.83958 0.110166 0.356041 370 | SVIL 0.176263 4.32863 0.110241 0.356119 371 | BPHL -0.261422 2.84404 0.110399 0.356378 372 | C14orf166 -0.324759 1.9447 0.110517 0.356527 373 | HM13 -0.303065 3.19479 0.110925 0.357171 374 | ALG1 -0.531055 1.76778 0.112821 0.360791 375 | EXOC3L1 -1.2013 -0.00380225 0.113384 0.361694 376 | SKIL 0.294625 2.55313 0.114024 0.362819 377 | IER5L -1.21588 -0.792157 0.1156 0.365523 378 | GOLGA8A -0.343626 1.7273 0.115739 0.365744 379 | PHPT1 -1.63466 -1.68263 0.115766 0.36579 380 | AHCYL2 0.209629 4.0019 0.115983 0.36621 381 | TXLNG 0.478609 1.0757 0.116043 0.366335 382 | ITPRIP 0.274862 2.84452 0.117319 0.368679 383 | MTMR9LP -0.412507 1.50756 0.118444 0.370564 384 | TNFRSF10B -0.446203 2.29207 0.118907 0.371256 385 | AC004453.8 -0.729119 0.462296 0.118964 0.371334 386 | ARPC2 -0.275184 3.42396 0.119007 0.371394 387 | LPL 0.320567 2.52197 0.119578 0.372288 388 | PELI2 2.98629 -1.79055 0.119801 0.37273 389 | TSC22D2 0.244817 2.90753 0.120436 0.373887 390 | PZP 0.278294 2.40588 0.12059 0.374111 391 | JUND -1.7489 -1.2039 0.121906 0.376686 392 | GSTP1 -0.384538 1.79658 0.122165 0.377084 393 | PVRL2 -0.284827 3.05468 0.122926 0.37843 394 | TXNDC16 0.397683 1.38199 0.12323 0.378945 395 | ETS1 0.244762 2.77335 0.123287 0.379035 396 | ATF3 0.302174 3.06272 0.124998 0.382134 397 | NR4A3 -0.390155 1.60829 0.125226 0.382448 398 | KIAA1107 0.437083 1.16153 0.1263 0.384221 399 | GNE -0.331665 2.40461 0.127872 0.386974 400 | APOE -0.812018 0.535956 0.129197 0.389029 401 | XIRP1 0.438177 1.58965 0.129671 0.389951 402 | YES1 -0.557378 0.742287 0.129897 0.390317 403 | RC3H2 -0.263089 2.36978 0.130633 0.391481 404 | KAT2A -0.813763 -0.132464 0.13115 0.392139 405 | ATOH8 0.281046 3.37258 0.133247 0.395367 406 | C11orf84 -0.463279 1.93031 0.133462 0.395723 407 | PTMS 1.03112 -0.153556 0.134813 0.39792 408 | TIPARP 0.614826 0.973204 0.136029 0.399899 409 | THG1L -0.349718 2.09918 0.136272 0.400381 410 | TEK 0.411654 1.16554 0.136785 0.401334 411 | PIM1 -0.34664 1.85118 0.138672 0.404383 412 | ACTR3 -0.3345 2.07541 0.139097 0.405068 413 | IL15 0.335627 2.6744 0.139485 0.405546 414 | SRSF11 -0.281777 2.54029 0.141485 0.408685 415 | SIVA1 -0.440276 1.59484 0.143101 0.411233 416 | RP11-864N7.2 -1.22435 -0.621804 0.144647 0.413839 417 | CCNG2 -0.277804 2.46858 0.144751 0.414035 418 | AKAP12 -0.241807 2.90587 0.144834 0.414117 419 | CDKN1A 0.518252 1.6232 0.145264 0.41468 420 | SDE2 -0.564011 0.955784 0.146576 0.416636 421 | HSPA1B 0.509879 0.691614 0.14665 0.416734 422 | CPD -0.256817 3.00099 0.147065 0.417396 423 | HECA 0.366469 1.67304 0.147611 0.41811 424 | NUDT3 0.419523 1.42155 0.148241 0.41905 425 | MSANTD4 -0.344085 1.91704 0.148356 0.419276 426 | TBL2 -0.372074 2.12833 0.148537 0.41947 427 | BLVRB -0.632623 0.794188 0.148745 0.419759 428 | ADAMTS9 0.191261 3.47028 0.148839 0.419863 429 | PTPN11 -0.506408 0.685061 0.150496 0.422341 430 | AZIN1 -0.252631 2.82805 0.153484 0.426819 431 | MTMR12 -0.311208 1.71246 0.153679 0.42707 432 | RSRP1 -0.590939 0.271381 0.154372 0.428288 433 | NAB2 -0.686654 0.562703 0.155944 0.430576 434 | CHRND -0.4277 1.28311 0.15703 0.432165 435 | PIK3IP1 -1.23057 -0.758576 0.157098 0.432244 436 | HMOX1 -0.323038 2.25036 0.159146 0.435255 437 | RP11-1334A24.5 -0.487978 0.744373 0.159891 0.436252 438 | DHRS3 0.418505 2.3609 0.159936 0.436328 439 | GIMAP8 -0.335062 1.76424 0.160375 0.436987 440 | ADAMTS13 -0.279574 2.28512 0.160709 0.437611 441 | MCFD2 -0.404706 0.998381 0.161707 0.438967 442 | FOXN3 0.193099 3.60292 0.162471 0.439946 443 | AGBL1 -0.334693 1.71356 0.162599 0.44014 444 | ECHDC2 0.222707 2.78074 0.162853 0.440408 445 | FERMT2 -0.25433 3.37115 0.164575 0.442658 446 | AGL 0.240455 2.52426 0.164612 0.442692 447 | RNF152 -0.258272 2.46589 0.164824 0.443031 448 | UGCG -0.518222 1.52586 0.166203 0.444934 449 | SFXN5 -0.177633 4.25651 0.167012 0.446026 450 | ALS2CL -0.198866 3.49001 0.167286 0.446419 451 | CX3CL1 -0.306036 2.26867 0.16921 0.449519 452 | VWF -0.18963 3.31236 0.169426 0.449827 453 | FOSL1 -0.390201 1.54113 0.170667 0.451327 454 | TMED4 0.437863 1.28278 0.170842 0.451573 455 | LRRC32 -0.300965 2.31397 0.171729 0.452966 456 | DIO3OS -0.493416 1.16079 0.173944 0.455785 457 | MOB3C 0.730626 -0.0893616 0.175436 0.457835 458 | IBA57 -0.362474 1.98812 0.176432 0.459039 459 | FAM46A 0.399312 1.93213 0.17772 0.460885 460 | TRA2B -0.26936 2.79828 0.179017 0.46276 461 | RP11-87E22.2 -0.314182 1.62275 0.179042 0.462801 462 | SLFN11 0.239297 2.91946 0.180407 0.464445 463 | TBC1D9 0.321662 2.45427 0.1807 0.464809 464 | SRGN 0.443666 1.04377 0.180708 0.464817 465 | LTN1 -0.29906 2.15598 0.181925 0.466377 466 | ULK3 -0.565029 1.18108 0.181994 0.466453 467 | FLNC 0.24682 2.8271 0.182079 0.466599 468 | CIR1 -0.399407 1.34844 0.182456 0.467024 469 | CIRBP -0.25328 2.59525 0.182727 0.467442 470 | PTPRB 0.21013 3.03157 0.183352 0.468194 471 | ZNF106 0.264131 2.59538 0.183618 0.468572 472 | ZBTB10 2.03206 -1.60049 0.187137 0.473594 473 | RGS16 -0.424386 1.21607 0.1886 0.475316 474 | KIF1B 0.226975 3.41045 0.189599 0.476747 475 | WARS -0.227612 2.66567 0.190242 0.477374 476 | DNAJB4 1.75033 -1.77146 0.191183 0.478591 477 | MAFF 0.226934 2.50978 0.191284 0.478721 478 | UBE2E3 0.263165 2.52177 0.191395 0.478836 479 | LINC00707 -0.441491 1.3191 0.191472 0.478923 480 | NR6A1 0.5459 0.580465 0.192616 0.480429 481 | TSC22D1 0.305825 1.99174 0.194248 0.482606 482 | RP11-426D19.1 -0.363341 1.38704 0.195468 0.484222 483 | TMEM176B -0.413274 0.928953 0.19581 0.484691 484 | PIAS1 -0.179968 3.16034 0.197932 0.48734 485 | RMND5A -0.248235 2.55857 0.200515 0.490613 486 | RPS24 -0.356639 1.85504 0.20084 0.491087 487 | XRN1 -0.204974 3.25509 0.201264 0.491591 488 | BTN3A1 0.271119 2.12348 0.201619 0.491957 489 | MAD2L2 -0.392153 1.35224 0.201901 0.492315 490 | ARID5A -0.25142 2.50192 0.204177 0.495602 491 | ICAM1 -0.318767 2.02864 0.205427 0.497295 492 | CXCR4 -0.284574 1.74336 0.205794 0.497756 493 | TPCN1 -0.257422 3.23925 0.205882 0.497833 494 | AMD1 -0.239314 2.34477 0.206141 0.497979 495 | NEDD9 -0.214168 2.74957 0.207672 0.499775 496 | TTN -0.204865 3.2818 0.207805 0.499926 497 | ARRDC4 0.424912 1.33205 0.208666 0.500953 498 | SERF2 1 -0.817463 0.209654 0.502072 499 | ATP5G2 0.310346 2.17096 0.211023 0.503633 500 | AEBP2 -0.208661 2.72503 0.212141 0.505125 501 | COL4A1 0.273925 2.49011 0.212579 0.505602 502 | OSGIN1 -0.252169 2.89561 0.213547 0.506819 503 | SLC22A5 -0.198025 2.79418 0.214828 0.508473 504 | ATG2B -0.199436 2.86228 0.218453 0.51326 505 | RPL37A -0.305473 1.4614 0.219111 0.514175 506 | NEDD4 0.197565 2.98791 0.221748 0.51751 507 | RP11-474J18.1 1.09094 -0.180136 0.223889 0.520293 508 | NF1 -0.14045 4.05616 0.224701 0.521216 509 | PLCL1 0.186933 3.38141 0.225252 0.521932 510 | CHUK -0.442686 0.716603 0.226396 0.523274 511 | SLC2A3 -0.225377 2.7687 0.226499 0.523407 512 | KLF13 -0.163616 3.95894 0.226734 0.523738 513 | ITGA1 0.285237 2.34563 0.226929 0.523935 514 | SOCS5 0.330894 1.73575 0.227338 0.524379 515 | IFRD1 0.176359 3.36676 0.228982 0.526397 516 | UQCRHL -0.372702 1.1442 0.229921 0.527379 517 | FOXJ2 -0.291333 2.32463 0.231699 0.529624 518 | ADAMTS4 -0.85285 -0.958828 0.233276 0.531317 519 | SNRNP35 0.237485 2.2828 0.235442 0.534045 520 | ZBTB11 -0.719009 -0.032115 0.235665 0.534281 521 | KIAA2018 0.196199 2.68151 0.235784 0.534426 522 | TIMM9 -0.46446 0.41485 0.235795 0.53444 523 | CXXC1 -0.300438 1.47818 0.236453 0.535326 524 | FANCM 0.218536 2.94989 0.236702 0.535676 525 | ZBTB47 -0.320697 1.42712 0.238461 0.53764 526 | LRRK2 0.216061 2.82722 0.243489 0.543711 527 | EGR3 0.420017 0.880328 0.243701 0.543978 528 | USP37 -0.242885 2.86684 0.244117 0.544553 529 | RASIP1 0.337657 1.55039 0.247789 0.548551 530 | ACOT13 -0.428735 0.632195 0.248824 0.549581 531 | ZC3H15 0.3959 1.56001 0.249771 0.550477 532 | MEDAG -0.381398 1.7493 0.253069 0.55396 533 | PNP 0.307063 1.69137 0.253503 0.554401 534 | HIST1H2BD 1.04546 -1.41684 0.254588 0.5556 535 | UAP1 0.257563 2.09925 0.256237 0.557285 536 | ATP1A1 -0.145805 3.62 0.256284 0.55731 537 | TMUB2 -0.696784 0.023902 0.257047 0.558091 538 | SLC29A2 -0.4134 0.786848 0.25757 0.558621 539 | EIF4BP3 0.354589 1.14365 0.258613 0.559638 540 | NENF -0.416977 1.44181 0.259507 0.560539 541 | KCNJ8 0.351963 1.48974 0.260618 0.561757 542 | FEN1 -0.635668 -0.147915 0.260922 0.562077 543 | PRMT9 0.213051 2.55295 0.26154 0.562712 544 | CHD1 -0.19271 2.66117 0.263924 0.565155 545 | TACC1 -0.139607 4.10337 0.264465 0.565817 546 | WSB1 0.336015 1.17884 0.265018 0.56634 547 | KIAA0355 -0.268613 2.67311 0.266219 0.567511 548 | BCAS2 0.441354 1.19455 0.267583 0.569035 549 | FAM126A -0.200526 2.45553 0.268132 0.569546 550 | VSIG2 -0.284427 1.70755 0.269783 0.571456 551 | MRPS11 -0.395271 0.83653 0.269938 0.571681 552 | SRSF5 -0.231083 2.45178 0.271448 0.5735 553 | SOX17 -0.343457 1.5599 0.271456 0.573505 554 | SERPINE1 -0.329148 1.2246 0.271526 0.573587 555 | LAMC1 0.170394 3.64806 0.27379 0.576159 556 | ERF -0.381049 1.4011 0.275018 0.577406 557 | ATP1B4 0.401605 0.566393 0.276077 0.578551 558 | CNIH4 0.234803 2.48441 0.277379 0.579735 559 | MAFB -0.253221 1.91407 0.277857 0.580185 560 | SPOPL 0.279391 1.36438 0.278699 0.581068 561 | EFR3A -0.16877 3.23466 0.279371 0.581867 562 | STK38L -0.188714 3.11336 0.281059 0.583724 563 | TRIP12 -0.180721 3.2339 0.284689 0.58729 564 | PAN2 -0.303147 1.72775 0.285422 0.587972 565 | RAPGEF5 0.129098 3.89426 0.287613 0.590296 566 | CAPN7 -0.241674 2.13943 0.288484 0.591143 567 | VPS11 -0.303826 1.58158 0.289034 0.591629 568 | LBP 0.280342 1.48567 0.289752 0.59235 569 | BHLHE40 1.66664 -1.35639 0.291742 0.594388 570 | MED13 0.240424 2.54488 0.293962 0.596598 571 | RP11-159D12.2 -0.34819 1.12619 0.294866 0.597457 572 | SRF 0.277216 1.70383 0.295169 0.59774 573 | MCAM -0.287357 1.70194 0.295172 0.59774 574 | TET3 -0.255679 2.22113 0.2962 0.598661 575 | CRYBG3 0.304452 2.1129 0.297229 0.599537 576 | MTCH1 -0.206802 3.04844 0.299555 0.601718 577 | TAGLN2 -0.22005 2.12961 0.300654 0.602719 578 | AC013437.7 0.791853 -0.595068 0.304422 0.606402 579 | HSPA4L 0.240141 2.01818 0.305281 0.607194 580 | DUSP8 -0.281197 2.59426 0.307509 0.609308 581 | CEP350 -0.170734 3.2004 0.307641 0.609454 582 | PRSS45 -0.328619 1.47912 0.30883 0.610597 583 | PPP1R14A -0.228105 2.0502 0.3102 0.611776 584 | SGSM1 -0.182761 2.45683 0.310334 0.611901 585 | MARCKSL1 -0.372694 1.50016 0.312259 0.613701 586 | NSUN5P1 -0.758663 -0.659645 0.312773 0.614141 587 | TBC1D10B -0.379855 0.834291 0.316741 0.618039 588 | ARHGAP1 -0.227034 2.25783 0.317617 0.618934 589 | EPB41L2 0.102275 4.71985 0.320637 0.621653 590 | GPR4 -0.45899 0.421048 0.321137 0.622114 591 | SDC4 -0.469846 -0.0689152 0.321691 0.622585 592 | RNF145 0.222217 2.13608 0.321735 0.622659 593 | ZBED5-AS1 0.424385 0.882361 0.322379 0.62338 594 | AKR1C1 -0.763196 -0.897607 0.323819 0.624637 595 | MTCO1P5 0.348922 0.716047 0.325077 0.625756 596 | COA5 -0.410739 0.383825 0.325449 0.625951 597 | SAMD14 0.197849 3.28622 0.326015 0.626352 598 | HSP90AA1 0.239998 1.96358 0.329133 0.629303 599 | PCOLCE -0.418316 0.508103 0.333163 0.633026 600 | RPS28P7 0.259399 1.40358 0.334895 0.634523 601 | ZNF644 -0.190907 2.65306 0.335227 0.634828 602 | HIVEP1 0.200628 2.48622 0.33671 0.63599 603 | GLUL 0.183649 2.68722 0.337176 0.636353 604 | SLK -0.26466 2.1249 0.337432 0.636651 605 | ZBTB44 -0.23392 1.8796 0.338839 0.637761 606 | SDCCAG3 -0.346656 0.970645 0.34038 0.638929 607 | POGLUT1 0.22867 1.91529 0.341092 0.639488 608 | ITGB1BP2 -0.63333 -0.0544529 0.341624 0.640153 609 | G3BP2 0.189824 2.4488 0.342928 0.641343 610 | MYH2 0.239936 1.53343 0.34757 0.645561 611 | C11orf96 -0.238805 1.98907 0.348737 0.646712 612 | PPP4R1 -0.140064 3.49708 0.349199 0.64714 613 | DPYD 0.202906 2.07138 0.349745 0.647564 614 | CD83 -0.328649 1.11742 0.350278 0.648152 615 | SEMA6D 0.106198 3.95719 0.351586 0.649375 616 | SUGT1 0.419565 0.880652 0.352443 0.650101 617 | RHOC -0.324011 1.38399 0.35268 0.650274 618 | STX11 -0.235713 1.34395 0.358876 0.655266 619 | OTUD4 0.18354 2.45443 0.359587 0.655984 620 | NIN 0.144826 3.6678 0.359843 0.656239 621 | RGS2 0.296618 1.13678 0.363007 0.659184 622 | ABRA 0.278739 1.65191 0.363372 0.659451 623 | ID3 0.301837 1.87435 0.365131 0.660907 624 | RRM2B -0.236259 1.41638 0.365232 0.660958 625 | ARG2 0.211745 1.61848 0.365929 0.661531 626 | ERAP1 0.178041 2.20256 0.369048 0.664167 627 | YWHAQ -0.205723 2.13622 0.369554 0.664558 628 | ZBTB21 0.614273 0.163095 0.370778 0.665619 629 | RAB21 -0.162566 2.41914 0.370995 0.665828 630 | ZC3HAV1 -0.218847 2.32444 0.371376 0.666163 631 | CTD-3035D6.1 0.582568 0.487676 0.3719 0.666642 632 | DDX52 -0.267343 1.64343 0.373653 0.667653 633 | CLIC4 -0.278432 0.964892 0.373709 0.667664 634 | SMIM19 0.351952 0.566229 0.373884 0.667834 635 | SMG1 -0.156971 2.97802 0.378434 0.671353 636 | ANAPC7 0.158495 2.86281 0.379008 0.671691 637 | RPS6 0.431331 -0.0729416 0.382156 0.674421 638 | OSBPL11 0.299367 0.950837 0.38246 0.674739 639 | SCAI -0.158805 2.65706 0.383193 0.675403 640 | KIF13A 0.189387 2.59578 0.383256 0.675427 641 | MAP4 0.108884 3.91577 0.386048 0.677601 642 | HSP90AA2P -0.316684 1.2506 0.388233 0.67952 643 | PLCD4 0.15832 2.9308 0.388696 0.679772 644 | ZNF596 0.581686 -0.475964 0.389777 0.680536 645 | MTR -0.156288 2.56813 0.392711 0.682801 646 | GSTM2 -0.190965 2.40501 0.39283 0.682897 647 | CNTROB -0.180658 2.47215 0.394597 0.684353 648 | HDDC2 0.133839 2.93269 0.395213 0.68476 649 | HSP90AB3P 0.278339 1.0352 0.395386 0.684857 650 | USP6 -0.315679 0.946542 0.396411 0.685673 651 | RPS4XP3 0.250665 1.06889 0.397583 0.686599 652 | EDEM3 0.15912 2.37078 0.397909 0.686886 653 | GOLGB1 -0.160802 2.39269 0.398672 0.687512 654 | RABEP2 -0.223618 1.62872 0.399216 0.687902 655 | SERP1 0.324588 0.71722 0.400334 0.688914 656 | AMMECR1L -0.684227 -0.611823 0.404472 0.692122 657 | ACOX2 -0.149748 3.40791 0.406344 0.693808 658 | SLC4A5 -0.0912914 4.08998 0.407185 0.694445 659 | TNFRSF10D -0.271036 1.00027 0.408104 0.695268 660 | NFKBIA 0.2323 1.97711 0.40819 0.695269 661 | KRAS -0.267688 1.48505 0.409153 0.696032 662 | FAM53C -0.502107 -0.450466 0.409192 0.696058 663 | HSPB1 0.263433 1.91427 0.409291 0.696157 664 | THBD 0.173182 2.22938 0.412023 0.698429 665 | WEE1 0.187033 2.15207 0.412089 0.698439 666 | RND3 0.130263 3.33916 0.413094 0.699336 667 | ASB7 -0.326057 0.723292 0.418176 0.703175 668 | EGR1 -0.263551 1.70944 0.418684 0.703636 669 | RP4-635E18.8 -0.344331 0.446715 0.421739 0.705887 670 | RELT -0.211561 2.63993 0.4243 0.707789 671 | SNRK 0.141561 2.80817 0.424566 0.708059 672 | NMD3P1 0.223667 1.38729 0.42771 0.710325 673 | NFIL3 0.311508 0.530058 0.432716 0.713859 674 | ORMDL1 -0.340898 0.874042 0.434713 0.715263 675 | ADAMTSL3 -0.110157 3.46718 0.435651 0.716083 676 | UPK3A 0.232027 1.632 0.435829 0.71622 677 | RPL34 1.02722 -1.45844 0.435942 0.716289 678 | SLC20A1 -0.172749 1.76766 0.438506 0.718217 679 | GATSL2 -0.380301 0.770254 0.439226 0.718781 680 | CTB-89H12.4 -0.412242 -0.326135 0.440842 0.719933 681 | SERPINE2 -0.11266 3.45647 0.442577 0.721087 682 | ZFAND2A -0.563978 0.798086 0.444927 0.722921 683 | EIF4A3 -0.242033 1.6114 0.445291 0.723108 684 | NEB -0.128961 4.01773 0.445754 0.723382 685 | HEG1 0.121367 3.32988 0.447175 0.724391 686 | MUC3A -0.114474 3.35619 0.447548 0.724803 687 | MARCH6 0.108471 3.06937 0.44945 0.726246 688 | P2RX4 -0.142008 2.34977 0.449596 0.726366 689 | HUWE1 -0.138174 3.0879 0.45009 0.72667 690 | TMEM81 0.294533 0.766287 0.451677 0.727547 691 | LONRF2 -0.18506 1.9586 0.453242 0.728737 692 | NEK4 -0.1705 2.02772 0.453595 0.728978 693 | NUSAP1 0.157386 2.19062 0.45373 0.729076 694 | MED13L -0.148616 3.25026 0.4542 0.729417 695 | SPRY4 -0.384839 -0.135763 0.455854 0.730619 696 | ANKRD26 -0.118136 3.04244 0.456071 0.730647 697 | ARHGAP5 -0.161331 1.87448 0.4582 0.732164 698 | BCL6B -0.219026 1.69552 0.45841 0.732261 699 | AMY2B -0.151319 2.25867 0.460917 0.73416 700 | WISP2 0.118692 2.89083 0.469421 0.740532 701 | HNRNPH3 -0.260645 1.12423 0.471172 0.741638 702 | JOSD1 -0.161414 2.60316 0.47266 0.742638 703 | CYYR1 -0.203711 1.39536 0.473001 0.74293 704 | UBE2C -0.236598 1.42496 0.473035 0.74293 705 | RP11-326I11.5 -0.668253 -0.568534 0.474091 0.743546 706 | IL6 -0.540802 -0.287447 0.474109 0.743546 707 | FOSB 0.175192 2.15917 0.476568 0.745407 708 | PPP3CB-AS1 0.460957 -0.350617 0.479597 0.747526 709 | PDK4 0.255799 1.41013 0.480063 0.74781 710 | RP11-96A15.1 0.164746 1.91796 0.480769 0.748295 711 | DUSP5 -0.221616 1.27088 0.481072 0.74841 712 | TAOK1 -0.283115 0.996124 0.481244 0.748486 713 | TARBP2 -0.586874 -1.30662 0.483839 0.750176 714 | RP1-309I22.2 -0.239598 1.28846 0.485483 0.751309 715 | PPP4R2 0.19335 1.58243 0.485919 0.751638 716 | ZYG11B -0.250775 0.853478 0.486195 0.751844 717 | PHC3 0.135695 3.02256 0.487477 0.752739 718 | NAF1 0.119066 2.48456 0.488335 0.753449 719 | HSBP1 -0.878875 -1.7882 0.489269 0.754062 720 | OSBPL7 0.127165 3.14379 0.492054 0.755867 721 | ATP8A1 -0.0877786 3.66825 0.492077 0.755867 722 | SDHCP4 0.187213 1.32649 0.492289 0.755948 723 | IL6ST -0.114688 2.77717 0.494127 0.757098 724 | MB21D1 -0.146224 2.07337 0.498172 0.760003 725 | MAGED2 0.292416 0.673168 0.501146 0.761948 726 | PTPN14 0.135846 2.74591 0.501168 0.761948 727 | BTN3A2 0.198728 1.47286 0.502732 0.762872 728 | FEM1C -0.22069 1.11797 0.503351 0.763291 729 | TEX101 -0.12896 2.4168 0.504486 0.764149 730 | JMY 0.238631 0.62389 0.507346 0.765878 731 | HNRNPH1 -0.170069 2.06621 0.508859 0.766785 732 | GTF3A -0.151776 2.58303 0.511793 0.768654 733 | ARHGEF12 0.0762817 3.89462 0.514404 0.770433 734 | HELZ -0.149593 1.8921 0.515069 0.770856 735 | SDHC 0.173374 1.63729 0.517344 0.772437 736 | RP11-16F15.1 0.178594 1.12524 0.51764 0.772587 737 | RPL4P4 -0.245248 0.761301 0.518598 0.773262 738 | ANKRD33B 0.160316 1.97695 0.519862 0.773983 739 | BTG3 -0.107977 2.88017 0.519959 0.774048 740 | MIDN 0.18455 2.66203 0.520103 0.774138 741 | STXBP5 -0.227548 0.565447 0.522244 0.775504 742 | EIF3A -0.197063 1.1844 0.522492 0.775664 743 | ADCY9 -0.108776 3.58301 0.522823 0.775921 744 | TCEAL7 0.37799 0.63685 0.524807 0.777438 745 | RP11-441O15.3 0.907275 -1.76738 0.526619 0.77874 746 | KRTAP21-4P 0.201471 0.830508 0.528732 0.780154 747 | GIMAP5 -0.124691 2.88045 0.532814 0.782844 748 | BOLA3 0.447211 -0.273785 0.53332 0.783141 749 | RP11-102K13.5 -0.151194 1.97394 0.533596 0.783365 750 | RP11-135F9.3 0.253568 0.991641 0.533786 0.783505 751 | PLAUR -0.134862 2.28489 0.534051 0.783589 752 | CDK6 0.148307 2.50695 0.534228 0.78364 753 | VPS13B 0.0939519 3.64099 0.536913 0.785417 754 | PRDM2 0.086552 3.46521 0.538813 0.786547 755 | PAIP2B 0.194327 1.03036 0.541375 0.788321 756 | RP11-64B16.4 -0.999858 -1.37632 0.543418 0.789718 757 | SLC25A45 0.137049 2.45271 0.54351 0.789753 758 | USP53 -0.126113 2.35607 0.544574 0.790523 759 | TNFSF10 0.965134 -1.69321 0.544797 0.790676 760 | RPL35A 0.433043 0.245332 0.54959 0.793922 761 | STK32B -0.0765513 4.14021 0.549914 0.794049 762 | KLF4 0.144678 2.29505 0.552297 0.795629 763 | USP9X 0.130423 2.47276 0.553121 0.796232 764 | LAS1L 0.176389 1.19736 0.558279 0.799078 765 | TNPO1 0.11063 2.64153 0.558344 0.799103 766 | DUSP16 0.125472 2.27348 0.559463 0.799873 767 | NEK10 0.0941767 3.46446 0.559744 0.800063 768 | PSMB5 -0.750587 -1.62423 0.559908 0.800171 769 | NT5C1A -0.154976 1.79422 0.560665 0.800643 770 | PIK3C2A 0.195252 0.723114 0.560979 0.800877 771 | DCP1A -0.168788 1.57907 0.561132 0.801018 772 | PLS3 0.148933 1.59945 0.561216 0.801065 773 | CBL -0.227877 0.975858 0.562348 0.801632 774 | C16orf72 -0.141651 1.78088 0.562604 0.801807 775 | PHF3 -0.0784941 3.45956 0.563385 0.802229 776 | ATP5HP4 0.203661 0.658586 0.564516 0.802835 777 | CACNB1 -0.1195 2.72599 0.564613 0.802896 778 | ARFGEF2 -0.115588 2.20523 0.565158 0.803275 779 | RP11-164J13.1 -0.274784 -0.213999 0.566689 0.804208 780 | RPS13 0.403911 -0.407971 0.571868 0.806866 781 | PER2 0.0995642 3.3175 0.573219 0.807581 782 | FAM189A2 0.082608 3.34613 0.576567 0.809438 783 | RBFOX2 0.0612027 3.90307 0.578255 0.810316 784 | RIPK2 0.175366 0.823662 0.579186 0.810812 785 | RP11-631N16.2 -1.20952 -1.46703 0.58049 0.811686 786 | DMD 0.066169 3.81536 0.580928 0.811869 787 | ZNF460 0.513996 -0.733377 0.583774 0.813586 788 | RBPJ -0.0725519 4.04405 0.584465 0.814019 789 | SOCS7 -0.147105 1.51419 0.584795 0.814262 790 | FKBP4 -0.252427 -0.00118524 0.586359 0.815113 791 | TMEM120B -0.0881366 3.27709 0.587557 0.815965 792 | SMAD5 0.129552 2.28191 0.592107 0.818826 793 | PGPEP1L 0.146293 1.64682 0.593046 0.819442 794 | RSL24D1 -0.183484 1.096 0.596321 0.821034 795 | KB-431C1.5 -0.245904 0.539048 0.596391 0.821053 796 | RPS12 1.12042 -1.50317 0.600206 0.823072 797 | UNQ6494 -0.103193 2.65948 0.601257 0.823552 798 | SNAI1 -0.13659 1.61149 0.603826 0.825073 799 | ANP32E 0.126348 1.3899 0.604821 0.825608 800 | CLK2 -0.146463 1.55118 0.610138 0.82875 801 | LCMT1 -0.100083 2.81094 0.610852 0.829092 802 | DUSP2 -0.212458 0.359431 0.613328 0.830584 803 | FOXO1 0.113848 2.69996 0.616179 0.832146 804 | COX7B 0.305765 -0.64321 0.619931 0.834113 805 | PIK3CB 0.0658178 3.76561 0.620066 0.834176 806 | SLC38A2 -0.148397 1.1736 0.620604 0.834561 807 | RYBP -0.15873 1.14129 0.624964 0.836784 808 | CLDN12 0.0990527 2.70621 0.625339 0.836995 809 | NFE2L2 -0.122563 2.01443 0.628899 0.839096 810 | REEP1 0.128859 3.02986 0.629947 0.839499 811 | RP11-54O7.17 -0.258142 -0.25177 0.630409 0.839771 812 | PHF14 0.0721473 3.50612 0.630644 0.839771 813 | MXD1 0.124214 2.15635 0.63073 0.839785 814 | RFX3 0.0750657 3.09018 0.63357 0.841284 815 | SV2B 0.0728655 2.96224 0.634239 0.841756 816 | RP11-434J24.2 0.265823 0.175468 0.634414 0.841812 817 | ZBTB8OS -0.125006 1.30324 0.63697 0.843497 818 | CYP2J2 0.0910025 2.16034 0.638898 0.844643 819 | PFKP -0.0988461 3.33676 0.643242 0.847131 820 | SMARCA5 0.114479 1.46569 0.644686 0.848163 821 | SMAD7 0.0722192 3.58453 0.647822 0.850001 822 | SETBP1 0.137754 1.54728 0.651683 0.85216 823 | LLNLR-268E12.1 0.131016 1.08015 0.654433 0.853514 824 | FNDC3A 0.095115 1.84751 0.654694 0.853679 825 | SDCBP 0.0786277 2.90298 0.655381 0.854026 826 | NSF 0.111019 1.96711 0.658982 0.856139 827 | ALDH8A1 -0.0967739 2.5904 0.663284 0.858322 828 | IKZF2 0.131862 1.76844 0.663905 0.85854 829 | ELL2 -0.103701 2.10437 0.667191 0.860226 830 | MAPRE1 -0.120434 1.42712 0.670157 0.861659 831 | WDR53 -0.429013 -1.05603 0.671966 0.862553 832 | EGR2 -0.0781418 2.75053 0.674624 0.863666 833 | YWHAEP5 -0.164611 0.535504 0.675204 0.863865 834 | LINC00948 0.0884606 2.31882 0.676791 0.86478 835 | SYDE2 0.106111 1.63244 0.682455 0.867395 836 | GADD45B -0.0864373 1.89128 0.686641 0.869566 837 | GTF2H1 -0.0694572 2.86069 0.688776 0.870926 838 | OSGEP -0.157581 0.509684 0.688785 0.870927 839 | LATS1 -0.125778 0.843303 0.688833 0.87096 840 | ZKSCAN3 0.204387 -0.0399426 0.689113 0.871134 841 | HK2 0.11034 1.68252 0.697281 0.875121 842 | LRRC8A -0.104436 1.97578 0.699814 0.876533 843 | AP000361.2 -0.161049 0.0596945 0.704297 0.878892 844 | NSA2 0.409886 -1.41691 0.704953 0.879086 845 | CRK 0.0819388 2.24723 0.705238 0.879137 846 | ST3GAL1 -0.0729197 3.11931 0.705578 0.879309 847 | RNF19A 0.0496894 3.76535 0.705759 0.879375 848 | LARP7 -0.116143 1.5478 0.706266 0.879638 849 | CD93 -0.0988937 1.88783 0.707379 0.880201 850 | SIRT1 0.0819841 2.39191 0.707489 0.880297 851 | UTRN -0.0417878 4.0571 0.707643 0.880388 852 | ADAMTS1 0.0660631 2.3987 0.710491 0.882041 853 | TRIM52-AS1 -0.198081 -0.186181 0.711967 0.882749 854 | SPTBN1 -0.0510533 3.21976 0.716359 0.885167 855 | PPP1R15B 0.10706 1.19181 0.717427 0.885751 856 | PAPOLA -0.0513874 3.19728 0.717596 0.885813 857 | MORC3 -0.0810818 2.53006 0.721822 0.887804 858 | AKAP13 -0.0360993 4.57623 0.722688 0.888243 859 | LIFR -0.0617495 3.3587 0.724597 0.889011 860 | DNAJB1 0.273946 -0.836074 0.726756 0.889934 861 | ACTG1P12 0.493262 -1.37678 0.727723 0.890197 862 | ZNF770 0.114351 1.08742 0.728809 0.890749 863 | MYC -0.104072 1.00514 0.731531 0.892159 864 | ZNF621 0.0787205 2.01189 0.731597 0.892168 865 | CGGBP1 0.304249 -0.749399 0.732391 0.892532 866 | SLC10A7 0.0910323 2.16855 0.734746 0.893915 867 | FSTL1 0.0578287 3.10375 0.734833 0.893991 868 | USP34 0.0355578 4.15377 0.736701 0.894884 869 | CCL2 0.0805893 1.61987 0.739938 0.896481 870 | PHACTR2 0.0433279 3.57305 0.740013 0.896481 871 | RIT1 0.121374 0.802451 0.74171 0.897309 872 | BRWD1 0.0563044 3.14326 0.74258 0.897629 873 | SYNM -0.0986621 1.82423 0.74577 0.899473 874 | SNHG6 0.157634 0.612387 0.747369 0.900248 875 | RP3-368A4.6 -0.27153 -0.594858 0.750487 0.901903 876 | SLC6A6 -0.050936 3.21861 0.750541 0.901904 877 | PMP22 0.127868 0.898811 0.751798 0.902415 878 | TMEM116 0.142092 0.508567 0.752098 0.902568 879 | ATP5E 0.112353 0.887156 0.752874 0.902832 880 | PIGZ -0.0697383 2.3402 0.75364 0.903136 881 | RP11-270C12.3 0.10086 1.19645 0.754989 0.90378 882 | ARHGAP29 0.0610974 3.08807 0.759511 0.905623 883 | NKTR 0.054168 2.75394 0.760547 0.906036 884 | A2M 0.0504511 3.37611 0.763388 0.907477 885 | PAX8-AS1 -0.0386721 3.4543 0.764223 0.907693 886 | NEIL1 0.50738 -1.72302 0.769125 0.910033 887 | ZDHHC20 -0.0510941 2.71719 0.769587 0.910274 888 | MRC1 0.0877602 1.41879 0.777643 0.914157 889 | RP11-466H18.1 0.0729207 1.59882 0.778564 0.914581 890 | DCAF5 -0.10565 1.11055 0.77882 0.914656 891 | TSPYL2 0.105891 0.977937 0.779441 0.914896 892 | SLC2A5 -0.0498939 3.35106 0.78195 0.915999 893 | RPL37 -0.20848 -0.46642 0.785244 0.917499 894 | STARD9 -0.0350026 3.52292 0.78612 0.917888 895 | ERMARD 0.0685418 1.97477 0.78616 0.917925 896 | DDIT4 -0.166799 -0.217724 0.791538 0.920216 897 | RAB3GAP2 -0.0581999 2.16716 0.793092 0.920892 898 | MIEN1 0.172595 -0.478985 0.80155 0.924596 899 | GXYLT1 -0.0823296 0.950893 0.802188 0.924773 900 | RNF38 -0.0562452 2.37477 0.805615 0.926561 901 | SETD2 -0.0485455 2.79307 0.80808 0.927543 902 | PCED1A -0.0833576 0.322345 0.817137 0.931292 903 | CHMP4BP1 0.0919681 0.966769 0.817475 0.931439 904 | SPEN 0.0596408 2.00407 0.817667 0.931487 905 | HIPK1 -0.052998 2.23868 0.81872 0.931969 906 | AC073109.2 -0.0624456 1.4528 0.821132 0.933229 907 | NBEA 0.0340465 3.10403 0.821888 0.933569 908 | ZCCHC2 0.0347309 3.26502 0.824193 0.934703 909 | SUDS3 -0.057585 1.61952 0.825855 0.935437 910 | RCAN1 -0.038791 3.24318 0.826609 0.935903 911 | RP11-144L1.8 -0.0562841 1.43875 0.826964 0.936054 912 | GOSR1 -0.0481845 2.45827 0.827282 0.936157 913 | OPA1 0.0334582 3.38492 0.830561 0.937536 914 | RP13-36G14.4 0.107432 -0.0745183 0.83228 0.938385 915 | MSN 0.0646694 1.34146 0.833093 0.938634 916 | ATP5G2P4 0.0845116 0.710588 0.833451 0.938807 917 | ITIH5 -0.0251005 3.752 0.834066 0.939069 918 | AC096669.3 0.0799591 0.9958 0.834187 0.939134 919 | KDM5B 0.0323999 3.06682 0.834712 0.939239 920 | SACS 0.0326168 3.2165 0.836227 0.940032 921 | GIMAP7 0.388596 -1.71371 0.83647 0.940166 922 | RHOB -0.0746789 0.901526 0.84001 0.941368 923 | HNRNPU -0.141589 -0.279111 0.841378 0.94207 924 | ID1 -0.0986937 -0.0110149 0.842089 0.942403 925 | ING1 -0.1758 -0.879319 0.842113 0.942413 926 | EMP1 -0.0463632 2.6863 0.845534 0.943888 927 | PLAU -0.0500829 1.86573 0.847087 0.944622 928 | NEK7 0.035586 2.87222 0.849708 0.945639 929 | DCP2 -0.0377995 2.49276 0.849772 0.945639 930 | ATP13A3 -0.0240627 3.54841 0.850724 0.946081 931 | PAK2 0.0362836 2.2807 0.850914 0.946182 932 | CBR1 -0.0725152 0.477492 0.851098 0.946215 933 | PIN1 -0.10576 0.56313 0.852149 0.946565 934 | UBR3 -0.0265178 3.16981 0.860369 0.949962 935 | LEMD3 0.0561844 1.58331 0.863042 0.950785 936 | SNX29 0.0222174 3.34049 0.864968 0.951548 937 | GLI3 -0.0263275 3.34851 0.86703 0.952584 938 | DDX3Y 0.0783167 0.358869 0.868133 0.953067 939 | RP11-104F15.9 -0.0552606 1.21159 0.874316 0.955495 940 | PSMB7 -0.0395285 1.78895 0.87448 0.955525 941 | PGM2 -0.0387719 2.30764 0.874574 0.955529 942 | FUT1 -0.117444 -0.0968602 0.876533 0.956115 943 | C17orf89 0.136649 -1.60686 0.88018 0.95752 944 | ANKRD9 0.0331545 2.1471 0.880362 0.957598 945 | RANBP2 0.0499497 1.06362 0.883347 0.958522 946 | CEP170 -0.0289786 2.7442 0.884141 0.9588 947 | DLC1 0.0166944 4.25795 0.890625 0.961465 948 | PPP1R12B 0.0154797 4.08922 0.893866 0.962663 949 | MRPL32 0.108622 -0.233519 0.902902 0.96597 950 | ANKRD12 0.0315958 1.75825 0.90616 0.967084 951 | GJA1 -0.0445121 1.22399 0.908386 0.967836 952 | CHMP3 0.0230246 2.08869 0.909995 0.968546 953 | CTD-3014M21.1 0.0339527 1.48417 0.910554 0.968798 954 | PTPRG -0.0179864 3.46579 0.911895 0.9692 955 | IFIT2 0.0416659 1.09408 0.912203 0.969378 956 | JUN -0.208036 -1.83509 0.913478 0.96975 957 | CDK17 0.0187567 3.08622 0.914358 0.970067 958 | THBS1 0.0314 1.75108 0.914485 0.970113 959 | LUC7L3 -0.0236502 2.43169 0.915087 0.970344 960 | AMER1 0.0493318 0.180701 0.915249 0.970362 961 | TRO -0.0750458 -0.0370496 0.918309 0.97145 962 | QKI -0.0183508 3.02877 0.920854 0.97228 963 | CITED4 0.0760931 -0.872856 0.921835 0.972568 964 | FGD4 0.0137209 3.31662 0.924291 0.973524 965 | TBX3 0.0231901 2.01116 0.924669 0.973575 966 | WNK1 -0.0107767 3.97929 0.924703 0.973592 967 | LINC-PINT -0.0190297 2.46269 0.926358 0.974214 968 | DENND2C 0.0190467 2.46937 0.933209 0.976609 969 | ZFP36L2 -0.0672189 -1.76841 0.935573 0.977578 970 | CYR61 -0.0537745 -0.258455 0.936136 0.97774 971 | NUFIP2 -0.0207192 1.17906 0.941643 0.980052 972 | SYNJ2 -0.00951498 3.63683 0.945604 0.981383 973 | LITAF -0.00994253 3.76726 0.946632 0.981693 974 | ZFP36 -0.0288675 0.571529 0.946732 0.981734 975 | PLCB1 -0.00645912 4.03897 0.953824 0.983821 976 | HIPK2 -0.0187047 1.24968 0.954836 0.98434 977 | RNA5SP286 0.0184331 1.27439 0.956821 0.985162 978 | LRRC66 0.0172193 1.09746 0.957917 0.98546 979 | SMIM4 0.0373209 -0.591542 0.958841 0.98582 980 | CMB9-22P13.1 0.0171706 0.966454 0.961554 0.986884 981 | UTP20 0.00837016 2.30465 0.963027 0.987323 982 | VEGFA -0.0120245 1.9648 0.965511 0.987858 983 | HECTD1 -0.00615397 3.89035 0.966758 0.988267 984 | NFATC3 -0.00933475 1.96742 0.969131 0.989143 985 | KMT2C 0.00643227 3.42697 0.970204 0.989519 986 | EPB41L4A-AS1 -0.101425 -1.19489 0.972619 0.990273 987 | JDP2 0.0128878 0.535312 0.973276 0.990513 988 | TFRC -0.00710835 2.70608 0.973381 0.990545 989 | RP11-110G21.1 -0.0696023 -1.81281 0.973652 0.990626 990 | KCNE4 0.00916812 1.56011 0.973736 0.990626 991 | KIDINS220 0.00359038 3.60616 0.979134 0.992339 992 | UHMK1 0.00790442 1.30601 0.980133 0.992764 993 | NDEL1 -0.00328003 3.25296 0.983938 0.994152 994 | UBN2 0.00420202 2.38725 0.983973 0.994152 995 | NDUFB1 -0.0119008 -0.308871 0.984611 0.994377 996 | SLC36A2 -0.00272612 2.35935 0.989223 0.995832 997 | KDM7A -0.00339732 1.4435 0.99006 0.996081 998 | PCNX 0.00132885 3.84649 0.990797 0.996482 999 | CXCL2 0.00301103 1.27658 0.991513 0.996759 1000 | SETD7 0.0016649 1.98255 0.993927 0.997537 1001 | CAMLG 0.000398388 1.52812 0.99854 0.999498 1002 | -------------------------------------------------------------------------------- /inst/extdata/k9a.tsv: -------------------------------------------------------------------------------- 1 | logFC logCPM PValue adj.p.value 2 | TTN 1.15238 4.49057 4.82932e-35 2.40012e-30 3 | QKI 1.15728 4.25107 1.28108e-27 1.39071e-23 4 | CPED1 0.909482 4.26776 4.013e-25 2.08559e-21 5 | TRA2B 0.997601 4.12511 5.60009e-25 2.76331e-21 6 | CPEB4 1.2595 4.22725 1.83109e-22 4.51769e-19 7 | TXNIP 1.4109 3.67406 4.40963e-22 9.97283e-19 8 | SLC25A25 1.36596 3.67287 8.07723e-22 1.68623e-18 9 | VGLL2 1.31364 3.19058 2.62949e-21 4.83813e-18 10 | NEB 0.834295 4.80405 1.76673e-20 2.77957e-17 11 | ARID5B 1.23822 3.85375 4.06385e-20 5.80473e-17 12 | HSPA1B 1.34051 2.14793 1.53633e-19 1.917e-16 13 | AKAP13 0.644196 5.05363 1.75009e-19 2.15892e-16 14 | IFRD1 0.817124 4.25111 1.40237e-18 1.3238e-15 15 | PPTC7 1.08942 3.39857 1.78138e-18 1.65283e-15 16 | NEDD4 0.858233 4.30217 4.8944e-18 4.18363e-15 17 | PCBP2 1.08635 3.18421 5.72561e-18 4.7812e-15 18 | ZNF106 1.13649 3.55354 1.15175e-17 8.8674e-15 19 | PPP1R12B 0.666202 4.78384 4.0281e-17 2.76758e-14 20 | KLF9 1.22273 3.39312 4.80631e-17 3.26099e-14 21 | ATP1B1 1.411 2.83719 8.8221e-17 5.56802e-14 22 | TRDN 1.12389 3.51547 1.06846e-16 6.66601e-14 23 | NR4A1 1.1769 4.88784 1.45856e-16 8.79652e-14 24 | USP2 1.40698 3.72686 3.0193e-16 1.69827e-13 25 | PPP1R15A 1.38133 3.09471 4.25966e-16 2.33543e-13 26 | TSC22D2 0.987739 4.16492 6.90944e-16 3.6061e-13 27 | PK 0.916605 3.9625 7.7587e-16 4.02996e-13 28 | ZBTB16 0.980517 4.79776 1.47857e-15 7.23013e-13 29 | LINC01372 1.78908 0.623098 4.31696e-15 1.93651e-12 30 | SVIL 0.794761 5.03209 4.59032e-15 2.03392e-12 31 | STOM 1.33407 2.30367 6.2884e-15 2.70893e-12 32 | DUSP1 1.3018 3.45427 7.40691e-15 3.11656e-12 33 | CMYA5 0.907044 3.46605 8.82405e-15 3.62846e-12 34 | HSP90AB1 1.39976 3.10523 2.29096e-14 8.78799e-12 35 | RBFOX2 0.665635 4.5562 2.4431e-14 9.30197e-12 36 | XIRP2 0.862075 3.17342 2.60054e-14 9.80234e-12 37 | C1orf132 1.21267 3.97583 3.04671e-14 1.12881e-11 38 | ARHGEF12 0.599862 4.68594 4.06089e-14 1.44065e-11 39 | RP11-64B16.4 1.76252 0.395014 4.33236e-14 1.51712e-11 40 | TSC22D1 0.995062 2.84393 4.4006e-14 1.53114e-11 41 | ABRA 1.1387 2.78713 4.9427e-14 1.69263e-11 42 | AGL 0.825074 3.74955 8.49862e-14 2.77887e-11 43 | RNF122 1.0524 2.89857 9.17041e-14 2.9453e-11 44 | PLCL1 0.674321 3.98134 1.08007e-13 3.4084e-11 45 | NRIP1 0.967868 2.37588 1.08889e-13 3.41637e-11 46 | PICALM 0.698988 4.3301 2.18977e-13 6.30544e-11 47 | MAP4 0.794382 4.64918 2.70951e-13 7.62012e-11 48 | VEGFA 1.27806 2.69884 3.38484e-13 9.30249e-11 49 | SORBS1 0.79654 4.5836 3.92084e-13 1.05617e-10 50 | WBP1L 1.00862 3.43895 4.75174e-13 1.24598e-10 51 | FOXO3 1.22116 3.77845 5.86942e-13 1.49177e-10 52 | SRSF3 1.10189 3.22305 6.39917e-13 1.60804e-10 53 | MED13 0.86501 3.54927 6.69174e-13 1.66232e-10 54 | TNPO1 0.795771 3.57877 9.88426e-13 2.34281e-10 55 | NR4A2 0.970948 4.19036 1.02272e-12 2.41356e-10 56 | G3BP2 0.905329 3.42314 1.06974e-12 2.51903e-10 57 | SDCBP 0.668154 3.74403 1.15143e-12 2.68807e-10 58 | SLC15A4 1.03004 3.79699 1.1549e-12 2.6904e-10 59 | FOS 1.18421 3.01805 1.17987e-12 2.73683e-10 60 | MCL1 1.22085 3.15736 1.55726e-12 3.51458e-10 61 | OTUD4 0.971963 3.54258 1.9389e-12 4.23503e-10 62 | KLF10 1.01878 3.34996 4.54581e-12 9.1385e-10 63 | ID3 1.06222 2.74769 8.58366e-12 1.59285e-09 64 | BACH1 0.717803 3.78475 1.14265e-11 2.07082e-09 65 | HSPH1 0.989689 3.36817 1.28157e-11 2.29218e-09 66 | JUN 1.47126 0.708593 1.35184e-11 2.40183e-09 67 | HNRNPU 1.3261 1.39429 1.47286e-11 2.58004e-09 68 | NUFIP2 0.961079 3.06501 1.50871e-11 2.62469e-09 69 | FKBP5 1.00842 3.75293 1.52337e-11 2.63625e-09 70 | NR4A3 1.04911 2.41029 1.8578e-11 3.11337e-09 71 | RP11-159D12.2 1.07229 1.81281 2.70925e-11 4.33149e-09 72 | CRYBG3 0.887174 3.20703 2.7433e-11 4.37305e-09 73 | XIRP1 1.2193 2.54779 2.87037e-11 4.56221e-09 74 | ENAH 0.646744 4.41612 3.14382e-11 4.95333e-09 75 | NEK7 0.665547 3.73282 3.19884e-11 5.02542e-09 76 | CSRNP1 1.15208 2.97918 3.27129e-11 5.1244e-09 77 | RSL24D1 0.821913 2.69022 3.87858e-11 5.9723e-09 78 | DUSP16 0.956104 3.53023 4.25137e-11 6.4278e-09 79 | CTD-2003C8.2 1.10513 2.47868 4.57054e-11 6.86258e-09 80 | OSBPL11 0.865738 2.86379 8.39764e-11 1.16427e-08 81 | DBP 1.48866 1.49288 9.49705e-11 1.28871e-08 82 | ZFP36L1 1.00814 4.05679 1.08566e-10 1.44255e-08 83 | NXPE3 0.752806 3.31524 1.10579e-10 1.4671e-08 84 | SLC7A2 0.911266 2.08904 1.40924e-10 1.81474e-08 85 | ATP5G2 0.9555 3.06924 1.47473e-10 1.88122e-08 86 | PDE4B 0.486343 4.72023 1.48144e-10 1.88756e-08 87 | KLF6 1.08879 3.82547 1.58117e-10 2.00055e-08 88 | RAPGEF2 0.604306 4.72481 1.59973e-10 2.01618e-08 89 | KIAA2018 0.720612 3.57577 1.63796e-10 2.05801e-08 90 | LINC00948 0.808646 2.95422 1.90583e-10 2.35908e-08 91 | HSPB1 1.22348 2.97255 2.70149e-10 3.17044e-08 92 | DPYD 0.775837 2.87628 3.00365e-10 3.4725e-08 93 | FYCO1 0.920828 3.23355 3.38673e-10 3.85785e-08 94 | HES1 1.41761 3.04161 4.06722e-10 4.54244e-08 95 | RBPJ 0.482806 4.56785 4.09769e-10 4.56707e-08 96 | SKIL 0.79969 3.13152 5.27398e-10 5.69679e-08 97 | HNRNPH1 0.906025 3.4979 6.9189e-10 7.2082e-08 98 | ZBTB38 0.528564 4.69802 7.80396e-10 7.9824e-08 99 | CACNA2D1 0.524317 4.36812 8.32462e-10 8.40719e-08 100 | ZCCHC2 0.654153 3.82116 9.13723e-10 9.1001e-08 101 | TMED4 0.893801 2.61209 9.39086e-10 9.29872e-08 102 | TRAF3IP2 0.982021 2.54003 9.72736e-10 9.54904e-08 103 | UBL3 0.798325 3.02342 1.06077e-09 1.03e-07 104 | OTUD1 0.922565 3.08832 1.06878e-09 1.03408e-07 105 | SLC36A2 0.91696 3.16116 1.13238e-09 1.08306e-07 106 | RTN2 1.07847 1.37042 1.20387e-09 1.14139e-07 107 | MIR133A1HG 1.77235 1.0844 1.24109e-09 1.17258e-07 108 | DCP1A 0.785964 2.84865 1.50965e-09 1.38649e-07 109 | KIAA0232 0.917415 3.66033 1.75851e-09 1.58554e-07 110 | ITPRIP 0.733595 3.7128 1.85984e-09 1.66583e-07 111 | RAB21 0.80413 3.39822 1.98458e-09 1.76301e-07 112 | PPP2R1B 0.771823 3.9117 2.25288e-09 1.97389e-07 113 | IRF2BP2 1.05017 1.653 2.69799e-09 2.31165e-07 114 | HECTD1 0.467593 4.58316 2.8953e-09 2.45935e-07 115 | AHCYL2 0.542255 4.4295 3.33228e-09 2.76773e-07 116 | HERC1 0.539091 4.16974 3.5331e-09 2.90343e-07 117 | KIF1B 0.649853 4.09839 4.09841e-09 3.30443e-07 118 | ZBED5-AS1 1.24525 1.495 4.66622e-09 3.68649e-07 119 | WNK1 0.527153 4.78106 5.28042e-09 4.10327e-07 120 | DMD 0.547422 4.40726 5.31186e-09 4.12475e-07 121 | PJA2 0.737886 3.25282 6.15392e-09 4.69466e-07 122 | HIPK3 0.882388 3.74727 7.69089e-09 5.70677e-07 123 | CTD-3014M21.1 0.923116 2.29742 7.95557e-09 5.88306e-07 124 | KDM2A 0.551743 4.72467 8.08635e-09 5.96352e-07 125 | ARFGEF2 0.754236 2.91225 9.57721e-09 6.91272e-07 126 | FNDC3B 0.590446 4.41668 1.05638e-08 7.53647e-07 127 | FOXN3 0.589625 3.90111 1.29908e-08 9.00097e-07 128 | FGD4 0.642089 4.05653 1.36065e-08 9.3664e-07 129 | FEM1C 0.997365 2.84114 1.44302e-08 9.8275e-07 130 | ARPC2 0.554626 4.06101 1.52353e-08 1.02726e-06 131 | MED13L 0.536833 4.0547 1.5278e-08 1.02951e-06 132 | RICTOR 0.60702 3.91329 1.58835e-08 1.06174e-06 133 | SLC38A2 1.1102 2.13583 2.02057e-08 1.30331e-06 134 | FNDC3A 0.738644 2.6598 2.02948e-08 1.30754e-06 135 | HIVEP2 0.942808 3.37927 2.03778e-08 1.31052e-06 136 | PPP4R2 0.820135 2.79286 2.1676e-08 1.38011e-06 137 | LINC-PINT 0.776588 3.3871 2.21829e-08 1.40908e-06 138 | UBR5 0.476804 4.48064 2.48976e-08 1.55334e-06 139 | ALPK3 0.944457 3.41703 2.98466e-08 1.81009e-06 140 | ZFHX3 0.802229 3.41696 2.99053e-08 1.81263e-06 141 | SMAD7 0.824841 4.39689 3.4119e-08 2.0284e-06 142 | ZBTB10 1.46613 -0.0744911 3.74146e-08 2.19428e-06 143 | SPTBN1 0.591861 4.13533 3.75835e-08 2.20062e-06 144 | BMI1 0.844616 2.69588 3.93431e-08 2.29499e-06 145 | MSL2 0.792784 3.46278 4.36747e-08 2.5099e-06 146 | KDM6B 0.911589 3.68686 4.49502e-08 2.57495e-06 147 | PDK4 0.940456 2.30726 4.64292e-08 2.63886e-06 148 | EIF3A 0.930036 3.04678 4.9208e-08 2.76925e-06 149 | SACS 0.557471 3.64422 5.21681e-08 2.91318e-06 150 | NR1D1 1.17175 2.81862 7.12447e-08 3.78938e-06 151 | FAM46A 0.852391 3.07333 7.20528e-08 3.8263e-06 152 | MYH2 0.967537 2.86794 8.09703e-08 4.23609e-06 153 | SDE2 0.925753 2.37866 9.4102e-08 4.82771e-06 154 | SRSF5 0.734633 3.37808 9.88199e-08 5.03879e-06 155 | ACSL3 0.583277 3.59015 1.03588e-07 5.2523e-06 156 | CTNNA3 0.564871 3.74782 1.0403e-07 5.2698e-06 157 | PHACTR2 0.48466 4.1607 1.08606e-07 5.45344e-06 158 | TP53INP2 0.782253 2.79624 1.14766e-07 5.70449e-06 159 | GSS 0.787027 2.72278 1.24579e-07 6.08637e-06 160 | MAGED2 0.958039 1.4418 1.24686e-07 6.08884e-06 161 | PYGO1 0.79464 3.00297 1.30104e-07 6.30998e-06 162 | FLNC 1.04652 3.88933 1.43015e-07 6.83632e-06 163 | IKZF2 0.71287 2.5259 1.43164e-07 6.84042e-06 164 | CDKN1A 1.02165 2.0331 1.46707e-07 6.98364e-06 165 | GOLGB1 0.639914 3.27843 1.4674e-07 6.98364e-06 166 | PLS3 0.854966 2.45944 1.47326e-07 7.00261e-06 167 | TFRC 0.628546 3.53729 1.51887e-07 7.18171e-06 168 | SDHC 0.733725 2.74361 1.64803e-07 7.69153e-06 169 | CTB-52I2.4 0.904533 2.50973 1.64926e-07 7.69153e-06 170 | PTPN14 0.676052 3.3703 1.67313e-07 7.78191e-06 171 | PPP1R15B 0.952164 3.23588 1.72195e-07 7.97824e-06 172 | IER5 0.909158 3.20375 1.91802e-07 8.74483e-06 173 | NFKBIA 0.879185 3.06205 1.93344e-07 8.80774e-06 174 | ZYG11B 0.789434 2.65523 2.11334e-07 9.49971e-06 175 | FAM193B 0.60079 3.56749 2.15007e-07 9.63688e-06 176 | NCOA3 0.604489 3.35665 2.33656e-07 1.03657e-05 177 | CDK12 0.524858 3.99082 2.41046e-07 1.06414e-05 178 | PHF3 0.478229 4.22404 2.42992e-07 1.07142e-05 179 | ATP1B4 0.952315 0.963952 2.57444e-07 1.126e-05 180 | DNAJC7 0.668161 2.62349 2.67066e-07 1.16246e-05 181 | ITGB1BP2 0.91958 1.06326 2.71439e-07 1.17819e-05 182 | PRDM2 0.480239 4.28567 2.94315e-07 1.25692e-05 183 | LIX1L 1.01265 2.34761 3.03992e-07 1.28942e-05 184 | TRIB1 0.853491 3.72487 3.05941e-07 1.29606e-05 185 | FAM189A2 0.562392 3.98939 3.29072e-07 1.37927e-05 186 | HDDC2 0.544612 3.40935 3.40344e-07 1.4145e-05 187 | C14orf166 0.586858 2.83259 3.81509e-07 1.56285e-05 188 | SUCO 0.475152 3.97022 4.34549e-07 1.73666e-05 189 | MARCH6 0.573836 3.47575 4.68636e-07 1.85603e-05 190 | GLUL 0.876868 3.76631 4.76386e-07 1.8826e-05 191 | GIGYF2 0.452322 4.35528 4.95799e-07 1.93955e-05 192 | ZC3H15 0.707022 2.46372 5.41541e-07 2.09658e-05 193 | UTRN 0.380256 4.71891 5.6992e-07 2.19315e-05 194 | ADAMTS1 0.814321 3.56384 5.74449e-07 2.2098e-05 195 | MKL1 0.514911 4.06461 5.96102e-07 2.27776e-05 196 | GOSR1 0.528699 3.27014 6.192e-07 2.35276e-05 197 | SMG1 0.603158 3.39686 6.26587e-07 2.37751e-05 198 | IQGAP1 0.48093 4.00224 6.55723e-07 2.46907e-05 199 | HNRNPA0 0.811441 3.37747 6.90814e-07 2.58596e-05 200 | UHMK1 0.800258 2.4678 7.14064e-07 2.65651e-05 201 | SYNM 0.691047 2.26684 7.32168e-07 2.70992e-05 202 | FERMT2 0.452429 4.12159 7.72295e-07 2.83141e-05 203 | SIRT1 0.590433 3.28348 7.80407e-07 2.85633e-05 204 | ATF3 0.775663 3.87235 8.41058e-07 3.04647e-05 205 | BCL3 0.855607 3.48267 8.64329e-07 3.12451e-05 206 | RYBP 0.84279 2.28168 8.71753e-07 3.14925e-05 207 | RPL37 1.06276 0.399555 9.1682e-07 3.2804e-05 208 | RNF19A 0.470445 4.32772 9.60945e-07 3.418e-05 209 | IPO7 0.504065 3.41638 9.87724e-07 3.49504e-05 210 | DCP2 0.577286 3.50737 1.02279e-06 3.60024e-05 211 | ZNF770 0.780523 2.77526 1.05373e-06 3.68762e-05 212 | NAF1 0.551009 3.34705 1.11055e-06 3.86031e-05 213 | PIAS1 0.478757 3.94131 1.15028e-06 3.9793e-05 214 | LTN1 0.543835 3.19536 1.2048e-06 4.14284e-05 215 | AHNAK 0.610621 2.77515 1.28568e-06 4.37112e-05 216 | PLEKHA1 0.699047 3.5478 1.33824e-06 4.53278e-05 217 | COX7C 0.811 2.01025 1.39167e-06 4.67727e-05 218 | CSRP3 0.767671 2.51802 1.39383e-06 4.68308e-05 219 | CMB9-22P13.1 0.737378 1.62498 1.39688e-06 4.69043e-05 220 | LIFR 0.535857 3.45117 1.59796e-06 5.25329e-05 221 | SYNPO2 0.628355 3.33999 1.65019e-06 5.39663e-05 222 | HSP90AA1 0.933058 3.19528 1.76534e-06 5.71207e-05 223 | PER2 0.666444 3.78409 1.86797e-06 5.99236e-05 224 | ZFP36L2 1.16671 0.149472 1.87692e-06 6.01574e-05 225 | DDX3X 1.06262 2.50581 1.88067e-06 6.02242e-05 226 | SNRK 0.55044 3.35782 1.93897e-06 6.17814e-05 227 | NDEL1 0.453084 3.88258 2.05208e-06 6.47958e-05 228 | UBR3 0.458072 3.86129 2.07985e-06 6.5539e-05 229 | ACYP2 0.639968 2.47975 2.12792e-06 6.68019e-05 230 | TPCN1 0.569773 3.6775 2.23602e-06 6.97516e-05 231 | TBX3 0.725968 3.38846 2.28408e-06 7.10874e-05 232 | MTCH1 0.634281 3.72155 2.36071e-06 7.30952e-05 233 | HIST1H2BD 1.63716 -0.432568 2.82239e-06 8.47463e-05 234 | AMD1 0.65501 3.60409 2.87705e-06 8.61345e-05 235 | ATP1A1 0.468554 4.09809 3.00976e-06 8.94661e-05 236 | EFR3A 0.44723 3.88542 3.01922e-06 8.96736e-05 237 | IL15 0.508666 3.21299 3.12697e-06 9.2193e-05 238 | KIAA0355 0.668169 3.34988 3.21239e-06 9.43527e-05 239 | CAMLG 0.878402 2.51221 3.23743e-06 9.48828e-05 240 | SETBP1 0.613576 1.97753 3.2778e-06 9.59105e-05 241 | COX7B 1.25462 1.046 3.28772e-06 9.61489e-05 242 | MYC 1.0316 2.1946 3.44818e-06 0.000100194 243 | RP11-255P5.3 0.753291 1.87047 3.46752e-06 0.000100702 244 | DHRS3 0.771799 2.60082 3.55992e-06 0.000102978 245 | ZSWIM1 0.678432 1.67057 3.6146e-06 0.000104276 246 | YWHAQ 0.687917 3.16224 4.11806e-06 0.000116539 247 | BCAS2 0.940442 1.72905 4.33291e-06 0.000121166 248 | RAB3GAP2 0.607046 2.865 4.91115e-06 0.000134836 249 | CCDC117 0.790529 2.22461 5.02464e-06 0.000137499 250 | KCNJ8 0.670148 2.13877 5.38756e-06 0.000145813 251 | RPS24 0.598504 2.72806 5.51812e-06 0.000148459 252 | SLK 0.558437 3.22811 5.65131e-06 0.000151404 253 | ATOH8 0.698782 3.94952 6.27777e-06 0.000165011 254 | CDK17 0.453979 3.92905 6.47508e-06 0.000169255 255 | ANKRD9 0.836732 2.96479 6.47857e-06 0.000169264 256 | DUSP5 0.673145 1.4509 6.67169e-06 0.000173517 257 | KIF13A 0.560199 3.57697 6.70246e-06 0.000174233 258 | CYR61 1.06095 1.00936 7.27363e-06 0.000186008 259 | SOCS5 0.685533 2.60173 8.99418e-06 0.000221334 260 | CACNB1 0.636109 3.43604 9.23722e-06 0.000226307 261 | CACNG1 0.752066 2.76482 9.82072e-06 0.000237864 262 | PLCD4 0.567767 3.25604 9.92413e-06 0.000240048 263 | MXD1 0.643093 3.27968 1.06173e-05 0.000254325 264 | RNF145 0.526011 2.81356 1.13971e-05 0.000269551 265 | PSMB7 0.613827 2.37202 1.17478e-05 0.00027652 266 | PTPRG 0.564882 4.15789 1.25009e-05 0.000291215 267 | PAIP2B 0.760973 2.2854 1.33047e-05 0.000306259 268 | DENND2C 0.66982 3.07799 1.36285e-05 0.000312387 269 | RND3 0.530158 3.78661 1.37356e-05 0.000314245 270 | LATS2 0.683642 3.09704 1.45225e-05 0.000328646 271 | RSRP1 0.837974 0.845722 1.48016e-05 0.000334057 272 | REEP1 0.535577 3.39295 1.54183e-05 0.000345461 273 | USP9X 0.614781 3.35093 1.57147e-05 0.000350752 274 | PIM1 0.820088 2.96031 1.64814e-05 0.000364171 275 | PSMA4 0.652961 2.3986 1.77536e-05 0.000386692 276 | GTF3A 0.52316 3.36421 1.90082e-05 0.000410395 277 | UBN2 0.585495 3.30274 1.91802e-05 0.000413206 278 | FOSB 0.816457 2.86455 2.05542e-05 0.000437768 279 | CLK2 0.711001 2.55403 2.12011e-05 0.000449693 280 | RCAN1 0.44443 3.79466 2.24153e-05 0.000472218 281 | NFIL3 0.886865 1.83688 2.30999e-05 0.000484758 282 | IFIT2 0.712633 1.72934 2.55489e-05 0.000525586 283 | HSPD1 0.726959 1.70264 2.56595e-05 0.000527102 284 | RMND5A 0.512257 2.85726 2.62312e-05 0.000536976 285 | ZFP36 0.847672 0.727148 3.07129e-05 0.000609524 286 | CRK 0.638746 2.92362 3.19044e-05 0.000628241 287 | USP37 0.447915 3.22265 3.26953e-05 0.000641364 288 | UGCG 0.573441 3.07363 3.49986e-05 0.000678091 289 | HEG1 0.409517 3.56907 3.51842e-05 0.000680958 290 | CSNK1D 0.595078 3.39482 3.53945e-05 0.000684539 291 | SETD5 0.415482 3.95093 3.58776e-05 0.000691911 292 | IVNS1ABP 0.539623 2.49835 3.67256e-05 0.000706132 293 | SMAD5 0.458992 2.97534 3.6854e-05 0.000708098 294 | SLC10A7 0.505079 3.26296 3.88781e-05 0.000740048 295 | FOSL2 0.944338 1.80993 3.93013e-05 0.000746401 296 | LAMC1 0.516335 4.18265 4.33357e-05 0.000807622 297 | CPD 0.438143 3.78191 4.37961e-05 0.000814124 298 | SNAI1 0.673474 1.96138 4.45814e-05 0.000827145 299 | ARRDC4 0.702663 2.40842 4.80132e-05 0.000876585 300 | STK32B -0.402017 3.89865 4.96995e-05 0.000901458 301 | GXYLT1 0.636748 2.42268 5.03425e-05 0.000911183 302 | COX7A1 0.951987 2.32648 5.13641e-05 0.000927466 303 | AMZ2P1 0.629826 2.38275 5.20232e-05 0.000936875 304 | LBR 0.429041 3.82875 5.34227e-05 0.000958737 305 | CNIH4 0.583325 2.87377 5.54737e-05 0.000989494 306 | SEMA6D 0.334141 4.4194 5.62433e-05 0.00100026 307 | FBXL14 0.660878 3.07146 6.21962e-05 0.00108708 308 | NKTR 0.416882 3.36854 6.33788e-05 0.00110348 309 | SERPINH1 0.60604 3.55764 6.478e-05 0.0011223 310 | CHD1 0.428955 3.09532 6.52802e-05 0.00112879 311 | JOSD1 0.536016 3.11911 6.58551e-05 0.00113711 312 | SENP2 0.420668 3.25397 6.71208e-05 0.00115383 313 | NDUFB1 1.0786 0.0353974 7.0481e-05 0.00119906 314 | OBSCN 0.49409 3.97353 7.04993e-05 0.00119918 315 | KIDINS220 0.345227 4.0315 7.47177e-05 0.00125832 316 | MAMSTR 0.727205 2.88822 7.48051e-05 0.0012596 317 | PRKRIP1 0.622836 2.77261 7.79792e-05 0.00130254 318 | HECA 0.674346 2.51624 8.5957e-05 0.00141019 319 | NIN 0.436182 4.21212 8.68653e-05 0.00142208 320 | ATP5E 0.67412 2.42344 8.89859e-05 0.00144915 321 | RNF38 0.567254 3.70433 9.63853e-05 0.00154691 322 | CLIC4 0.690854 2.35349 9.72451e-05 0.00155657 323 | PAK2 0.45372 3.2799 9.88288e-05 0.0015768 324 | LINC01091 0.524466 2.36543 9.89244e-05 0.00157786 325 | ARID5A 0.689549 3.20741 0.00010156 0.00161327 326 | RASD1 0.709775 2.3198 0.000102654 0.00162564 327 | RHOB 0.858377 2.12164 0.000111622 0.00174099 328 | RIT1 0.662894 1.71741 0.000112332 0.0017493 329 | UBE2E3 0.410749 3.19491 0.000120795 0.00185203 330 | TIPARP 0.671985 1.791 0.000132261 0.0019918 331 | RP11-446N19.1 0.67069 1.17247 0.000132415 0.00199315 332 | EGR1 0.859652 2.60039 0.000134658 0.00201963 333 | FOXJ2 0.571602 3.48284 0.000140885 0.0020968 334 | NT5C1A 0.65876 2.44159 0.000142755 0.0021194 335 | SRF 0.618819 3.15733 0.000146606 0.00216385 336 | EMP1 0.453848 3.04875 0.000146887 0.00216653 337 | OSBPL7 0.568313 3.48825 0.000149859 0.00220347 338 | APOLD1 0.647692 2.48488 0.000161341 0.0023428 339 | EDEM3 0.461255 3.41098 0.000163511 0.00236607 340 | JUND 1.21503 -0.288071 0.000165603 0.00239188 341 | SNRNP35 0.586178 2.78458 0.000168675 0.00242731 342 | SESN2 0.5754 2.39893 0.000171006 0.00245073 343 | RP13-36G14.4 0.892985 0.068805 0.000183322 0.00258958 344 | MIDN 0.794718 3.27743 0.000188025 0.00264538 345 | SLC6A6 0.595687 3.39992 0.000188323 0.00264919 346 | PRMT9 0.405191 2.96612 0.00019047 0.00267523 347 | USP53 0.534101 2.67225 0.000193292 0.0027089 348 | TSPYL2 0.677555 1.76037 0.000195106 0.00272905 349 | MAFB 0.696455 3.07401 0.000197671 0.00275852 350 | BOLL 0.487106 2.82031 0.000200474 0.0027901 351 | SLC4A7 0.400484 3.36415 0.000201983 0.00280679 352 | ADCY9 0.502441 4.17662 0.000206842 0.0028604 353 | NDUFB7 0.619738 1.90996 0.000211357 0.00290839 354 | KLF4 0.798237 3.01596 0.000211448 0.00290898 355 | CEBPD 0.767996 3.24627 0.000214551 0.00294244 356 | PHC3 0.370385 3.69722 0.000235412 0.00316989 357 | RC3H2 0.42981 3.28969 0.000239955 0.0032163 358 | TMEM120B 0.504853 3.72532 0.000249235 0.00332019 359 | CEP170 0.501546 2.98936 0.000250859 0.00333773 360 | MTR 0.444854 3.40492 0.000253033 0.00335965 361 | HUWE1 0.39445 3.52738 0.000254405 0.0033721 362 | PNP 0.517757 2.27409 0.000255822 0.00338684 363 | FOXO1 0.640877 3.77649 0.000260413 0.00343502 364 | SYNCRIP 0.704647 3.2317 0.000264126 0.00347422 365 | KDM5B 0.366548 3.61638 0.000264316 0.00347569 366 | THBD 0.656649 3.10599 0.000277292 0.00360762 367 | STK38L 0.383398 3.74521 0.000278164 0.00361767 368 | LINC00657 0.770117 2.16909 0.000299433 0.0038332 369 | BHLHE40 1.1802 -0.389493 0.000302681 0.00386718 370 | AMER1 0.807554 1.72357 0.000316535 0.00399873 371 | PGPEP1L 0.600142 2.10545 0.00031706 0.00400345 372 | LONRF2 0.450495 2.66155 0.00032067 0.00403606 373 | AAAS 0.585837 3.66069 0.000323724 0.00406552 374 | RANBP2 0.559796 2.12729 0.000329147 0.00411844 375 | RYR1 0.406895 3.88629 0.000353151 0.00435697 376 | LPL 0.487764 2.96309 0.000373637 0.00455843 377 | THG1L 0.442699 2.41727 0.00039006 0.00471796 378 | WSB1 0.65601 1.46857 0.000405483 0.00486387 379 | LUC7L3 0.391042 3.05202 0.000406193 0.00487023 380 | RGS2 0.630239 2.4724 0.000406251 0.00487039 381 | RP11-426D19.1 0.573111 1.5795 0.000409082 0.00489621 382 | ACTN4 0.573289 4.11522 0.000410972 0.00491288 383 | GNE 0.434948 2.91106 0.000414838 0.00494766 384 | CDC42EP1 0.570908 2.70197 0.000415573 0.0049537 385 | KMT2C 0.358357 3.78571 0.000435315 0.00513601 386 | ADAMTS9 0.359318 3.63743 0.000445349 0.00523125 387 | PLAU 0.623515 2.4283 0.000448024 0.00525285 388 | GJA1 0.567832 2.37636 0.000464449 0.00541123 389 | SUV420H1 0.438034 3.83626 0.000468263 0.00544661 390 | FAM126A 0.387236 3.27156 0.000469841 0.00546203 391 | ARG2 0.599006 1.84629 0.000482462 0.00557532 392 | PIK3CB 0.34207 4.16487 0.000490931 0.00565874 393 | ETS2 0.574997 3.26401 0.000492967 0.00567678 394 | ZNF644 0.378053 3.13831 0.000514749 0.00587232 395 | NFATC3 0.602426 2.85935 0.000514911 0.00587232 396 | RELT 0.548775 3.08428 0.000548509 0.0061653 397 | RHOC 0.608891 2.52234 0.000562012 0.00628609 398 | TEK 0.493038 1.46441 0.000565964 0.00631767 399 | PLEC 0.592697 4.83399 0.000571118 0.00635886 400 | GLI3 0.379759 3.84981 0.000580628 0.00644623 401 | ARHGAP29 0.386387 3.65726 0.000583773 0.00647386 402 | JDP2 0.687617 0.922424 0.000587051 0.00650092 403 | AEBP2 0.472026 3.38018 0.000589366 0.00652056 404 | CHMP3 0.457367 2.24363 0.000622961 0.00680898 405 | FANCM 0.358895 3.14228 0.000645846 0.00700341 406 | CYP2J2 0.4419 2.57785 0.000664959 0.00716344 407 | ZDHHC20 0.353013 3.48395 0.000666232 0.00717545 408 | EPAS1 0.396994 4.49743 0.000684156 0.00732695 409 | ANKRD26 0.399862 3.65165 0.000705407 0.00750533 410 | FEN1 0.734482 0.284704 0.000721333 0.00763661 411 | ZKSCAN3 0.59634 1.91199 0.000751065 0.00787914 412 | TXLNG 0.519397 1.98676 0.000764459 0.00799339 413 | DIO3OS -0.612702 1.11669 0.000778716 0.00811121 414 | GADD45B 0.752737 2.63528 0.000791005 0.00821557 415 | KRAS 0.504175 2.48822 0.000801286 0.00829681 416 | NUDT3 0.665348 2.67466 0.000816221 0.00841707 417 | RP11-1334A24.5 0.727716 0.984606 0.000828323 0.00851598 418 | PCNX 0.316039 4.13012 0.000834537 0.00856365 419 | IL6ST 0.344986 3.21787 0.000875228 0.00888047 420 | SOCS6 0.430451 3.6594 0.000876443 0.00888864 421 | ST3GAL1 0.373641 3.4566 0.000890846 0.00900999 422 | C16orf72 0.686623 3.24423 0.000964624 0.00957541 423 | UAP1 0.471183 2.40573 0.000986336 0.00974904 424 | EPB41L2 0.234815 5.08624 0.00106605 0.0103189 425 | AZIN1 0.39191 2.99541 0.00107024 0.0103489 426 | HNRNPH3 0.473677 1.63252 0.00109255 0.0105244 427 | CCDC150P1 0.477001 1.26361 0.00112594 0.0107776 428 | PHF14 0.295836 3.86032 0.0011343 0.0108352 429 | ATXN7L2 0.558944 2.95449 0.00118752 0.0112206 430 | UPK3A 0.542339 1.79667 0.00120937 0.0113756 431 | PGM2 0.393914 2.70222 0.00123991 0.0115742 432 | AMY2B 0.490735 2.50362 0.00126222 0.0117364 433 | UTP20 0.378049 2.91845 0.00126349 0.0117448 434 | EGR2 0.451342 3.25856 0.00126356 0.0117448 435 | ITCH 0.392687 3.29109 0.00127464 0.0118157 436 | MCFD2 0.48665 1.53761 0.00127517 0.0118174 437 | MYH9 0.425264 4.01935 0.00130213 0.012018 438 | KLF13 0.527356 4.65286 0.00133627 0.0122653 439 | MUC3A -0.401129 2.88209 0.00138062 0.0125915 440 | ZNF460 0.884671 0.0490629 0.00142299 0.012888 441 | RPL27A 0.676699 0.701407 0.00149968 0.0134368 442 | SYDE2 0.468222 2.90261 0.00151901 0.0135716 443 | VASP 0.535153 3.54464 0.0015256 0.0136193 444 | PIK3R3 0.505683 1.87512 0.00152839 0.0136423 445 | MTMR12 0.45034 2.21578 0.00160789 0.0141943 446 | A2M 0.339535 3.67309 0.00162925 0.0143363 447 | TAL1 0.441814 2.20862 0.00174226 0.0150938 448 | SMIM19 0.604314 0.541831 0.00180328 0.0154955 449 | SMIM4 0.979586 -0.0569697 0.0019357 0.016335 450 | STIM2 0.337308 3.29636 0.00195332 0.0164518 451 | ING1 1.32815 0.727764 0.00202003 0.0168824 452 | CBL 0.523129 2.21044 0.00202739 0.0169257 453 | DIP2C 0.419705 3.6764 0.00205771 0.0171224 454 | METTL17 0.486091 2.17639 0.00208479 0.0172894 455 | SLC12A2 0.482464 2.17965 0.00209814 0.0173669 456 | PPRC1 0.42101 3.15051 0.00215992 0.0177552 457 | ZNF575 0.473221 2.04619 0.00224723 0.0182914 458 | MAPRE1 0.497968 2.00382 0.00227272 0.018438 459 | CD93 0.533655 2.0117 0.00231875 0.0187025 460 | MAD2L2 0.523982 2.0972 0.00231938 0.0187048 461 | SETD2 0.349092 3.05278 0.00232667 0.0187454 462 | GATSL2 0.523621 1.29644 0.00235396 0.0188994 463 | NES 0.491086 2.23585 0.00237104 0.0189982 464 | STX11 0.474111 2.10013 0.00238433 0.0190727 465 | OSGIN1 0.444578 3.09336 0.00245253 0.019479 466 | NBEA 0.288037 3.6538 0.00245325 0.0194833 467 | TEX101 -0.447738 2.13417 0.00245798 0.0195081 468 | DGCR8 0.540623 3.49279 0.00255281 0.0200685 469 | HELZ 0.408144 2.36106 0.00267375 0.0207636 470 | MAFF 0.543528 2.40156 0.0026752 0.0207712 471 | SLC25A45 0.537822 2.77527 0.00289518 0.0220943 472 | ECHDC2 0.335355 2.97722 0.00293429 0.0223093 473 | XRN1 0.285354 3.60115 0.00299755 0.0226905 474 | LATS1 0.848601 1.36823 0.00305213 0.022994 475 | GTPBP1 0.559068 2.92959 0.00311279 0.023319 476 | RBM28 0.391376 2.80572 0.0032233 0.0239551 477 | TBC1D10B 0.58793 1.61246 0.00344569 0.025199 478 | MORC3 0.371773 2.67331 0.00355953 0.0258141 479 | PTMS 0.884391 -0.276876 0.00365913 0.0263779 480 | SCAI 0.346995 3.30363 0.00368983 0.0265393 481 | DNAJB1 0.856605 -0.381293 0.00369234 0.0265503 482 | TRIP12 0.261501 3.68833 0.00383077 0.0272496 483 | LLNLR-268E12.1 0.46459 1.40922 0.003937 0.0278067 484 | MEOX1 0.495195 2.07307 0.00411233 0.0287532 485 | LY6G5C 0.451591 2.20149 0.00414702 0.028938 486 | BTN3A1 0.3621 2.78633 0.004175 0.0290822 487 | GIMAP8 0.419115 1.72702 0.00429282 0.0297261 488 | SLC22A5 0.401093 3.09429 0.00437359 0.0301392 489 | OPA1 0.271916 3.83975 0.00456885 0.0310979 490 | HSPA4L 0.45246 2.8371 0.00459964 0.0312452 491 | WEE1 0.384567 3.0461 0.00460367 0.0312624 492 | CHUK 0.661199 1.15934 0.0046439 0.0314608 493 | ORMDL1 0.424244 1.3633 0.00476898 0.0320185 494 | C8orf4 0.488403 1.96882 0.00505807 0.0334139 495 | PLK3 0.590972 2.26303 0.00507807 0.0335092 496 | SNRNP70 0.508153 2.95087 0.00508989 0.0335688 497 | HM13 0.352971 3.67547 0.00527642 0.0344952 498 | C1orf127 0.352475 3.53739 0.00529651 0.0345953 499 | ANTXR2 0.309714 3.38105 0.00549423 0.0355958 500 | SIVA1 0.592822 2.60108 0.00550692 0.0356649 501 | PIK3CA 0.414337 1.28009 0.00556973 0.0359879 502 | CCL2 0.433594 1.77841 0.00564304 0.0363297 503 | BPHL 0.377717 3.4039 0.00569091 0.0365577 504 | PITPNB 0.419098 2.32897 0.00569832 0.0365923 505 | WBP1 0.684914 0.560658 0.00578559 0.0370017 506 | SPOPL 0.433728 1.6687 0.00599532 0.0379576 507 | CYB561D2 -0.741517 -0.00458538 0.00613589 0.0385962 508 | TLN2 0.23718 4.38648 0.00635288 0.0395805 509 | DDIT4 0.832098 -0.120936 0.00647974 0.040184 510 | EGR3 0.578383 2.04071 0.00658677 0.0406757 511 | IL6 0.656216 0.13812 0.00659402 0.0407021 512 | HSP90AB3P -0.565529 0.780322 0.00669074 0.0411309 513 | TBL2 0.389869 2.77818 0.00706383 0.0428579 514 | RP11-326I11.5 0.93682 -0.218585 0.00709254 0.0429537 515 | SFXN5 0.293304 4.66865 0.00713636 0.0431565 516 | USP34 0.278033 4.22154 0.00746437 0.0445715 517 | TACC1 0.261034 4.46127 0.00777231 0.0458601 518 | ETS1 0.290482 3.16946 0.00778634 0.0459182 519 | KMT2D 0.372469 4.59303 0.00783835 0.0461422 520 | FBLN2 -0.384992 3.88195 0.00806665 0.0470625 521 | PDE11A 0.262417 4.01532 0.00817314 0.0475228 522 | KLHL15 0.533295 1.68009 0.00834124 0.0482419 523 | CAPN7 0.350539 2.68435 0.00842692 0.0485744 524 | CTB-89H12.4 0.716651 0.13275 0.00879074 0.0500575 525 | TNKS1BP1 0.365899 3.27427 0.00892561 0.0505656 526 | GOLGA8B 0.429999 3.14279 0.00900804 0.0508732 527 | TOB1 0.872228 -0.730024 0.00901832 0.0509154 528 | SLC2A5 0.294445 3.31909 0.00915982 0.0514387 529 | FOXK2 0.363291 3.85679 0.00944373 0.0524954 530 | PFKP 0.285696 3.01593 0.00958353 0.0530309 531 | GSTM2 0.389262 2.57011 0.00999913 0.0546179 532 | SRGN 0.381258 1.60943 0.0102647 0.0556763 533 | MSANTD4 0.341524 2.50904 0.0103534 0.0560119 534 | NF1 0.199673 4.39797 0.0106241 0.05707 535 | PMP22 0.42891 1.09021 0.0113957 0.0600166 536 | ATG2B 0.37 3.15412 0.0115519 0.0606256 537 | RP11-102K13.5 0.50632 2.26541 0.0120937 0.0627221 538 | SOCS7 0.511461 2.98457 0.012417 0.0639141 539 | ARHGAP5 0.394936 2.11552 0.0125178 0.06429 540 | BMP2 0.480113 2.60251 0.0125758 0.0645023 541 | HTT 0.243073 4.35972 0.0128698 0.0655887 542 | AMMECR1L 0.691526 -0.337095 0.0130805 0.0663327 543 | FAM65A 0.352484 2.58177 0.0132706 0.0670242 544 | MLLT4 0.211498 4.18581 0.0133922 0.0674565 545 | NEK4 0.317581 2.88907 0.0135311 0.0679479 546 | RP11-441O15.3 1.18451 -1.1543 0.0138832 0.0692103 547 | MSN 0.476664 2.33191 0.0139015 0.069263 548 | CIRBP 0.40683 2.72045 0.0139277 0.0693491 549 | DNAJB4 1.22884 -1.14457 0.0140482 0.0697955 550 | FRMD8 0.41889 2.64134 0.0140787 0.0698801 551 | GEM 0.456307 2.2366 0.0143756 0.0709435 552 | PAPD7 0.35142 3.73623 0.0148536 0.0726334 553 | AC006480.1 0.539102 0.702132 0.0151721 0.0737417 554 | PAPOLA 0.261 3.6073 0.0153349 0.0742808 555 | PELI2 1.13845 -0.877405 0.015663 0.0754729 556 | ANXA3 0.252936 3.04133 0.0161316 0.0770367 557 | RFX3 0.341594 3.309 0.016631 0.0786876 558 | CHD4 0.313899 3.82911 0.0166618 0.0787859 559 | PCOLCE -0.456332 0.849938 0.016939 0.0797348 560 | RP11-110G21.1 1.35177 -0.954211 0.0171573 0.0804452 561 | TMEM104 -0.433509 1.60517 0.0172105 0.0806282 562 | HMOX1 0.36572 2.69719 0.0174718 0.0815049 563 | RABEP2 0.446907 2.03355 0.0177416 0.0824052 564 | PCED1A -0.512719 0.753018 0.0177641 0.082467 565 | MAP9 0.392713 1.6374 0.018087 0.0835873 566 | CEP350 0.24837 3.58187 0.0184004 0.0846503 567 | LAS1L 0.43593 1.99666 0.0187484 0.0858147 568 | LCMT1 -0.254897 2.93707 0.0187731 0.0858959 569 | CGGBP1 0.690797 -0.2541 0.0188012 0.0859728 570 | ANAPC7 0.267472 3.29685 0.0189506 0.0864521 571 | MEF2BNB -0.486048 0.783456 0.01928 0.0874917 572 | CXXC1 0.417332 2.23798 0.0192927 0.0875277 573 | TAGLN2 0.391459 2.48156 0.0195342 0.0882663 574 | SLC29A2 0.509306 1.49377 0.0195921 0.0884303 575 | KAT2A 0.613642 -0.0933436 0.019945 0.0895066 576 | SRSF11 0.248471 3.31963 0.0201645 0.090201 577 | TXNDC16 0.359053 1.31694 0.0202935 0.0905838 578 | RPS6 0.428486 1.59854 0.0207966 0.0922114 579 | C11orf96 0.398017 2.551 0.0223462 0.0967302 580 | ZC3HAV1 0.328385 3.07753 0.0227666 0.098055 581 | LRRC14 0.471259 1.76911 0.0228844 0.0984296 582 | NUSAP1 0.292004 2.3612 0.0237094 0.100911 583 | RPS4XP3 -0.439046 0.981813 0.0244238 0.103106 584 | C11orf84 0.306649 2.60092 0.0245692 0.103482 585 | CITED4 0.780098 -0.169482 0.0250895 0.104966 586 | CXCR4 0.377076 2.34449 0.0260933 0.107806 587 | EXOC5 0.266961 2.51272 0.0263617 0.108523 588 | STAT2 0.355491 2.27737 0.0286037 0.114822 589 | CCNG2 0.318704 3.00272 0.0286659 0.114964 590 | ELL2 0.278145 2.43869 0.0293998 0.116911 591 | SAMD14 0.310938 3.7585 0.0304743 0.119828 592 | ANKRD12 0.394225 2.21173 0.0307589 0.120619 593 | SPEN 0.288848 2.45202 0.0309035 0.121024 594 | GAK -0.270906 3.95251 0.0309933 0.121262 595 | MYH3 0.299993 2.21528 0.0320966 0.124111 596 | DDX3Y 0.416586 0.639727 0.0323307 0.124684 597 | TMEM176B -0.471685 0.703296 0.0325641 0.125326 598 | ANXA1 0.243017 3.14997 0.0326625 0.12558 599 | CLEC1A 0.345856 1.68398 0.032781 0.125911 600 | TNK2 0.366176 4.23471 0.034112 0.129416 601 | ZNF596 0.739123 -0.652276 0.0341518 0.129481 602 | FAM160A1 0.230595 2.81297 0.0341549 0.129483 603 | CHRND 0.422362 1.23965 0.0344426 0.130174 604 | ATP13A3 0.22386 3.86672 0.0348888 0.131252 605 | YES1 0.327229 1.20618 0.0355028 0.132794 606 | RP11-96A15.1 0.304052 1.74398 0.0356071 0.133065 607 | RPL34 0.526577 -0.265365 0.036442 0.135152 608 | RASIP1 0.392417 1.31242 0.0366975 0.135766 609 | LRRC66 0.325765 1.3921 0.0373764 0.137467 610 | RRM2B 0.349103 1.87764 0.037951 0.138977 611 | MASP2 -0.373629 1.8902 0.0379551 0.138983 612 | RAPGEFL1 0.269819 2.63025 0.0405533 0.145503 613 | ALS2CL 0.277549 3.69345 0.0409414 0.146523 614 | SNHG6 0.45247 1.13078 0.0410351 0.146766 615 | ZNF621 0.29595 2.82653 0.0411991 0.147178 616 | FUT1 0.540404 -0.0483017 0.041217 0.147223 617 | GPR4 0.567195 0.497013 0.042134 0.149485 618 | DDX11 -0.283302 2.55966 0.042517 0.150406 619 | HIPK1 0.243734 2.64827 0.0427034 0.150798 620 | ATP8A1 0.178298 3.85821 0.0431865 0.151943 621 | ZZEF1 0.197679 3.50044 0.0435012 0.152631 622 | HMGN3 1.09683 -1.30113 0.0441541 0.154128 623 | BRWD1 0.205719 3.35861 0.0445955 0.155092 624 | EFR3B 0.277392 3.19775 0.0448368 0.155651 625 | SEC31B 0.198561 3.85656 0.0456102 0.157427 626 | GSTP1 0.406053 2.50419 0.0467088 0.15999 627 | DLL4 0.387312 2.58706 0.046881 0.160382 628 | ID1 0.671288 -0.37773 0.0472402 0.161205 629 | DYNC1H1 0.218946 3.82348 0.0483771 0.163792 630 | DOHH 0.308069 2.44247 0.0485119 0.164105 631 | RPS28P7 -0.315021 1.39622 0.0491558 0.165573 632 | SEMA4C 0.356437 3.3032 0.0503143 0.168164 633 | VWF -0.26103 3.23427 0.0516863 0.171221 634 | PIGZ 0.291958 3.12449 0.052676 0.17342 635 | CTD-3035D6.1 0.489905 0.389863 0.052817 0.173722 636 | CHMP4BP1 0.379353 0.892493 0.0560478 0.180639 637 | LASP1 0.326903 3.62165 0.0565012 0.181574 638 | ITIH4 0.274143 2.73245 0.0566455 0.181797 639 | PPP4R1 0.172496 3.65512 0.0568913 0.18224 640 | ALG1 0.313991 2.10866 0.0573514 0.183211 641 | NEK10 0.193067 3.54379 0.0588816 0.186248 642 | ASB7 0.287181 1.18107 0.0593816 0.187296 643 | TAP1 0.489957 0.0914528 0.0595945 0.187709 644 | MTMR9LP 0.368606 2.13084 0.0600173 0.18857 645 | SERP1 0.352725 1.03969 0.060395 0.189445 646 | DUSP6 0.385129 0.621906 0.0605023 0.189677 647 | HELZ2 -0.374085 2.88031 0.0607355 0.190194 648 | CIR1 0.295627 1.73567 0.0623772 0.193559 649 | LITAF 0.224679 3.8983 0.0626893 0.194184 650 | SF3A3 0.258448 2.20923 0.0627648 0.194332 651 | FGD3 -0.226799 3.75529 0.0633097 0.19546 652 | NSF 0.250958 2.22545 0.0636042 0.196134 653 | LRRC8A 0.314429 2.22231 0.0638651 0.196654 654 | ACOT13 0.295988 1.27631 0.0653554 0.199488 655 | RP1-309I22.2 0.278913 1.22099 0.0668261 0.202468 656 | MSX1 0.357336 2.43002 0.0671106 0.203024 657 | SMARCA5 0.24288 2.11163 0.0679928 0.204788 658 | WARS 0.214505 2.95123 0.0679935 0.204788 659 | LPCAT4 0.277046 2.96232 0.0682656 0.205311 660 | RP11-16F15.1 -0.381307 1.03765 0.0699466 0.208638 661 | DCAF5 0.288731 2.00462 0.0706252 0.209838 662 | NENF 0.268471 2.40301 0.071175 0.210969 663 | HIVEP1 0.234268 3.07533 0.0727467 0.213904 664 | CBR1 0.428035 1.22808 0.0749259 0.218162 665 | POLR2C 0.400436 0.895417 0.0755049 0.219307 666 | ERAP1 0.225944 2.49828 0.075936 0.220091 667 | MCAM 0.341571 2.31676 0.0774494 0.222861 668 | GCH1 0.304174 2.25433 0.0779117 0.223751 669 | NEDD9 0.18521 3.2204 0.0796355 0.226962 670 | HSP90AA2P -0.28527 1.15419 0.0825869 0.232328 671 | DLL1 0.36932 2.31171 0.0829923 0.23306 672 | HIPK2 -0.366635 1.29766 0.083029 0.23311 673 | TNFRSF14 -0.326766 0.611896 0.0839626 0.234817 674 | RGS16 0.300407 1.71695 0.0844599 0.235712 675 | NR6A1 0.362735 1.11276 0.0848293 0.23634 676 | ATP5HP4 0.293794 1.1739 0.0858639 0.238173 677 | MB21D1 0.260022 2.22001 0.0884097 0.242796 678 | SPON2 0.245013 3.69321 0.0885576 0.243079 679 | MOB3C 0.517811 -0.0359656 0.089338 0.244486 680 | POLG 0.246408 2.47197 0.0895158 0.2448 681 | LBX1-AS1 0.288128 1.94545 0.0906868 0.246938 682 | TUBB6 0.320711 1.94427 0.0907277 0.246988 683 | TBCC 0.891904 -1.5238 0.0909404 0.247325 684 | WISP2 0.212348 2.8172 0.0936276 0.251902 685 | ZBTB44 0.229085 2.10677 0.0955325 0.255041 686 | TET3 0.250616 2.56656 0.0956104 0.255199 687 | DNAJA4 0.205857 2.61785 0.0968596 0.257312 688 | SPRY4 0.418234 -0.0324568 0.0969509 0.257436 689 | MIR143HG 0.362195 1.14963 0.100296 0.263104 690 | NIFK 0.24211 1.45438 0.100848 0.263975 691 | AKAP12 0.200821 3.23561 0.101176 0.264506 692 | ARHGAP1 0.295573 2.19724 0.103249 0.267933 693 | HSBP1 0.536636 -0.91111 0.105614 0.271884 694 | SARDH -0.272643 3.60217 0.105836 0.272233 695 | MRPL32 0.374949 0.261029 0.106024 0.272569 696 | IRS2 0.877253 -1.07965 0.107445 0.274782 697 | SDC4 0.356772 0.0619392 0.107797 0.275383 698 | ZNF239 0.236174 2.48839 0.107975 0.275664 699 | HK2 0.351979 1.52041 0.110195 0.279358 700 | TMEM132A -0.293601 1.54528 0.115501 0.287838 701 | ADAMTS4 0.519434 -0.395753 0.117001 0.290295 702 | FLT1 -0.177966 3.30856 0.117394 0.290923 703 | CLDN5 0.255519 2.68244 0.119212 0.293774 704 | ARFGAP1 -0.408944 0.631034 0.119887 0.294814 705 | PLCB1 0.118449 4.30397 0.120428 0.295688 706 | GOLGA8A 0.246721 2.18093 0.121653 0.297571 707 | RP4-635E18.8 0.304869 0.501291 0.124966 0.302603 708 | FOSL1 0.301688 1.76748 0.126079 0.304319 709 | OBSL1 0.261534 1.76834 0.126929 0.305595 710 | TCEAL7 0.25732 0.887131 0.127409 0.306311 711 | NFE2L2 0.237652 2.1298 0.130523 0.310828 712 | RP11-164J13.1 0.483033 -0.172017 0.131572 0.31241 713 | PAN2 0.234523 2.13258 0.134458 0.316734 714 | AC004453.8 0.245513 1.1895 0.135546 0.31842 715 | UBXN11 0.202913 2.77334 0.137584 0.321309 716 | DNAJA1 0.354406 0.664177 0.138522 0.322723 717 | FAM53C 0.331843 0.193857 0.139675 0.32433 718 | BCL6B 0.22614 2.06623 0.142519 0.328412 719 | CXCL2 0.276637 2.02418 0.14258 0.328474 720 | ATF6B 0.269456 1.47144 0.143446 0.329727 721 | ANP32E 0.242737 1.39405 0.148356 0.336801 722 | UBAP1L 0.221741 1.89036 0.148453 0.33695 723 | ANKZF1 0.300436 0.623743 0.151682 0.34112 724 | RP11-144L1.8 -0.253135 1.06616 0.15313 0.343329 725 | KCNE4 0.244595 1.94756 0.154615 0.34535 726 | MRPS11 0.226576 1.19486 0.157846 0.349628 727 | AQP1 0.264101 3.0779 0.157943 0.349778 728 | SUDS3 0.234943 1.76189 0.160988 0.35401 729 | RP11-87E22.2 0.246485 1.94106 0.161486 0.354666 730 | TMEM259 0.321985 2.04095 0.162268 0.355778 731 | RP11-309L24.4 0.298743 1.27266 0.162458 0.356068 732 | LDLR 0.17287 3.04734 0.162557 0.356222 733 | PTPN11 0.225384 1.17357 0.162921 0.356721 734 | DDX52 0.202095 1.9197 0.164186 0.35844 735 | SETD7 0.196937 2.2664 0.16881 0.36499 736 | SLCO4A1 -0.250488 1.39719 0.169835 0.366397 737 | NHSL1 0.143202 3.61115 0.173232 0.371079 738 | MME 0.122855 3.89286 0.173421 0.371322 739 | LUC7L 0.211341 2.61808 0.175739 0.374263 740 | PTPRS -0.225331 3.10964 0.178503 0.377851 741 | KIAA0226 0.162171 2.73119 0.179409 0.379 742 | EIF4BP3 -0.264074 0.906832 0.179871 0.379551 743 | ATP11A -0.126819 4.48481 0.183345 0.38397 744 | RP11-373D23.2 0.40082 -0.346879 0.186136 0.38754 745 | PPP2R3B 0.178303 3.3045 0.187615 0.389515 746 | TRO 0.281718 0.767494 0.189261 0.391568 747 | YWHAEP5 -0.313513 0.0497987 0.190979 0.393775 748 | PPP1R27 0.500065 -0.778829 0.195211 0.398858 749 | CDK6 0.140017 2.86545 0.195578 0.3993 750 | ITGA1 0.156617 2.53628 0.195612 0.399321 751 | NSA2 0.65617 -1.18396 0.197767 0.401953 752 | ZC3H12A -0.38634 -0.0187562 0.199186 0.403716 753 | FKBP4 0.278024 0.588095 0.200553 0.405495 754 | GPR137C -0.215966 1.23902 0.202652 0.408036 755 | COA5 0.279185 1.09925 0.20562 0.411718 756 | ZYX 0.288317 0.268319 0.208906 0.41587 757 | PVRL2 0.214351 3.59137 0.212334 0.42006 758 | PPP1R14A 0.214329 2.29348 0.212879 0.420731 759 | AC083843.1 0.342189 0.0113986 0.213416 0.421386 760 | TMUB2 0.263333 0.18993 0.214236 0.422358 761 | ACLY 0.201882 3.11645 0.214987 0.423203 762 | L3MBTL1 -0.150129 3.46022 0.218433 0.427229 763 | ATP5G2P4 0.302254 0.507452 0.218594 0.427458 764 | LARP7 0.174853 1.76902 0.218673 0.427535 765 | UNQ6494 0.1499 2.43709 0.22027 0.429482 766 | SGSM1 -0.170924 2.60475 0.223005 0.432509 767 | RPS12 0.520662 -1.59484 0.224744 0.434475 768 | RGS5 0.199284 1.16441 0.224957 0.434711 769 | G6PD 0.244677 0.706796 0.225121 0.434915 770 | BTG3 0.155188 3.14628 0.225935 0.435746 771 | C16orf58 -0.186682 1.66182 0.22609 0.435936 772 | LTBP2 -0.189965 3.0938 0.226451 0.436262 773 | GTF2H1 0.133677 3.10394 0.230297 0.440775 774 | NSUN5P1 0.307592 -0.10343 0.232082 0.442854 775 | GABARAPL1 -0.414561 -0.59316 0.233557 0.444227 776 | GGT5 -0.205214 1.51261 0.238572 0.450052 777 | GIMAP7 0.641086 -0.691187 0.241059 0.452775 778 | SEPT1 -0.310046 -0.0949417 0.247314 0.459736 779 | MYO1E -0.152384 2.67017 0.248311 0.460751 780 | VPS13B 0.115166 3.70461 0.24968 0.462345 781 | RP11-466H18.1 -0.165788 1.45145 0.250256 0.462815 782 | CNN2 0.24568 2.15336 0.252828 0.465832 783 | SLC45A3 0.219766 2.5719 0.253563 0.466591 784 | ANKRD33B 0.218465 2.39158 0.259768 0.473871 785 | AC096669.3 0.270495 1.1806 0.260776 0.475088 786 | ACOX2 0.13552 3.58471 0.260889 0.475191 787 | ADAMTS13 -0.168349 2.06588 0.261334 0.475659 788 | ASAP2 0.123946 3.61096 0.266113 0.480792 789 | ZBTB47 0.207536 1.59173 0.267539 0.482486 790 | PPP3CB-AS1 0.246278 0.359694 0.271343 0.48671 791 | WDR34 0.167761 2.55774 0.274068 0.489665 792 | WAPAL 0.131518 2.80403 0.282015 0.498391 793 | GJA4 0.201114 1.69763 0.284353 0.500917 794 | ATP5O 0.161111 1.54229 0.285895 0.502655 795 | COL4A1 0.160273 2.4141 0.287515 0.504323 796 | KCP -0.169602 2.61622 0.288098 0.504868 797 | RP11-463O12.5 0.411017 -0.96688 0.289032 0.505908 798 | SLC2A3 0.121264 3.02549 0.291959 0.509005 799 | TINAGL1 0.18741 2.39749 0.296888 0.514548 800 | CYYR1 -0.164541 1.63358 0.29882 0.516576 801 | MCRS1 0.185876 1.73529 0.301884 0.519733 802 | STXBP5 0.210443 0.537843 0.303186 0.521046 803 | DOC2B -0.158009 2.77702 0.305575 0.523363 804 | SUGT1 0.188813 1.05676 0.30627 0.524082 805 | SERPINE2 0.091005 3.6701 0.308661 0.526449 806 | ITGA5 0.140354 3.18344 0.311199 0.528889 807 | KIAA1107 0.19749 1.06246 0.314488 0.532296 808 | ADAMTSL3 -0.110524 3.43172 0.317031 0.53475 809 | TPM4 -0.123816 3.04299 0.320755 0.538379 810 | UBE2C 0.181605 1.68712 0.320846 0.538461 811 | NIT1 0.199781 0.839851 0.323547 0.541207 812 | SDHCP4 -0.15878 1.263 0.327839 0.545412 813 | RP11-631N16.2 0.538898 -1.54237 0.331228 0.548889 814 | TNFRSF10D 0.187325 1.67702 0.332374 0.549956 815 | TTC31 -0.19895 1.28341 0.333683 0.551396 816 | KRTAP21-4P -0.201248 0.510645 0.342085 0.559711 817 | NOS1 0.138858 2.7454 0.34394 0.561418 818 | CA8 0.10588 2.75823 0.34422 0.561698 819 | ENGASE 0.168088 3.16871 0.34456 0.562015 820 | ATG4B -0.125934 2.85356 0.347919 0.565087 821 | BBS9 0.104422 3.4619 0.348146 0.565341 822 | MARCKSL1 0.204516 2.08538 0.357371 0.574096 823 | RAPGEF5 0.0773414 4.10552 0.360306 0.576778 824 | SERF2 0.400739 -0.916783 0.360988 0.577514 825 | NDRG1 0.102068 4.22379 0.361475 0.577975 826 | FAM136A 0.496074 -1.55736 0.361633 0.578112 827 | ENC1 0.139731 1.59509 0.362372 0.578815 828 | LRRK2 0.10714 3.03874 0.363431 0.579869 829 | POGLUT1 -0.12595 2.27958 0.364715 0.58117 830 | C17orf89 0.431268 -1.77476 0.369695 0.585795 831 | MYH14 0.106112 3.777 0.370451 0.586491 832 | AMDHD2 0.171033 0.862422 0.374429 0.590232 833 | RNF152 0.125407 2.99468 0.38538 0.600259 834 | EIF4A3 0.144617 1.79259 0.392132 0.606292 835 | ZBTB21 0.340466 -0.112413 0.392951 0.607054 836 | LINC00707 0.154486 1.11787 0.392987 0.607091 837 | KDM7A 0.129173 1.62405 0.395675 0.609337 838 | DLC1 0.0642001 4.49428 0.396062 0.609646 839 | ACTR3 0.116745 2.38792 0.397507 0.610905 840 | CLDN12 0.109203 2.64899 0.405758 0.618356 841 | SERPINE1 -0.142725 1.37522 0.408652 0.620986 842 | SUPT6H 0.0978701 3.29129 0.409746 0.621953 843 | RPL7 0.149757 0.888761 0.410348 0.622416 844 | DUSP8 0.154693 2.75948 0.413988 0.625688 845 | PLEKHA2 0.108994 3.29977 0.41948 0.630328 846 | MYO15A -0.104253 3.92827 0.426976 0.636718 847 | PSMB5 0.388648 -1.4256 0.428581 0.638018 848 | SEC24A 0.138759 0.877197 0.430622 0.639837 849 | MRTO4 -0.151326 1.41153 0.432332 0.641236 850 | PRRC2A -0.139565 1.09692 0.438228 0.646149 851 | USP6 0.188453 0.710573 0.447482 0.654014 852 | GIMAP6 -0.119884 1.48909 0.447691 0.654233 853 | ADRA2B 0.161735 1.91319 0.448596 0.65496 854 | ZBTB11 0.183735 -0.200547 0.450581 0.656609 855 | APP 0.0687502 3.89962 0.452935 0.658611 856 | AGBL1 -0.118702 1.52459 0.454876 0.660266 857 | LBP 0.130041 1.34153 0.465527 0.669213 858 | ASH1L 0.102911 1.62705 0.467417 0.670841 859 | ACTG1 0.16834 0.444188 0.468596 0.671853 860 | TMEM147-AS1 -0.17989 0.436511 0.473073 0.675534 861 | RPL4P4 0.13929 0.71127 0.478236 0.679842 862 | AC109333.10 -0.14329 1.19248 0.491233 0.690154 863 | CES3 0.105747 2.54995 0.491328 0.690216 864 | PZP 0.0970536 2.27312 0.49436 0.69252 865 | ULK3 0.118992 1.46571 0.496226 0.693861 866 | SOGA1 0.10144 3.39533 0.504911 0.700571 867 | SYNJ2 0.0789812 3.85605 0.505851 0.701237 868 | RP11-864N7.2 0.210283 0.0171619 0.508219 0.703261 869 | TAOK1 0.108689 1.70688 0.510309 0.70476 870 | EPB41L4A-AS1 0.296818 -1.24428 0.510587 0.704946 871 | PAX8-AS1 0.0781142 3.39464 0.511414 0.705576 872 | C14orf79 -0.126792 2.73821 0.515113 0.708093 873 | PIK3IP1 0.187469 -0.0959251 0.523245 0.714157 874 | LRRC32 0.124354 2.74204 0.533007 0.72135 875 | DDX3YP3 -0.121648 1.32064 0.535639 0.723334 876 | MRC1 0.108575 1.34187 0.53647 0.723927 877 | EXOC3L1 -0.198222 0.0818666 0.544722 0.730195 878 | RP11-104F15.9 0.0993797 1.23784 0.547708 0.732381 879 | ADAM33 -0.0875511 2.89572 0.548664 0.732976 880 | SLC4A5 0.0541678 4.10798 0.555348 0.73753 881 | SOX17 -0.101603 1.75259 0.562912 0.742801 882 | PIK3C2A 0.101081 0.821133 0.566697 0.745624 883 | IBA57 0.105801 2.05157 0.569033 0.747472 884 | GATA2 -0.0925386 2.53029 0.57218 0.749635 885 | FES 0.10693 2.755 0.574527 0.751405 886 | TMEM81 0.104869 1.00817 0.58015 0.755593 887 | VSIG2 -0.0839703 1.28367 0.582757 0.757295 888 | EML4 0.0690951 2.57103 0.585513 0.759255 889 | CCDC85C 0.0777109 3.65347 0.592847 0.764553 890 | RP3-368A4.6 0.195634 -0.929053 0.600441 0.769929 891 | ZFAND2A -0.111818 0.646522 0.603675 0.772268 892 | FLNB -0.0514552 3.78747 0.61144 0.77762 893 | FSTL1 0.057397 3.57781 0.619447 0.783355 894 | TMEM175 -0.0884307 2.04918 0.620415 0.783976 895 | SEMA3F -0.0729097 2.45663 0.624898 0.787118 896 | GLULP4 -0.0802691 1.28853 0.628165 0.789643 897 | P2RX4 0.0595008 2.73438 0.630065 0.790962 898 | CHORDC1 0.0716103 1.49048 0.63536 0.794581 899 | GUSB 0.0530142 3.1104 0.640629 0.798044 900 | ARHGAP27 -0.0656794 3.91906 0.644817 0.800839 901 | TNXB 0.0602126 3.6045 0.645223 0.801027 902 | MMRN2 0.0787438 2.40884 0.646289 0.801755 903 | GPR98 0.0365979 4.68237 0.649675 0.803889 904 | GRASP 0.0826303 2.98415 0.652523 0.805914 905 | OSGEP 0.103577 0.393052 0.653174 0.806288 906 | UQCRHL 0.0763014 1.06631 0.663838 0.813687 907 | TRIM52-AS1 0.117009 -0.0392292 0.66541 0.81475 908 | CD83 0.0605326 1.96478 0.675523 0.821149 909 | ACAP3 -0.086505 2.01804 0.676203 0.821645 910 | NRARP 0.0970601 2.01777 0.676873 0.822036 911 | USHBP1 0.0782683 0.831743 0.678389 0.822984 912 | KITLG 0.0663212 2.10854 0.683078 0.825665 913 | ERMARD 0.0583138 2.09347 0.685638 0.827424 914 | TMEM116 0.0759719 1.14788 0.695526 0.833557 915 | APOE 0.0956727 0.883682 0.703242 0.838208 916 | SMO 0.055095 3.21043 0.704062 0.838699 917 | DUSP2 -0.0885026 0.197012 0.706506 0.840101 918 | IER5L 0.141631 -0.90532 0.709472 0.841913 919 | BOLA3 -0.093383 -0.102059 0.713872 0.844622 920 | NEIL1 -0.169799 -1.49356 0.715465 0.845526 921 | AC073109.2 -0.0717357 1.349 0.716387 0.846086 922 | RP11-434J24.2 -0.080501 0.166213 0.716498 0.846152 923 | EPHA2 0.0565029 2.38788 0.723296 0.850508 924 | RP11-1100L3.8 0.0849849 0.953924 0.727594 0.853205 925 | NAGPA -0.0789907 0.958676 0.727959 0.853421 926 | SLFN11 -0.0353416 3.26713 0.731614 0.855859 927 | ERF 0.0646402 1.55723 0.734125 0.857418 928 | MRPL27 0.0944278 -0.437901 0.735409 0.858208 929 | SLC20A1 0.04026 2.33679 0.736184 0.858684 930 | MEDAG 0.050124 1.85739 0.737179 0.859348 931 | TBC1D9 -0.0526458 2.29243 0.737306 0.859422 932 | RP11-270C12.3 0.0529663 1.28257 0.74215 0.862642 933 | SCARF2 -0.0657225 2.88723 0.743793 0.863525 934 | RPL35A -0.0605211 1.01099 0.747141 0.865625 935 | AP000361.2 -0.0828438 -0.00115111 0.750036 0.867323 936 | MTCO1P5 -0.0724554 0.562496 0.750215 0.867367 937 | GIMAP5 0.0351328 2.92266 0.75787 0.872132 938 | AKR1C1 0.127635 -0.937601 0.764614 0.876348 939 | NAB2 0.0625996 0.850249 0.764982 0.876641 940 | P2RX6 0.0467375 1.45552 0.776623 0.883474 941 | CYBRD1 0.0559574 2.22935 0.780274 0.885749 942 | TARBP2 -0.098848 -1.20099 0.785871 0.888859 943 | PLAUR 0.0422416 2.37223 0.787029 0.889668 944 | STARD9 0.0244214 3.5911 0.789987 0.891183 945 | GPR116 0.0266405 3.42997 0.791428 0.891994 946 | CTC-425O23.5 0.0577751 0.32393 0.795006 0.894113 947 | ICAM1 0.0451069 2.32282 0.795311 0.894196 948 | ZBTB8OS -0.0423693 1.71426 0.803952 0.899334 949 | RNA5SP286 -0.0437724 1.28095 0.807323 0.901152 950 | RP11-474J18.1 -0.0847623 -0.288486 0.808027 0.901547 951 | SNX29 0.0257018 3.2586 0.815741 0.905819 952 | SDCCAG3 0.0436313 1.11692 0.817507 0.906931 953 | TPT1P12 0.0452619 1.09532 0.820687 0.908705 954 | CX3CL1 0.0310821 2.53099 0.821967 0.909529 955 | BLVRB 0.0368436 1.12101 0.822433 0.909776 956 | RIPK2 -0.041301 1.19983 0.824601 0.910968 957 | ALDH8A1 0.0310539 2.43804 0.826958 0.912287 958 | TNFSF10 -0.115983 -1.79199 0.830185 0.913982 959 | CNTROB 0.025511 2.41154 0.843893 0.92157 960 | C1QC -0.0293452 1.5383 0.845277 0.92244 961 | PTPRB -0.0206143 3.24785 0.846168 0.922918 962 | SCN7A -0.0220561 3.07176 0.851385 0.925863 963 | ITIH5 0.0178467 3.87434 0.853683 0.927085 964 | PHPT1 0.0717874 -1.51349 0.855647 0.92794 965 | CPSF2 0.0257803 1.43646 0.856559 0.928407 966 | AC013437.7 -0.062307 -1.07547 0.859533 0.930103 967 | WDR53 -0.0565607 -0.630378 0.86094 0.930908 968 | PIN1 -0.042829 0.395681 0.861425 0.931155 969 | RPL37A 0.0251496 1.41518 0.862525 0.931802 970 | RP11-54O7.17 -0.0676531 -0.977243 0.862859 0.931931 971 | VPS11 0.0279258 1.84043 0.867363 0.934227 972 | RPL32 -0.0496614 -0.774577 0.870506 0.935812 973 | IK 0.0303998 1.0327 0.872294 0.93667 974 | NMD3P1 0.0294006 1.00814 0.873795 0.937469 975 | PLEKHG1 -0.0216242 2.56378 0.877907 0.939778 976 | TIMM9 0.0274114 0.697875 0.88595 0.944356 977 | DENND3 -0.0133727 4.36385 0.889575 0.946342 978 | THBS1 0.0253061 1.72959 0.893043 0.948086 979 | SV2B 0.0139924 3.2444 0.895704 0.949523 980 | LEMD3 0.0188613 2.07713 0.898578 0.950979 981 | POLR2A -0.0168973 2.7096 0.90005 0.95177 982 | SULT1A1 0.0144001 2.82996 0.916376 0.960427 983 | AMT 0.0223337 0.932603 0.91947 0.961798 984 | PRSS45 0.0159867 1.16795 0.926527 0.965078 985 | CRISPLD2 0.0112573 3.21401 0.92821 0.965965 986 | TTYH2 0.0117613 3.42044 0.9296 0.966826 987 | RP11-135F9.3 0.0164604 0.941441 0.934802 0.969242 988 | BTN3A2 -0.0124739 1.51485 0.935375 0.969446 989 | JMY -0.0150595 1.17437 0.940697 0.972194 990 | LENG8 0.0117216 1.58597 0.941506 0.972676 991 | HSPG2 -0.00743951 4.6593 0.94501 0.974434 992 | ZNF692 0.0113244 1.17537 0.957231 0.979648 993 | TNFRSF10B -0.00649059 2.14241 0.958325 0.980213 994 | HERC2 0.00520078 3.475 0.959532 0.980777 995 | ERBB3 -0.0058904 3.26749 0.961222 0.981474 996 | RP11-421F16.3 0.00892783 0.501466 0.964013 0.982708 997 | NPEPL1 -0.00522303 3.42696 0.972572 0.986911 998 | KB-431C1.5 0.00261235 1.1822 0.987774 0.994044 999 | MIEN1 -0.00395661 -0.315135 0.989304 0.994692 1000 | ACTG1P12 -0.000260293 -1.27214 0.998156 0.999021 1001 | RPS13 -0.000288763 -0.595819 0.999447 0.999755 1002 | -------------------------------------------------------------------------------- /inst/extdata/new.hgnc.table.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/markziemann/mitch/369a425727d65be5bfba6d7c0e487526e6423477/inst/extdata/new.hgnc.table.rds -------------------------------------------------------------------------------- /inst/mitch.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Mitch Report" 3 | author: "Antony Kaspi & Mark Ziemann" 4 | output: 5 | html_document: 6 | smart: false 7 | theme: cosmo 8 | --- 9 | 10 | date generated: `r Sys.Date()` 11 | 12 | ## Background 13 | 14 | Mitch performs unidimensional and multidimensional gene set enrichment analysis. The 15 | concept behind this dates to work by Cox and Mann 16 | (https://doi.org/10.1186/1471-2105-13-S16-S12). This implementation is suited to R based 17 | workflows of multi-omics datasets. This software was developed by Antony Kaspi and Mark 18 | Ziemann. Learn more about Mitch at the website: https://github.com/markziemann/Mitch 19 | 20 | ## Input profiles 21 | 22 | Here is the first few lines of the input profile. 23 | 24 | ```{r,checklibraries,echo=FALSE} 25 | 26 | suppressPackageStartupMessages({ 27 | library("mitch") 28 | library("gtools") 29 | }) 30 | 31 | ``` 32 | 33 | 34 | ```{r,peek,echo=FALSE} 35 | 36 | res <- readRDS(DATANAME) 37 | 38 | # capture the dimensionality of the data 39 | d=ncol(res$input_profile) 40 | 41 | # if working with >5 dimensions, then substitute the dimension (colnames) names with a number 42 | if ( d>5 ) { 43 | mydims<-data.frame(seq_len(d)) 44 | mydims$colnames<-attributes(res)$profile_dimensions 45 | colnames(mydims)<-c("dimension","contrast_name") 46 | print(kbl( mydims, caption = "Profile dimensions" ) %>% 47 | kable_styling("hover", full_width = FALSE)) 48 | colnames(res$input_profile)<- paste("d",seq_len(d),sep="") 49 | colnames(res$ranked_profile)<- paste("d",seq_len(d),sep="") 50 | ss<-res$ranked_profile 51 | } 52 | 53 | head(res$input_profile) %>% 54 | kbl(caption="The profiling data being passed to mitch") %>% 55 | kable_styling("hover", full_width = FALSE) 56 | 57 | ``` 58 | 59 | Here are some metrics about the input data profile: 60 | 61 | ```{r, metrics, echo=FALSE} 62 | 63 | if (d==1) { 64 | formatted<-t(as.data.frame(res$analysis_metrics[ c(1,2,3,4,5 ) ])) 65 | } else if (d==2) { 66 | unformatted<-t(as.data.frame(res$analysis_metrics[ c(2,3,4,5,11,12 ) ])) 67 | formatted<-unformatted 68 | formatted[1:4]<-as.character(round(as.numeric(unformatted[1:4]) , digits=0)) 69 | formatted[5:6]<-as.character(round(as.numeric(unformatted[5:6]) , digits=5)) 70 | } else if (d>2) { 71 | formatted<-t(as.data.frame(res$analysis_metrics[ c(2,3,4,5 ) ])) 72 | } 73 | 74 | colnames(formatted)="Profile metrics" 75 | 76 | formatted %>% 77 | kbl(caption="Profiling data metrics") %>% 78 | kable_styling("hover", full_width = FALSE) 79 | 80 | ``` 81 | 82 | Here is a plot of the input profiles. Note the dynamic ranges. 83 | 84 | ```{r, scatterplot, echo=FALSE,fig.height = 6, fig.width = 6.5, message=FALSE, warning=FALSE} 85 | 86 | if ( d==1 ) { 87 | par(mfrow=c(2,1)) 88 | hist(res$input_profile[,1],breaks=50,main="Distribution of DE scores",xlab=paste("DE score for ",colnames(res$input_profile))) 89 | plot(res$input_profile[,1],xlab=paste("DE score for ",colnames(res$input_profile)), 90 | pch="|",frame.plot=FALSE) 91 | UPS=length(which(res$input_profile[,1]>0)) 92 | DNS=length(which(res$input_profile[,1]<0)) 93 | TOTAL=nrow(res$input_profile) 94 | mtext(paste(TOTAL,"genes in total,",UPS,"trending up-regulated,",DNS,"trending down-regulated")) 95 | } else if ( d<3 ) { 96 | plot(res$input_profile, pch=19, col=rgb(red = 0, green = 0, blue = 0, alpha = 0.15), main="Input profiles" ) 97 | } else { 98 | ggpairs_points_plot <- function(data ,mapping, ...){ 99 | p <- ggplot(data = data, mapping = mapping) + 100 | geom_point(alpha=0.05) + 101 | geom_vline(xintercept=0,linetype="dashed") + 102 | geom_hline(yintercept=0,linetype="dashed") 103 | } 104 | p<-ggpairs(as.data.frame(res$input_profile), title="Scatterplot of all genes" , lower = list(continuous = ggpairs_points_plot )) 105 | print( p + theme_bw() ) 106 | } 107 | 108 | ``` 109 | 110 | Here is the contour plot of the profile including all detected genes. 111 | 112 | ```{r, contourplot, echo=FALSE,fig.height = 6, fig.width = 6.5,warning=FALSE, message=FALSE} 113 | 114 | palette <- colorRampPalette(c("white", "yellow","orange" ,"red","darkred","black")) 115 | 116 | #Contour of all the data 117 | ss<-res$ranked_profile 118 | 119 | if (d==1) { 120 | message("Contour plot does not apply to unidimensional analysis.") 121 | } else if (d==2) { 122 | xmin=min(ss[,1]) 123 | xmax=max(ss[,1]) 124 | ymin=min(ss[,2]) 125 | ymax=max(ss[,2]) 126 | ss<-res$ranked_profile 127 | k<-MASS:::kde2d(ss[,1],ss[,2]) 128 | X_AXIS=paste("Rank in contrast",colnames(ss)[1]) 129 | Y_AXIS=paste("Rank in contrast",colnames(ss)[2]) 130 | filled.contour(k, xlim=c(xmin,xmax),ylim=c(ymin,ymax), 131 | color=palette , 132 | plot.title={ abline(v=0,h=0,lty=2,lwd=2,col="blue") 133 | title( main="Rank-rank plot of all genes",xlab=X_AXIS,ylab=Y_AXIS ) } ) 134 | } else if (d>2) { 135 | #pairs contour plot function 136 | ggpairs_func <- function(data, mapping, ...){ 137 | p <- ggplot(data = data, mapping = mapping) + 138 | stat_density2d(aes(fill=..density..), geom="tile", contour = FALSE) + 139 | geom_vline(xintercept=0,linetype="dashed") + 140 | geom_hline(yintercept=0,linetype="dashed") + 141 | scale_fill_gradientn(colours=palette(25)) 142 | 143 | p 144 | 145 | } 146 | 147 | #pairs contour plot 148 | p<-ggpairs(as.data.frame(ss), title="Contour plot of all genes after ranking" , 149 | lower=list(continuous=ggpairs_func), 150 | diag=list(continuous=wrap("barDiag", binwidth=nrow(ss)/100))) 151 | 152 | print( p + theme_bw() ) 153 | 154 | #subset contour plot 155 | ggpairs_contour_limit_range <- function(data ,mapping, ...){ 156 | 157 | p <- ggplot(data = data, mapping = mapping) + 158 | stat_density2d(aes(fill=..density..), geom="tile", contour = FALSE) + 159 | geom_vline(xintercept=0,linetype="dashed") + 160 | geom_hline(yintercept=0,linetype="dashed") + 161 | scale_fill_gradientn(colours=palette(25)) + 162 | scale_x_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[1]))]),max(ss[,gsub("~","",as.character(mapping[1]))])) ) + 163 | scale_y_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[2]))]),max(ss[,gsub("~","",as.character(mapping[2]))])) ) 164 | 165 | p 166 | 167 | } 168 | 169 | #subset points plot 170 | ggpairs_points_limit_range <- function(data ,mapping, ...){ 171 | 172 | p <- ggplot(data = data, mapping = mapping) + 173 | geom_point(alpha=0.1) + 174 | geom_vline(xintercept=0,linetype="dashed") + 175 | geom_hline(yintercept=0,linetype="dashed") + 176 | scale_x_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[1]))]),max(ss[,gsub("~","",as.character(mapping[1]))])) ) + 177 | scale_y_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[2]))]),max(ss[,gsub("~","",as.character(mapping[2]))])) ) 178 | 179 | p 180 | 181 | } 182 | 183 | } 184 | 185 | ``` 186 | 187 | ## Input genesets 188 | 189 | Here are some metrics about the gene sets used: 190 | 191 | ```{r,input_geneset_metrics1,results="asis",echo=FALSE} 192 | 193 | ORIGINFILE=attributes(res$input_genesets)$originfile 194 | cat(paste("GMT file of genesets:",ORIGINFILE,"
")) 195 | unformatted<-t(as.data.frame(res$analysis_metrics[c(1,6,7)])) 196 | formatted<-as.data.frame(as.character( unformatted[1:3]) ) 197 | rownames(formatted)=rownames(unformatted) 198 | colnames(formatted)="Gene sets metrics" 199 | 200 | formatted %>% 201 | kbl(caption="Gene set metrics") %>% 202 | kable_styling("hover", full_width = F) 203 | 204 | ``` 205 | 206 | ```{r,input_geneset_metrics2,results="asis",echo=FALSE ,fig.height = 7, fig.width = 7 ,fig.show="all"} 207 | 208 | par(mfrow=c(3,1)) 209 | geneset_counts<-res$analysis_metrics$geneset_counts 210 | boxplot(geneset_counts$count,horizontal=TRUE,frame=FALSE,main="Gene set size",xlab="number of member genes included in profile") 211 | hist(geneset_counts$count,100,xlab="geneset size",main="Histogram of geneset size") 212 | hist(geneset_counts$count,100,xlim=c(0,500),xlab="geneset size",main="Trimmed histogram of geneset size") 213 | 214 | if ( d==2 ) { 215 | uu=length(which(res$input_profile[,1]>0 & res$input_profile[,2]>0)) 216 | ud=length(which(res$input_profile[,1]>0 & res$input_profile[,2]<0)) 217 | dd=length(which(res$input_profile[,1]<0 & res$input_profile[,2]<0)) 218 | du=length(which(res$input_profile[,1]<0 & res$input_profile[,2]>0)) 219 | a<-as.data.frame(c(uu,ud,dd,du)) 220 | rownames(a)=c("top-right","bottom-right","bottom-left","top-left") 221 | colnames(a)="a" 222 | par(mfrow=c(1,1)) 223 | xx<-barplot(a$a,names.arg=rownames(a),main="number of genes in each quadrant") 224 | text(x = xx, y = a$a, label = a$a , pos = 1, cex = 1) 225 | } else if (d>2) { 226 | if (d<6) { 227 | sig<-sign(ss) 228 | sector_count<-aggregate(seq_len(nrow(sig)) ~ ., sig, FUN = length) 229 | colnames(sector_count)[ncol(sector_count)]<-"Count" 230 | 231 | sector_count %>% 232 | kbl(caption="Genes by sector") %>% 233 | kable_styling("hover", full_width = FALSE) 234 | } 235 | 236 | } 237 | 238 | ``` 239 | 240 | ```{r,input_geneset_metrics3,results="asis",echo=FALSE,message=FALSE,fig.height = 7, fig.width = 7} 241 | 242 | if (d<2) { 243 | nsig=length(which(res$enrichment_result$p.adjustANOVA<0.05)) 244 | } else { 245 | nsig=length(which(res$enrichment_result$p.adjustMANOVA<0.05)) 246 | } 247 | 248 | if (d==1) { 249 | cat("

Differential pathway expression


") 250 | par(mfrow=c(1,1)) 251 | sig<-subset(res$enrichment_result,p.adjustANOVA<=0.05) 252 | 253 | plot(res$enrichment_result$s.dist,-log10(res$enrichment_result$pANOVA), 254 | xlab="s score",ylab="-log10(p-value)", 255 | main="volcano plot of gene set enrichments",pch=19,cex=0.8) 256 | 257 | points(sig$s.dist,-log10(sig$pANOVA),pch=19,cex=0.85,col="red") 258 | TOTAL=nrow(res$enrichment_result) 259 | SIG=nrow(sig) 260 | UP=length(which(sig$s.dist>0)) 261 | DN=length(which(sig$s.dist<0)) 262 | SUBHEADER=paste(TOTAL,"gene sets in total,",UP,"upregulated and ",DN,"downregulated (FDR<=0.05)") 263 | mtext(SUBHEADER) 264 | } 265 | 266 | if (d==2) { 267 | cat("

Gene sets by quadrant


") 268 | cat(paste("Number of significant gene sets (FDR<0.05)=", res$analysis_metrics$num_sets_significant,"
" )) 269 | a<-res$analysis_metrics[14] 270 | a<-as.data.frame(as.numeric(unlist(strsplit(as.character(a),','))),stringsAsFactors=FALSE) 271 | rownames(a)=c("top-right","bottom-right","bottom-left","top-left") 272 | colnames(a)="a" 273 | xx<-barplot(a$a,names.arg=rownames(a),main="number of genesets FDR<0.05") 274 | text(x = xx, y = a$a, label = a$a , pos = 1, cex = 1) 275 | } 276 | 277 | if ( nsig > 0 ) { 278 | if ( d>2 ) { 279 | if ( d<6 ) { 280 | cat(paste("Number of significant gene sets (FDR<0.05)=", res$analysis_metrics$num_sets_significant,"
" )) 281 | cat("

Gene sets by sector


") 282 | sig<-sign(res$enrichment_result[which(res$enrichment_result$p.adjustMANOVA<0.05),4:(4+d-1)]) 283 | sector_count<-aggregate(seq_len(nrow(sig)) ~ ., sig, FUN = length) 284 | colnames(sector_count)[ncol(sector_count)]<-"Count" 285 | 286 | sector_count %>% 287 | kbl(caption="Gene sets by sector") %>% 288 | kable_styling("hover", full_width = FALSE) 289 | } 290 | } 291 | } 292 | 293 | ``` 294 | 295 | ## Interactive enrichment scatterplot 296 | 297 | ```{r,echart1d,results="asis",echo=FALSE, fig.height = 7, fig.width = 7 ,fig.show="all", message=FALSE} 298 | 299 | if (d==1) { 300 | numsets=nrow(subset(res$enrichment_result,p.adjustANOVA<0.05)) 301 | p=NULL 302 | if (numsets==0){ 303 | message("No significant enrichments found.") 304 | } else { 305 | # volcano with echarts4r 306 | cat("Significance is calculated by -log10(p-value). All points shown are FDR<0.05.
") 307 | myres2<-subset(res$enrichment_result,p.adjustANOVA<0.05) 308 | myres2$significance<--log10(myres2$pANOVA) 309 | myres2$set<-sub(",","",myres2$set) 310 | XCOL=colnames(myres2)[4] 311 | YCOL=colnames(myres2)[6] 312 | colnames(myres2)[4]<-"xx" 313 | colnames(myres2)[6]<-"yy" 314 | 315 | p2<-myres2 %>% 316 | tibble::rownames_to_column("model") %>% 317 | dplyr::mutate(set = paste(set, setSize, p.adjustANOVA , sep = ",")) %>% 318 | e_charts(x = xx) %>% 319 | e_legend(show = FALSE) %>% 320 | e_x_axis(name = XCOL) %>% # add x axis name 321 | e_y_axis(name = YCOL) %>% # add y axis name 322 | e_scatter( serie = yy , bind = set , symbolSize = 10 ) %>% 323 | e_tooltip(formatter = htmlwidgets::JS(" 324 | function(params){ 325 | var vals = params.name.split(',') 326 | return('' + vals[0] + 327 | '
s.x-axis: ' + parseFloat(params.value[0]).toFixed(2) + 328 | '
s.y-axis: ' + parseFloat(params.value[1]).toFixed(2)) + 329 | '
setSize: ' + vals[1] + 330 | '
p-adjust ANOVA: ' + Number(vals[2]).toPrecision(2) 331 | }")) 332 | p2 333 | } 334 | } 335 | 336 | if (d==1) { 337 | resrows=length(res$detailed_sets) 338 | if (resrows>1) { 339 | myres2<-head(res$enrichment_result,resrows) 340 | myres2$significance<--log10(myres2$pANOVA) 341 | myres2$set<-sub(",","",myres2$set) 342 | XCOL=colnames(myres2)[4] 343 | YCOL=colnames(myres2)[6] 344 | colnames(myres2)[4]<-"xx" 345 | colnames(myres2)[6]<-"yy" 346 | 347 | p2<-myres2 %>% 348 | tibble::rownames_to_column("model") %>% 349 | mutate(set = paste(set, setSize, p.adjustANOVA , sep = ",")) %>% 350 | e_charts(x = xx) %>% 351 | e_legend(show = FALSE) %>% 352 | e_x_axis(name = XCOL) %>% # add x axis name 353 | e_y_axis(name = YCOL) %>% # add y axis name 354 | e_scatter( serie = yy , bind = set , symbolSize = 10 ) %>% 355 | e_tooltip(formatter = htmlwidgets::JS(" 356 | function(params){ 357 | var vals = params.name.split(',') 358 | return('' + vals[0] + 359 | '
s.x-axis: ' + parseFloat(params.value[0]).toFixed(2) + 360 | '
s.y-axis: ' + parseFloat(params.value[1]).toFixed(2)) + 361 | '
setSize: ' + vals[1] + 362 | '
p-adjust ANOVA: ' + Number(vals[2]).toPrecision(2) 363 | }")) 364 | 365 | cat("Significance is calculated by -log10(p-value). Top N sets shown irrespective of FDR.
") 366 | p2 367 | } 368 | } 369 | 370 | ```{r,echart2d,results="asis",echo=FALSE, fig.height = 7, fig.width = 7 ,fig.show="all", message=FALSE} 371 | 372 | echartscatter<-function(i){ 373 | my_x=plan[i,1] 374 | my_y=plan[i,2] 375 | XCOL=colnames(myres)[3+my_x] 376 | YCOL=colnames(myres)[3+my_y] 377 | myres$set<-sub(","," ",as.character(myres$set)) 378 | colnames(myres)[3+my_x]<-"xx" 379 | colnames(myres)[3+my_y]<-"yy" 380 | 381 | p2 <- myres %>% 382 | tibble::rownames_to_column("model") %>% 383 | mutate(set = paste(set, setSize, p.adjustMANOVA , s.dist , sep = ",")) %>% 384 | e_charts(x = xx , height = 300 ) %>% 385 | e_legend(show = FALSE) %>% 386 | e_x_axis(name = XCOL) %>% # add x axis name 387 | e_y_axis(name = YCOL) %>% # add y axis name 388 | e_scatter( serie = yy , bind = set , symbolSize = 10 ) %>% 389 | e_tooltip(formatter = htmlwidgets::JS(" 390 | function(params){ 391 | var vals = params.name.split(',') 392 | return('' + vals[0] + 393 | '
s.x-axis: ' + parseFloat(params.value[0]).toFixed(2) + 394 | '
s.y-axis: ' + parseFloat(params.value[1]).toFixed(2)) + 395 | '
setSize: ' + vals[1] + 396 | '
p-adjust MANOVA: ' + Number(vals[2]).toPrecision(2) + 397 | '
s.dist: ' + Number(vals[3]).toPrecision(2) 398 | }")) 399 | p2 400 | } 401 | 402 | if (d!=1) { 403 | cat("All sets with FDR<0.05. Try hovering over the points.
") 404 | 405 | numsets=nrow(subset(res$enrichment_result,p.adjustMANOVA<0.05)) 406 | 407 | p=NULL 408 | 409 | if (numsets<1){ 410 | 411 | message("No significant enrichments found.") 412 | p2<-list() 413 | 414 | } else { 415 | 416 | myres<-subset(res$enrichment_result,p.adjustMANOVA<0.05) 417 | 418 | if ( d<3 ) { 419 | 420 | myres2<-myres[,c(1,which(names(myres) %in% "p.adjustMANOVA"),2, 4:(4+d-1) , which(names(myres) %in% "s.dist") )] 421 | myres2$set<-sub(",","",myres2$set) 422 | XCOL=colnames(myres2)[4] 423 | YCOL=colnames(myres2)[5] 424 | colnames(myres2)[4]<-"xx" 425 | colnames(myres2)[5]<-"yy" 426 | 427 | p2 <- myres2 %>% 428 | tibble::rownames_to_column("model") %>% 429 | mutate(set = paste(set, setSize, p.adjustMANOVA , s.dist , sep = ",")) %>% 430 | e_charts(x = xx) %>% 431 | e_legend(show = FALSE) %>% 432 | e_x_axis(name = XCOL) %>% # add x axis name 433 | e_y_axis(name = YCOL) %>% # add y axis name 434 | e_scatter( serie = yy , bind = set , symbolSize = 10 ) %>% 435 | e_tooltip(formatter = htmlwidgets::JS(" 436 | function(params){ 437 | var vals = params.name.split(',') 438 | return('' + vals[0] + 439 | '
s.x-axis: ' + parseFloat(params.value[0]).toFixed(2) + 440 | '
s.y-axis: ' + parseFloat(params.value[1]).toFixed(2)) + 441 | '
setSize: ' + vals[1] + 442 | '
p-adjust MANOVA: ' + Number(vals[2]).toPrecision(2) + 443 | '
s.dist: ' + Number(vals[3]).toPrecision(2) 444 | }")) 445 | } else { 446 | plan<-combinations(n = d, r = 2, v = seq_len(d), repeats.allowed = FALSE) 447 | myres<-myres[,c(1, which(names(myres) %in% "p.adjustMANOVA") ,2,4:(4+d-1), 448 | which(names(myres) %in% "s.dist") ), which(names(myres) %in% "SD")] 449 | p2<-list() 450 | p2<-lapply( seq_len(nrow(plan)) , echartscatter) 451 | } 452 | htmltools::tagList(p2) 453 | } 454 | } 455 | 456 | if (d!=1) { 457 | cat("Top N sets irrespective of FDR. Try hovering over the points.
") 458 | resrows=length(res$detailed_sets) 459 | p=NULL 460 | 461 | if (resrows<1){ 462 | message("No results found.") 463 | } else { 464 | myres<-head(res$enrichment_result,resrows) 465 | 466 | if ( d<3 ) { 467 | myres2<-myres[,c(1,which(names(myres) %in% "p.adjustMANOVA"),2, 4:(4+d-1) , 468 | which(names(myres) %in% "s.dist") )] 469 | 470 | myres2$set<-sub(",","",myres2$set) 471 | XCOL=colnames(myres2)[4] 472 | YCOL=colnames(myres2)[5] 473 | colnames(myres2)[4]<-"xx" 474 | colnames(myres2)[5]<-"yy" 475 | 476 | p2<-myres2 %>% 477 | tibble::rownames_to_column("model") %>% 478 | mutate(set = paste(set, setSize, p.adjustMANOVA , s.dist , sep = ",")) %>% 479 | e_charts(x = xx) %>% 480 | e_legend(show = FALSE) %>% 481 | e_x_axis(name = XCOL) %>% # add x axis name 482 | e_y_axis(name = YCOL) %>% # add y axis name 483 | e_scatter( serie = yy , bind = set , symbolSize = 10 ) %>% 484 | e_tooltip(formatter = htmlwidgets::JS(" 485 | function(params){ 486 | var vals = params.name.split(',') 487 | return('' + vals[0] + 488 | '
s.x-axis: ' + parseFloat(params.value[0]).toFixed(2) + 489 | '
s.y-axis: ' + parseFloat(params.value[1]).toFixed(2)) + 490 | '
setSize: ' + vals[1] + 491 | '
p-adjust MANOVA: ' + Number(vals[2]).toPrecision(2) + 492 | '
s.dist: ' + Number(vals[3]).toPrecision(2) 493 | }")) 494 | } else { 495 | plan<-combinations(n = d, r = 2, v = seq_len(d), repeats.allowed = FALSE) 496 | 497 | myres<-myres[,c(1, which(names(myres) %in% "p.adjustMANOVA") ,2,4:(4+d-1), 498 | which(names(myres) %in% "s.dist") ), which(names(myres) %in% "SD")] 499 | 500 | p<-list() 501 | lapply( seq_len(nrow(plan)) , echartscatter) 502 | } 503 | htmltools::tagList(p) 504 | htmltools::tagList(p2) 505 | } 506 | } 507 | 508 | ``` 509 | 510 | ```{r,heatmap,results="asis",echo=FALSE, fig.height = 10, fig.width = 7 ,fig.show="all", message=FALSE} 511 | 512 | if (d!=1) { 513 | if (resrows>2) { 514 | cat("

A heatmap of S values for top results


") 515 | hmapx<-head( res$enrichment_result[,4:(4+d-1)] ,resrows) 516 | rownames(hmapx)<-head(res$enrichment_result$set,resrows) 517 | colnames(hmapx)<-gsub("^s.","",colnames(hmapx)) 518 | my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25) 519 | 520 | if(resrows<25){ 521 | CEXROW=1 522 | } else if (resrows<51) { 523 | CEXROW=0.8 524 | } else if (resrows<76) { 525 | CEXROW=0.6 526 | } else if (resrows<101) { 527 | CEXROW=0.4 528 | } else { 529 | CEXROW=0.25 530 | } 531 | heatmap.2(as.matrix(hmapx),scale="none",margin=c(10, 25),cexRow=CEXROW,trace="none",cexCol=1,col=my_palette) 532 | } 533 | } 534 | 535 | ``` 536 | 537 | ```{r,effectsize,results="asis",echo=FALSE, fig.height = 7, fig.width = 7 ,fig.show="all", message=FALSE} 538 | 539 | if (d!=1) { 540 | cat('

A plot of effect size versus significance


') 541 | cat('Significance is the -log2(p.adjustMANOVA) and effect size is the s.dist which is the hypotenuse of the s scores.
') 542 | colnames(myres)<-gsub("\\.","_",colnames(myres)) 543 | myres<-res$enrichment_result 544 | myres$significance<--log2(myres$p.adjustMANOVA) 545 | myres$set<-sub(",","",myres$set) 546 | XCOL=colnames(myres)[ncol(myres)-3] 547 | YCOL=colnames(myres)[ncol(myres)] 548 | colnames(myres)[ncol(myres)-3]<-"xx" 549 | colnames(myres)[ncol(myres)]<-"yy" 550 | 551 | p2<-myres %>% 552 | tibble::rownames_to_column("model") %>% 553 | mutate(set = paste( set , setSize , p.adjustMANOVA , sep = ",")) %>% 554 | e_charts( x = xx) %>% 555 | e_legend(show = FALSE) %>% 556 | e_x_axis(name = XCOL) %>% # add x axis name 557 | e_y_axis(name = YCOL) %>% # add y axis name 558 | e_scatter( serie = yy , bind = set , symbolSize = 10 ) %>% 559 | e_tooltip(formatter = htmlwidgets::JS(" 560 | function(params){ 561 | var vals = params.name.split(',') 562 | return('' + vals[0] + 563 | '
s.x-axis: ' + parseFloat(params.value[0]).toFixed(2) + 564 | '
s.y-axis: ' + parseFloat(params.value[1]).toFixed(2)) + 565 | '
setSize: ' + vals[1] + 566 | '
p-adjust MANOVA: ' + Number(vals[2]).toPrecision(2) 567 | }")) 568 | p2 569 | } 570 | 571 | ``` 572 | 573 | ## Results table 574 | 575 | ```{r,results_table,results="asis",echo=FALSE} 576 | 577 | if ( d==1 ) { 578 | resrows=length(res$detailed_sets) 579 | myres<-head(res$enrichment_result,resrows) 580 | myres[,c(3:ncol(myres))]<-signif(myres[,c(3:ncol(myres))],3) 581 | } else if (d>1) { 582 | resrows=length(res$detailed_sets) 583 | myres<-head(res$enrichment_result,resrows) 584 | myres<-myres[,c(1:3, which(names(myres) %in% "p.adjustMANOVA"), which(names(myres) %in% "s.dist") ,4:((d*2)+3)), which(names(myres) %in% "SD")] 585 | myres[,3:ncol(myres)]<-signif(myres[,3:ncol(myres)],3) 586 | } 587 | 588 | formatted<-myres 589 | formatted[,1]<-gsub("_"," ",myres[,1]) 590 | 591 | kbl( formatted , format="markdown", row.names=FALSE, caption = cat(paste("Top N=",resrows,"gene sets")) ,digits=100) %>% 592 | kable_styling("hover", full_width = FALSE) 593 | 594 | cat("

") 595 | 596 | ``` 597 | 598 | ## Results (complete table) 599 | 600 | ```{r,results_table_complete,results="asis",echo=FALSE} 601 | 602 | myres<-res$enrichment_result 603 | 604 | if ( d==1 ) { 605 | myres[,c(3:ncol(myres))]<-signif(myres[,c(3:ncol(myres))],3) 606 | formatted<-myres 607 | formatted[,1]<-gsub("_"," ",myres[,1]) 608 | } else if (d>1) { 609 | myres<-myres[,c(1:3, which(names(myres) %in% "p.adjustMANOVA"), which(names(myres) %in% "s.dist") ,4:((d*2)+3)), which(names(myres) %in% "SD")] 610 | myres[,3:ncol(myres)]<-signif(myres[,3:ncol(myres)],3) 611 | formatted<-myres 612 | formatted[,1]<-gsub("_"," ",myres[,1]) 613 | formatted[,3:ncol(myres)]<-format(myres[,3:ncol(myres)],digits=3) 614 | } 615 | 616 | HEADER=paste("
","Click HERE to show results for all gene sets","

",sep=" " ) 617 | 618 | cat(HEADER) 619 | 620 | kbl(formatted, format="html", row.names=FALSE, caption = "Complete results",digits=100) %>% 621 | kable_styling("hover", full_width = FALSE) 622 | 623 | cat("

") 624 | cat("

") 625 | 626 | ``` 627 | 628 | ```{r,detailed_geneset_reports1d,results="asis",echo=FALSE,fig.height=6, fig.width=6 ,out.width = '80%',comment=NA, message=FALSE} 629 | 630 | if (d==1) { 631 | cat("

Detailed Gene set reports


") 632 | cat('
') 633 | cat('\n') 634 | ss<-res$ranked_profile 635 | 636 | for ( i in seq_len(resrows) ) { 637 | mydat = NULL 638 | mydat<-t(myres[i,]) 639 | 640 | cat(paste("",as.character(myres[i,1]) ,"
")) 641 | 642 | tbl <- kbl(mydat,col.names = NULL,format='pipe',caption=NULL ) %>% 643 | kable_styling("hover", full_width = FALSE) 644 | 645 | cat(tbl) 646 | cat('\n\n\n\n') 647 | cat("
") 648 | 649 | # plots 650 | sss<-res$detailed_sets[[i]] 651 | set<-names(res$detailed_sets[i]) 652 | size<-length(sss) 653 | par(mfrow=c(3,1)) 654 | 655 | beeswarm(sss,vertical = FALSE,cex=0.75,xlim=c(min(ss), 656 | max(ss)),col="darkgray",pch=19,main=set,cex.main=1.5, 657 | xlab=paste("ranked DE score in:",colnames(ss))) 658 | 659 | mtext("beeswarm plot",cex=0.8) 660 | 661 | hist(sss,xlim=c(min(ss),max(ss)),breaks=15,col="darkgray",main=NULL, 662 | border="black",xlab=paste("ranked DE score in:",colnames(ss))) 663 | 664 | mtext("histogram",cex=0.8) 665 | 666 | plot(sss,rep(1,length(sss)),type="n",xlim=c(min(ss),max(ss)), 667 | frame=FALSE,axes=FALSE,ylab="", 668 | xlab=paste("ranked DE score in:",colnames(ss))) 669 | 670 | rug(sss, ticksize = 0.9) 671 | axis(1) 672 | mtext("rugplot",cex=0.8) 673 | cat("
") 674 | 675 | # top gene list 676 | setSign=sign(res$enrichment_result[i,4]) 677 | 678 | if (setSign==-1) { 679 | tops<-as.data.frame(res$detailed_sets[[i]][order(res$detailed_sets[[i]])]) 680 | } else if (setSign==1) { 681 | tops<-as.data.frame(res$detailed_sets[[i]][order(-res$detailed_sets[[i]])]) 682 | } 683 | 684 | cat("Top enriched genes\\n") 685 | tops$GeneID<-rownames(tops) 686 | tops<-tops[,c(2,1)] 687 | colnames(tops)=c("GeneID","Gene Rank") 688 | cat("
") 689 | 690 | tbl <- kbl(head(tops,n=20L), format='pipe', row.names=FALSE,caption="Top 20 genes",digits=100) %>% 691 | kable_styling("hover", full_width = FALSE) 692 | 693 | cat(tbl) 694 | cat('\n\n\n\n') 695 | HEADER=paste("
","Click HERE to show all gene set members","

",sep=" " ) 696 | cat(HEADER) 697 | 698 | tbl <- kbl(tops, format='pipe', row.names=FALSE, caption="All member genes",digits=100) %>% 699 | kable_styling("hover", full_width = FALSE) 700 | 701 | cat(tbl) 702 | cat('\n\n\n\n') 703 | cat("

") 704 | cat("
") 705 | } 706 | } 707 | 708 | ``` 709 | 710 | ```{r,detailed_geneset_reports2d,results="asis",echo=FALSE,fig.height=5, fig.width=6 ,out.width = '80%',comment=NA, message=FALSE} 711 | 712 | twodimplot<-function(i) { 713 | ll<-res$enrichment_result[i,] 714 | size<-ll$setSize 715 | sss<-res$detailed_sets[[i]] 716 | k<-MASS:::kde2d(sss[,1],sss[,2]) 717 | filled.contour( k, color = palette, xlim=c(xmin,xmax),ylim=c(ymin,ymax), 718 | plot.title={ abline(v=0,h=0,lty=2,lwd=2,col="blue") 719 | title( main=ll$set , xlab=X_AXIS,ylab=Y_AXIS )}) 720 | 721 | plot(sss, pch=19, col=rgb(red = 0, green = 0, blue = 0, alpha = 0.2), 722 | main=ll$set , xlim=c(xmin,xmax),ylim=c(ymin,ymax), 723 | xlab=X_AXIS,ylab=Y_AXIS) 724 | 725 | abline(v=0,h=0,lty=2,lwd=2,col="blue") 726 | sss_long<-melt(sss) 727 | colnames(sss_long) <- c("gene","contrast","value") 728 | colnames(ss_long) <- c("gene","contrast","value") 729 | 730 | p<-ggplot(ss_long,aes(contrast,value)) + 731 | geom_violin(data=ss_long,fill = "grey", colour = "grey") + 732 | geom_boxplot(data=ss_long,width=0.9,fill="grey",outlier.shape = NA) + 733 | geom_violin(data=sss_long,fill = "black", colour = "black") + 734 | geom_boxplot(data=sss_long,width=0.1,outlier.shape = NA) + 735 | labs(y = "Position in rank",title = ll[,1] ) 736 | 737 | print( p + theme_bw() + 738 | theme( axis.text=element_text(size=14), 739 | axis.title=element_text(size=15), 740 | plot.title = element_text(size = 14))) 741 | } 742 | 743 | 744 | # subset contour plot 745 | ggpairs_contour_limit_range <- function(data ,mapping, ...){ 746 | p <- ggplot(data = data, mapping = mapping) + 747 | stat_density2d(aes(fill=..density..), geom="tile", contour = FALSE) + 748 | geom_vline(xintercept=0,linetype="dashed") + 749 | geom_hline(yintercept=0,linetype="dashed") + 750 | scale_fill_gradientn(colours=palette(25)) + 751 | scale_x_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[1]))]),max(ss[,gsub("~","",as.character(mapping[1]))])) ) + 752 | scale_y_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[2]))]),max(ss[,gsub("~","",as.character(mapping[2]))])) ) 753 | p 754 | } 755 | 756 | 757 | # subset points plot 758 | ggpairs_points_limit_range <- function(data ,mapping, ...){ 759 | p <- ggplot(data = data, mapping = mapping) + 760 | geom_point(alpha=0.1) + 761 | geom_vline(xintercept=0,linetype="dashed") + 762 | geom_hline(yintercept=0,linetype="dashed") + 763 | scale_x_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[1]))]),max(ss[,gsub("~","",as.character(mapping[1]))])) ) + 764 | scale_y_continuous( limits = range(min(ss[,gsub("~","",as.character(mapping[2]))]),max(ss[,gsub("~","",as.character(mapping[2]))])) ) 765 | p 766 | } 767 | 768 | 769 | ndplot<-function(i) { 770 | ll<-res$enrichment_result[i,] 771 | size<-ll$setSize 772 | ss<-res$ranked_profile 773 | sss<-res$detailed_sets[[i]] 774 | 775 | if ( d>5 ) { 776 | colnames(sss)<- paste("d",seq_len(d),sep="") 777 | } 778 | 779 | empty_cnt <- length(which(is.na(cor(sss,use="pairwise.complete.obs")))) 780 | if ( empty_cnt == 0 ) { 781 | 782 | p<-ggpairs(as.data.frame(sss), title=ll[,1], lower=list(continuous=ggpairs_contour_limit_range), 783 | diag=list(continuous=wrap("barDiag", binwidth=nrow(ss)/10)) ) 784 | 785 | print( p + theme_bw() ) 786 | 787 | p<-ggpairs(as.data.frame(sss), title=ll[,1], lower= list(continuous = ggpairs_points_limit_range ), 788 | diag=list(continuous=wrap("barDiag", binwidth=nrow(ss)/10))) 789 | 790 | print( p + theme_bw() ) 791 | } 792 | 793 | sss_long<-melt(sss) 794 | colnames(sss_long) <- c("gene","contrast","value") 795 | colnames(ss_long) <- c("gene","contrast","value") 796 | 797 | empty_cnt <- apply(sss,2,function(x) sum(as.numeric(is.finite(x)))) 798 | empty_cnt <- length(which(empty_cnt==0)) 799 | if ( empty_cnt == 0 ) { 800 | 801 | p<-ggplot(ss_long,aes(contrast,value)) + 802 | geom_violin(data=ss_long,fill = "grey", colour = "grey") + 803 | geom_boxplot(data=ss_long,width=0.9,fill="grey",outlier.shape = NA) + 804 | geom_violin(data=sss_long,fill = "black", colour = "black") + 805 | geom_boxplot(data=sss_long,width=0.1,outlier.shape = NA) + 806 | labs(y = "Position in rank",title = ll[,1] ) 807 | 808 | print( p + theme_bw() + 809 | theme( axis.text=element_text(size=14), 810 | axis.title=element_text(size=15), 811 | plot.title = element_text(size = 14))) 812 | } 813 | } 814 | 815 | 816 | topgenelist<-function(i) { 817 | ss<-res$ranked_profile 818 | sss<-res$detailed_sets[[i]] 819 | 820 | if ( d>5 ) { 821 | colnames(sss)<- paste("d",seq_len(d),sep="") 822 | } 823 | 824 | tl=bl=tr=br=0 825 | myres<-head(res$enrichment_result,resrows) 826 | myres<-myres[,c(1:3, which(names(myres) %in% "p.adjustMANOVA"), which(names(myres) %in% "s.dist") ,4:((d*2)+3)) , which(names(myres) %in% "s.dist")] 827 | myres[,3:ncol(myres)]<-signif(myres[,3:ncol(myres)],3) 828 | 829 | #select 2 strongest columns to highlight genes 830 | if( d>2 ) { 831 | cols<-(ncol(myres)-(2*d)+1):(ncol(myres)-(2*d)+d) 832 | COLS<-order(-abs(unlist((myres[i,cols]))))[1:2] 833 | sss<-sss[,COLS] 834 | cols<-cols[COLS] 835 | mysx=signif(myres[i,cols[1]],3) 836 | mysy=signif(myres[i,cols[2]],3) 837 | } else { 838 | cols=c(ncol(myres)-3,ncol(myres)-2) 839 | mysx=signif(myres[i,cols[1]],3) 840 | mysy=signif(myres[i,cols[2]],3) 841 | } 842 | 843 | if ( mysy>0 ) { tl=tl+1 ; tr=tr+1 } else { bl=bl+1 ; br=br+1 } 844 | if ( mysx>0 ) { tr=tr+1 ; br=br+1 } else { tl=tl+1 ; bl=bl+1 } 845 | 846 | if (bl==2) { 847 | myquad<-sss[which(sss[,1]<0 & sss[,2]<0),,drop=FALSE] 848 | topgenes<-myquad[order(-rank(myquad[,1]*myquad[,2])),,drop=FALSE] 849 | } 850 | 851 | if (tr==2) { 852 | myquad<-sss[which(sss[,1]>0 & sss[,2]>0),,drop=FALSE] 853 | topgenes<-myquad[order(-rank(myquad[,1]*myquad[,2])),,drop=FALSE] 854 | } 855 | 856 | if (br==2) { 857 | myquad<-sss[which(sss[,1]>0 & sss[,2]<0),,drop=FALSE] 858 | topgenes<-myquad[order(rank(myquad[,1]*myquad[,2])),,drop=FALSE] 859 | } 860 | 861 | if (tl==2) { 862 | myquad<-sss[which(sss[,1]<0 & sss[,2]>0),,drop=FALSE] 863 | topgenes<-myquad[order(rank(myquad[,1]*myquad[,2])),,drop=FALSE] 864 | } 865 | 866 | cat("
") 867 | topgenes<-as.data.frame(topgenes) 868 | topgenes$Gene<-as.character(rownames(topgenes)) 869 | topgenes<-topgenes[,c(3,1,2)] 870 | 871 | tbl <- kbl(head(topgenes,n=20L), format='pipe', row.names=FALSE, caption="Top 20 genes", digits=100) %>% 872 | kable_styling("hover", full_width = FALSE) 873 | 874 | cat(tbl) 875 | cat('\n\n\n\n') 876 | HEADER=paste("
","Click HERE to show all gene set members","

",sep=" " ) 877 | cat(HEADER) 878 | sss<-res$detailed_sets[[i]] 879 | 880 | tbl <- kbl(sss, format='pipe', row.names=TRUE, caption="All member genes", digits=100) %>% 881 | kable_styling("hover", full_width = FALSE) 882 | 883 | cat(tbl) 884 | cat('\n\n\n\n') 885 | cat("

") 886 | cat("
") 887 | } 888 | 889 | 890 | #functions end here 891 | 892 | if (d!=1) { 893 | cat("

Detailed Gene set reports


") 894 | resrows=length(res$detailed_sets) 895 | ss<-res$ranked_profile 896 | myres<-head(res$enrichment_result,resrows) 897 | myres<-myres[,c(1:3, which(names(myres) %in% "p.adjustMANOVA"), which(names(myres) %in% "s.dist") ,4:((d*2)+3)), which(names(myres) %in% "SD")] 898 | myres[,3:ncol(myres)]<-signif(myres[,3:ncol(myres)],3) 899 | palette <- colorRampPalette(c("white", "yellow","orange" ,"red","darkred","black")) 900 | ss_long<-melt(ss) 901 | colnames(ss_long) <- c("gene","contrast","value") 902 | 903 | if ( d<3 ) { 904 | xmin=min(ss[,1]) 905 | xmax=max(ss[,1]) 906 | ymin=min(ss[,2]) 907 | ymax=max(ss[,2]) 908 | 909 | for ( i in seq_len(resrows )) { 910 | mydat = NULL 911 | mydat$metrics<-names(myres[2:ncol(myres)]) 912 | mydat$values<-unname(t(myres[i,2:ncol(myres)])) 913 | mydat<-as.data.frame(cbind(mydat$metrics,mydat$values)) 914 | colnames(mydat)<-c("metric","value") 915 | cat(paste("",as.character(myres[i,1]) ,"
")) 916 | 917 | tbl <- kbl(mydat,format='pipe', caption=as.character(t(myres[i,1]))) %>% 918 | kable_styling("hover", full_width = FALSE) 919 | 920 | cat(tbl) 921 | cat('\n\n\n\n') 922 | cat("
") 923 | twodimplot(i) 924 | cat("
") 925 | topgenelist(i) 926 | cat("

") 927 | } 928 | } else { 929 | 930 | for ( i in seq_len(resrows )) { 931 | mydat = NULL 932 | mydat$metrics<-names(myres[2:ncol(myres)]) 933 | mydat$values<-unname(t(myres[i,2:ncol(myres)])) 934 | mydat<-as.data.frame(cbind(mydat$metrics,mydat$values)) 935 | colnames(mydat)<-c("metric","value") 936 | cat(paste("",as.character(myres[i,1]) ,"
")) 937 | 938 | tbl <- kbl(mydat,format='pipe',digits=5,caption=as.character(t(myres[i,1]))) %>% 939 | kable_styling("hover", full_width = FALSE) 940 | 941 | cat(tbl) 942 | cat('\n\n\n\n') 943 | cat("
") 944 | ndplot(i) 945 | cat("
") 946 | topgenelist(i) 947 | cat("

") 948 | } 949 | } 950 | } 951 | 952 | ``` 953 | 954 | ## Network diagram 955 | 956 | Only used for one-dimensional analysis. 957 | 958 | Here, the network diagram is used to depict the similarity between some of the top ranked gene sets. 959 | It makes separate charts for up and downregulated sets. 960 | It works best when prioritisation is done by effect size during the `mitch_calc()` step. 961 | By default, we only show the top 20 genes, but you can use the `networkplot()` command yourself 962 | with other options. 963 | See `?networkplot` for more detail. 964 | There is an element of stochasticity with regard to the network projection, so if you see a lot of 965 | overlapping labels or labels getting cut off, you could repeat the chart generation until you get a 966 | nice layout. 967 | See `?networkplot` for more detail. 968 | 969 | Below the network diagrams, you will see lists of genes that make up the up and downregulated sets 970 | respectively. 971 | For upregulated genes the score needs to be >2 and for downregulated genes it needs to be < -2. 972 | This is to remove genes that have uninteresting differential expression and do not contribute 973 | enrichment. 974 | 975 | ```{r,network,fig.height=6, fig.width=14} 976 | 977 | if (d==1) { 978 | networkplot(eres=res,FDR=0.05,n_sets=20) 979 | network_genes(eres=res,FDR=0.05,n_sets=20) 980 | } else { 981 | message("Network charts only generated in one-dimensional analysis.") 982 | } 983 | 984 | ``` 985 | 986 | ## Session information 987 | 988 | Here is the session info with all the versions of packages used. 989 | 990 | ```{r session_info, include=TRUE, echo=TRUE, results='markup'} 991 | 992 | sessionInfo() 993 | 994 | ``` 995 | 996 | END of report 997 | -------------------------------------------------------------------------------- /inst/script/README.txt: -------------------------------------------------------------------------------- 1 | In the inst/extdata/ folder there are some "raw" data files which are used to 2 | demonstrate mitch functions in the examples. The instructions used to create 3 | these data are in the inst/script/example_datagen.R script. 4 | 5 | These are the files: 6 | . 7 | ├── k36a.tsv 8 | ├── k9a.tsv 9 | ├── rna.tsv 10 | └── sample_genesets.gmt 11 | 12 | These three tables are edgeR differential expression/binding tables comparing 13 | samples before and after 60 minutes stimulation. The 'rna.tsv' file is for 14 | gene expression measured by RNA-seq. The 'k9a.tsv' file represents histone 3 15 | lysine 9 acetylation responses measured with ChIP-seq. The 'k36a.tsv' file 16 | represents histone 3 lysine 36 acetylation responses measured with ChIP-seq. 17 | These files represent only 1000 genes to keep file sizes small for the 18 | purposes of the examples. The full files are to be found in another repo: 19 | https://github.com/markziemann/mitch_paper/tree/master/data-raw 20 | -------------------------------------------------------------------------------- /inst/script/example_datagen.R: -------------------------------------------------------------------------------- 1 | #install.packages("../..",repos = NULL, type="source") 2 | library(mitch) 3 | 4 | DATAURL=paste("https://raw.githubusercontent.com/", 5 | "markziemann/mitch_paper/master/data-raw/",sep="") 6 | RNAURL=paste(DATAURL,"rna.tsv",sep="") 7 | rna <- read.table(RNAURL, header=TRUE) 8 | K9AURL=paste(DATAURL,"k9a.tsv",sep="") 9 | k9a <- read.table(K9AURL, header = TRUE) 10 | K36AURL=paste(DATAURL,"k36a.tsv",sep="") 11 | k36a <- read.table(K36AURL, header = TRUE) 12 | myList <- list(rna = rna, k9a = k9a, k36a = k36a) 13 | 14 | myImportedData <- mitch_import(myList, "edger") 15 | myImportedData <- head(myImportedData, 1000) 16 | 17 | rna <- rna[rownames(rna) %in% rownames(myImportedData), ] 18 | k9a <- k9a[rownames(k9a) %in% rownames(myImportedData), ] 19 | k36a <- k36a[rownames(k36a) %in% rownames(myImportedData), ] 20 | 21 | myList <- list(rna = rna, k9a = k9a, k36a = k36a) 22 | 23 | REACTOMEURL=paste(DATAURL,"ReactomePathways.gmt",sep="") 24 | download.file(REACTOMEURL,destfile="ReactomePathways.gmt") 25 | genesetsExample <- head(gmt_import(REACTOMEURL), 200) 26 | reactome<-readLines("ReactomePathways.gmt") 27 | reactome<-head(reactome,200) 28 | writeLines(reactome,"../../inst/extdata/sample_genesets.gmt") 29 | 30 | resExample<-mitch_calc(myImportedData,genesetsExample, 31 | resrows=5,priority="significance") 32 | 33 | save(genesetsExample,file="../../data/genesetsExample.RData",compress="xz") 34 | save(k36a, file = "../../data/k36a.RData", compress = "xz") 35 | save(k9a, file = "../../data/k9a.RData", compress = "xz") 36 | save(rna, file = "../../data/rna.RData", compress = "xz") 37 | save(myList, file = "../../data/myList.RData", compress = "xz") 38 | save(myImportedData, file = "../../data/myImportedData.RData",compress="xz") 39 | save(resExample, file = "../../data/resExample.RData", compress = "xz") 40 | 41 | write.table(rna, file = "../../inst/extdata/rna.tsv", 42 | sep = "\t", quote = FALSE) 43 | write.table(k9a, file = "../../inst/extdata/k9a.tsv", 44 | sep = "\t", quote = FALSE) 45 | write.table(k36a, file = "../../inst/extdata/k36a.tsv", 46 | sep = "\t", quote = FALSE) 47 | 48 | message("1d") 49 | myImportedData <- mitch_import(rna, "edger" ) 50 | resExample <- mitch_calc(myImportedData, genesetsExample, 51 | resrows = 5, priority = "significance") 52 | nrow(resExample$enrichment_result) 53 | 54 | message("2d") 55 | myList <- list(rna = rna, k9a = k9a) 56 | myImportedData <- mitch_import(myList, "edger" ) 57 | resExample <- mitch_calc(myImportedData, genesetsExample, 58 | resrows = 5, priority = "significance") 59 | nrow(resExample$enrichment_result) 60 | 61 | message("3d") 62 | myList <- list(rna = rna, k9a = k9a, k36a = k36a) 63 | myImportedData <- mitch_import(myList, "edger" ) 64 | resExample <- mitch_calc(myImportedData, genesetsExample, 65 | resrows = 5, priority = "significance") 66 | nrow(resExample$enrichment_result) 67 | 68 | unlink("ReactomePathways.gmt") 69 | 70 | -------------------------------------------------------------------------------- /man/genesetsExample.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{data} 4 | \name{genesetsExample} 5 | \alias{genesetsExample} 6 | \title{Reactome gene sets} 7 | \format{ 8 | A list of gene sets 9 | } 10 | \source{ 11 | Reactome website: https://reactome.org/ 12 | } 13 | \usage{ 14 | data(genesetsExample) 15 | } 16 | \description{ 17 | Genesets from Reactome database suitable for enrichment analysis. 18 | Acquired August 2019. The structure of this data is a named list of 19 | vectors, containing human gene names (character strings). This is 20 | a sample of 200 gene sets from the approximately 2000 present in the 21 | full dataset. 22 | } 23 | \examples{ 24 | data(genesetsExample) 25 | } 26 | \references{ 27 | Fabregat et al. (2017) BMC Bioinformatics volume 18, 28 | Article number: 142, https://www.ncbi.nlm.nih.gov/pubmed/28249561 29 | } 30 | \keyword{datasets} 31 | -------------------------------------------------------------------------------- /man/gmt_import.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \name{gmt_import} 4 | \alias{gmt_import} 5 | \title{gmt_import} 6 | \usage{ 7 | gmt_import(gmtfile) 8 | } 9 | \arguments{ 10 | \item{gmtfile}{a gmt file.} 11 | } 12 | \value{ 13 | a list of gene sets. 14 | } 15 | \description{ 16 | This function imports GMT files into a list of character vectors for mitch 17 | analysis. GMT files are a commonly used 18 | format for lists of genes used in pathway enrichment analysis. GMT files can 19 | be obtained from Reactome, MSigDB, etc. 20 | } 21 | \examples{ 22 | # Import some gene sets 23 | genesetsExample<-gmt_import(system.file('extdata/sample_genesets.gmt', 24 | package = 'mitch')) 25 | } 26 | \keyword{genesets} 27 | \keyword{import} 28 | -------------------------------------------------------------------------------- /man/k36a.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{data} 4 | \name{k36a} 5 | \alias{k36a} 6 | \title{H3K36ac profile} 7 | \format{ 8 | data frame 9 | } 10 | \usage{ 11 | data(k36a) 12 | } 13 | \description{ 14 | Example edgeR result of differential ChIP-seq H3K36ac. 15 | This is a dataframe which contains columns for log fold change, log counts 16 | per million, p-value and FDR adjusted p-value. These columns consist of 17 | numerical values. The row names represent human gene names. This is a sample 18 | of 1000 gene of an original dataset that contains measurements of ~30000 19 | genes. 20 | } 21 | \examples{ 22 | data(k36a) 23 | } 24 | \keyword{datasets} 25 | -------------------------------------------------------------------------------- /man/k9a.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{data} 4 | \name{k9a} 5 | \alias{k9a} 6 | \title{H3K9ac profile} 7 | \format{ 8 | data frame 9 | } 10 | \usage{ 11 | data(k9a) 12 | } 13 | \description{ 14 | Example edgeR result of differential ChIP-seq H3K9ac. 15 | This is a dataframe which contains columns for log fold change, log counts 16 | per million, p-value and FDR adjusted p-value. These columns consist of 17 | numerical values. The row names represent human gene names. This is a sample 18 | of 1000 gene of an original dataset that contains measurements of ~30000 19 | genes. 20 | } 21 | \examples{ 22 | data(k9a) 23 | } 24 | \keyword{datasets} 25 | -------------------------------------------------------------------------------- /man/mitch.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{package} 4 | \name{mitch} 5 | \alias{mitch-package} 6 | \alias{mitch} 7 | \title{mitch: An R package for multi-dimensional pathway enrichment analysis} 8 | \description{ 9 | mitch is an R package for multi-dimensional enrichment analysis. At it's 10 | heart, it uses a rank-MANOVA based statistical approach to detect sets of 11 | genes that exhibit enrichment in the multidimensional space as compared to 12 | the background. mitch is useful for pathway analysis of profiling studies 13 | with two to or more contrasts, or in studies with multiple omics profiling, 14 | for example proteomic, transcriptomic, epigenomic analysis of the same 15 | samples. mitch is perfectly suited for pathway level differential analysis 16 | of scRNA-seq data. 17 | } 18 | \details{ 19 | A typical mitch workflow consists of: 20 | 1) Import gene sets with gmt_import() 21 | 2) Import profiling data with mitch_import() 22 | 3) Calculate enrichments with mitch_calc() 23 | 4) And generate plots and reports with mitch_plots() and mitch_report() 24 | 25 | More documentation on the github page https://github.com/markziemann/mitch 26 | or with ?, eg: ?mitch_import 27 | } 28 | \examples{ 29 | # Example workflow 30 | # Import some gene sets 31 | genesetsExample<-gmt_import(system.file('extdata/sample_genesets.gmt', 32 | package = 'mitch')) 33 | # Load some edgeR tables (rna, k9a, k36a). 34 | data(rna,k9a,k36a) 35 | # Create a list of differential profiles 36 | myList<-list('rna'=rna,'k9a'=k9a,'k36a'=k36a) 37 | # Import as edgeR table 38 | myImportedData<-mitch_import(myList,DEtype='edger') 39 | # Calculate enrichment using MANOVA 40 | resExample<-mitch_calc(myImportedData,genesetsExample,priority='effect', 41 | resrows=5,cores=2) 42 | # Generate some high res plots in PDF format 43 | mitch_plots(resExample,outfile='outres.pdf') 44 | #' Generate a report of the analysis in HTML format 45 | mitch_report(resExample,'outres.html') 46 | } 47 | \seealso{ 48 | Useful links: 49 | \itemize{ 50 | \item \url{https://github.com/markziemann/mitch} 51 | \item Report bugs at \url{https://github.com/markziemann/mitch} 52 | } 53 | 54 | } 55 | \author{ 56 | \strong{Maintainer}: Mark Ziemann \email{mark.ziemann@gmail.com} (\href{https://orcid.org/0000-0002-7688-6974}{ORCID}) [copyright holder] 57 | 58 | Authors: 59 | \itemize{ 60 | \item Antony Kaspi [copyright holder] 61 | } 62 | 63 | } 64 | -------------------------------------------------------------------------------- /man/mitch_calc.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \name{mitch_calc} 4 | \alias{mitch_calc} 5 | \title{mitch_calc} 6 | \usage{ 7 | mitch_calc( 8 | x, 9 | genesets, 10 | minsetsize = 10, 11 | cores = 1, 12 | resrows = 50, 13 | priority = NULL 14 | ) 15 | } 16 | \arguments{ 17 | \item{x}{a multicolumn numerical table with each column containing 18 | differential expression scores for a contrast. 19 | Rownames must match genesets.} 20 | 21 | \item{genesets}{lists of genes imported by the gmt_imprt function or 22 | similar.} 23 | 24 | \item{minsetsize}{the minimum number of genes required in a set for it to be 25 | included in the statistical analysis. 26 | Default is 10.} 27 | 28 | \item{cores}{the number of parallel threads for computation. Defaults to 1.} 29 | 30 | \item{resrows}{an integer value representing the number of top genesets for 31 | which a detailed report is to be 32 | generated. Default is 50.} 33 | 34 | \item{priority}{the prioritisation metric used to selecting top gene sets. 35 | Valid options are 'significance', 36 | 'effect' and 'SD'.} 37 | } 38 | \value{ 39 | mitch res object with the following parts: 40 | $input_profile: the supplied input differential profile 41 | $input_genesets: the supplied input gene sets 42 | $ranked_profile: the differential profile after ranking 43 | $enrichment_result: the table of MANOVA/ANOVA enrichment results for each 44 | gene set 45 | $analysis_metrics: several metrics that are important to the interpretation 46 | of the results 47 | $detailed_sets: a list of dataframes containing ranks of members of 48 | prioritised gene sets. 49 | } 50 | \description{ 51 | This function performs multivariate gene set enrichment analysis. 52 | } 53 | \examples{ 54 | # Example using mitch to calculate multivariate enrichments and 55 | # prioritise based on effect size 56 | data(myImportedData,genesetsExample) 57 | resExample<-mitch_calc(myImportedData,genesetsExample,priority='effect', 58 | minsetsize=5,cores=2) 59 | } 60 | \keyword{calc} 61 | \keyword{calculate} 62 | \keyword{manova} 63 | \keyword{mitch} 64 | -------------------------------------------------------------------------------- /man/mitch_import.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \name{mitch_import} 4 | \alias{mitch_import} 5 | \title{mitch_import} 6 | \usage{ 7 | mitch_import(x, DEtype, geneIDcol = NULL, geneTable = NULL, joinType = NULL) 8 | } 9 | \arguments{ 10 | \item{x}{a list of differential expression tables.} 11 | 12 | \item{DEtype}{the program that generated the differential expression table 13 | Valid options are 'edgeR', 'DESeq2', 'limma', 'ABSSeq', 'Sleuth', 'Seurat', 14 | 'topConfects', 'muscat', 'Swish', 'scDE', 'MAST', 'DEsingle', 'ballgown', 15 | 'NOIseq', 'TCC', 'DEDS', 'cuffdiff', 'fishpond', 'missMethyl', 'DMRcate', 16 | 'DEP', 'msmsTests', 'plgem', 'SDAMS', 'DEqMS', 'DiffBind' and 'prescored'. 17 | Where 'prescored' is a dataframe containing the test statistic and gene ID 18 | (either in rowname or separate column) and nothing else. 'preranked' is an 19 | alias for 'prescored'.} 20 | 21 | \item{geneIDcol}{the column containing gene names. If gene names are 22 | specified as row names, then geneIDcol=NULL.} 23 | 24 | \item{geneTable}{a 2 column table mapping gene identifiers in the profile to 25 | gene identifiers in the gene sets.} 26 | 27 | \item{joinType}{the type of join to perform, either 'inner' or 'full'. 28 | By default, joins are 'inner' except for Seurat and muscat where full is 29 | used.} 30 | } 31 | \value{ 32 | a multi-column table compatible with mitch_calc analysis. 33 | } 34 | \description{ 35 | This function imports differential omics data from common differential tools 36 | like edgeR, limma and DESeq2. It calculates a summarised differential 37 | expression metric by multiplying the sign of the log fold change by the 38 | -log10 of the p-value. If this behaviour is not desired, mitch_import can be 39 | bypassed in favour of another scoring metric. 40 | } 41 | \examples{ 42 | # first step is to create a list of differential profiles 43 | data(rna,k9a,k36a) 44 | x<-list('rna'=rna,'k9a'=k9a,'k36a'=k36a) 45 | # import as edgeR table 46 | imported<-mitch_import(x,DEtype='edger') 47 | } 48 | \keyword{import} 49 | \keyword{mitch} 50 | -------------------------------------------------------------------------------- /man/mitch_plots.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \name{mitch_plots} 4 | \alias{mitch_plots} 5 | \title{mitch_plots} 6 | \usage{ 7 | mitch_plots(res, outfile = "Rplots.pdf") 8 | } 9 | \arguments{ 10 | \item{res}{a mitch results object.} 11 | 12 | \item{outfile}{the destination file for the plots in PDF format. should 13 | contain 'pdf' suffix. Defaults to 14 | 'Rplots.pdf'} 15 | } 16 | \value{ 17 | generates a PDF file containing enrichment plots. 18 | } 19 | \description{ 20 | This function generates several plots of multivariate gene set enrichment in 21 | high resolution PDF format. 22 | The number of detailed sets to generate is dictated by the resrows set in 23 | the mitch_calc command. 24 | } 25 | \examples{ 26 | data(resExample) 27 | mitch_plots(resExample,outfile='outres.pdf') 28 | } 29 | \keyword{mitch} 30 | \keyword{pdf} 31 | \keyword{plot} 32 | \keyword{plots} 33 | -------------------------------------------------------------------------------- /man/mitch_report.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \name{mitch_report} 4 | \alias{mitch_report} 5 | \title{mitch_report} 6 | \usage{ 7 | mitch_report(res, outfile, overwrite = FALSE) 8 | } 9 | \arguments{ 10 | \item{res}{a mitch results object.} 11 | 12 | \item{outfile}{the destination file for the html report. should contain 13 | 'html' suffix. Defaults to 14 | 'Rplots.pdf'} 15 | 16 | \item{overwrite}{should overwrite the report file if it already exists?} 17 | } 18 | \value{ 19 | generates a HTML file containing enrichment plots. 20 | } 21 | \description{ 22 | This function generates an R markdown based html report containing tables 23 | and several plots of mitch results 24 | The plots are in png format, so are not as high in resolution as compared to 25 | the PDF generated by mitch_plots 26 | function. The number of detailed sets to generate is dictated by the resrows 27 | set in the mitch_calc command. 28 | } 29 | \examples{ 30 | data(resExample) 31 | mitch_report(resExample,'outres2.html') 32 | } 33 | \keyword{html} 34 | \keyword{knitr} 35 | \keyword{markdown} 36 | \keyword{mitch} 37 | \keyword{report} 38 | -------------------------------------------------------------------------------- /man/myImportedData.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{data} 4 | \name{myImportedData} 5 | \alias{myImportedData} 6 | \title{myImportedData: Example imported profiles} 7 | \format{ 8 | data frame 9 | } 10 | \usage{ 11 | data(myImportedData) 12 | } 13 | \description{ 14 | Example of three edgeR profiles imported using mitch. 15 | The structure of this data is a dataframe where each column represents 16 | one of the following profiling datasets after scoring: RNA, H3K9ac and 17 | H3K36ac. Each row represents one gene and this dataset contains just 1000 18 | rows to keep the example dataset small. 19 | } 20 | \examples{ 21 | data(myImportedData) 22 | } 23 | \keyword{datasets} 24 | -------------------------------------------------------------------------------- /man/myList.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{data} 4 | \name{myList} 5 | \alias{myList} 6 | \title{myList: A list of three edgeR results} 7 | \format{ 8 | data frame 9 | } 10 | \usage{ 11 | data(myList) 12 | } 13 | \description{ 14 | Example edgeR results of differential RNA, H3K9ac and H3K36ac profiling. 15 | The structure of this data is a list of three dataframes. Each data frame 16 | is 1000 lines only. 17 | } 18 | \examples{ 19 | data(myList) 20 | } 21 | \keyword{datasets} 22 | -------------------------------------------------------------------------------- /man/network_genes.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \name{network_genes} 4 | \alias{network_genes} 5 | \title{network_genes} 6 | \usage{ 7 | network_genes(eres, FDR = 0.05, n_sets = 20) 8 | } 9 | \arguments{ 10 | \item{eres}{a mitch results object.} 11 | 12 | \item{FDR}{the significance threshold for inclusion. By default, it is 0.05} 13 | 14 | \item{n_sets}{the number of sets to include.} 15 | } 16 | \value{ 17 | a list of up- and down-regulated gene sets 18 | } 19 | \description{ 20 | This is a companion function for the network diagrams to display the genes which contribute 21 | to the enrichment and are shared between gene sets. 22 | } 23 | \examples{ 24 | data(resExample) 25 | network_genes(resExample) 26 | } 27 | \keyword{mitch} 28 | \keyword{network} 29 | \keyword{visualisation} 30 | -------------------------------------------------------------------------------- /man/networkplot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \name{networkplot} 4 | \alias{networkplot} 5 | \title{networkplot} 6 | \usage{ 7 | networkplot(eres, FDR = 0.05, n_sets = 20) 8 | } 9 | \arguments{ 10 | \item{eres}{a mitch results object.} 11 | 12 | \item{FDR}{the significance threshold for inclusion. By default, it is 0.05} 13 | 14 | \item{n_sets}{the number of sets to include. This type of graph can get messy, 15 | so the default is 20, but it can be customised to your needs.} 16 | } 17 | \value{ 18 | generates network diagrams. 19 | } 20 | \description{ 21 | This function generates gene set network diagrams to complement the enrichment analysis. 22 | It produces separate charts for up and down-regulated sets. 23 | This only includes genes ranked in the top and bottom tertiles. 24 | Genes in the set which do not meet this criterion are discarded. 25 | The intensity of the colour is proportional to the s.dist value (enrichment score). 26 | Circle size is proportional to the number of genes in the set. 27 | Line thickness is proportional to the Jaccard similarity value. 28 | This function works best after prioritisation with "effect" when running 29 | mitch_calc(). 30 | Note that the circle size and the line width shown in the legend is approximate, 31 | although the values shown are exactly the smallest and largest respectively. 32 | Note that this chart works best when the width is double the height, otherwise 33 | many of the long gene set names could be cut off. 34 | There is an element of stochasticity with regard to the network projection, so 35 | it could be a good idea to repeat it a few times until you get a nice layout. 36 | } 37 | \examples{ 38 | data(resExample) 39 | networkplot(resExample) 40 | } 41 | \keyword{mitch} 42 | \keyword{network} 43 | \keyword{visualisation} 44 | -------------------------------------------------------------------------------- /man/resExample.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{data} 4 | \name{resExample} 5 | \alias{resExample} 6 | \title{resExample: Example mitch result} 7 | \format{ 8 | list of mixed data types 9 | } 10 | \usage{ 11 | data(resExample) 12 | } 13 | \description{ 14 | Example of mitch results. Enrichment of the Reactome gene sets in the RNA, 15 | H3K9ac and H3K36ac datasets. The structure of this data set is a list where 16 | the 1st element is "input_profile" that has been imported (data frame), 2nd 17 | element is the "input_genesets" (names list of gene names [character 18 | vectors]), 3rd is "ranked_profile" which is the input profiling data after 19 | ranking (data frame), 4th is "enrichment_result" which is a data frame which 20 | provides enrichment information on each gene set in the profiling data 21 | including s scores, p-values and FDR adjusted p-values. 5th is 22 | "analysis_metrics" (list). The 6th slot is "detailed_sets" which is a list 23 | of 5 matrices which details the enrichment of members of selected gene sets. 24 | } 25 | \examples{ 26 | data(resExample) 27 | } 28 | \keyword{datasets} 29 | -------------------------------------------------------------------------------- /man/rna.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/mitch.R 3 | \docType{data} 4 | \name{rna} 5 | \alias{rna} 6 | \title{RNA profile} 7 | \format{ 8 | data frame 9 | } 10 | \usage{ 11 | data(rna) 12 | } 13 | \description{ 14 | Example edgeR result of differential RNA expression. 15 | This is a dataframe which contains columns for log fold change, log counts 16 | per million, p-value and FDR adjusted p-value. These columns consist of 17 | numerical values. The row names represent human gene names. This is a sample 18 | of 1000 gene of an original dataset that contains measurements of ~15000 19 | genes. 20 | } 21 | \examples{ 22 | data(rna) 23 | } 24 | \keyword{datasets} 25 | -------------------------------------------------------------------------------- /tests/test-mitch.R: -------------------------------------------------------------------------------- 1 | library("mitch") 2 | library("testthat") 3 | 4 | test_that("multiplication works", { 5 | expect_equal(2 * 2, 4) 6 | }) 7 | 8 | # 1d 9 | data(rna,genesetsExample) 10 | y<-mitch_import(rna,DEtype="edgeR") 11 | res<-mitch_calc(y,genesetsExample,cores=2) 12 | mitch_plots(res,outfile="1d.pdf") 13 | if (file.exists("1d.html")) { unlink("1d.html") } 14 | mitch_report(res,"1d.html") 15 | 16 | test_that("1d works", { 17 | expect_equal( length(which(res$enrichment_result$p.adjustANOVA<0.1)) ,1) 18 | expect_true(file.info("1d.pdf")$size>10000) 19 | expect_true(file.info("1d.html")$size>1000000) 20 | }) 21 | 22 | unlink("1d.html") 23 | unlink("1d.pdf") 24 | 25 | 26 | # 1d part 2 to make sure that saving files at a different location works 27 | MYPATH=paste(getwd(),"/1d.html",sep="") 28 | if (file.exists("1d.html")) { unlink("1d.html") } 29 | mitch_report(res,MYPATH) 30 | 31 | test_that("1d works", { 32 | expect_true(file.info("1d.html")$size>1000000) 33 | }) 34 | 35 | unlink("1d.html") 36 | 37 | 38 | 39 | # 2d 40 | data(rna,k9a,genesetsExample) 41 | x<-list("rna"=rna,"k9a"=k9a) 42 | y<-mitch_import(x,DEtype="edgeR") 43 | res<-mitch_calc(y,genesetsExample,cores=2) 44 | mitch_plots(res,outfile="2d.pdf") 45 | if (file.exists("2d.html")) { unlink("2d.html") } 46 | mitch_report(res,"2d.html") 47 | 48 | test_that("2d works", { 49 | expect_equal( length(which(res$enrichment_result$p.adjustMANOVA<0.1)) ,1) 50 | expect_true(file.info("2d.pdf")$size>100000) 51 | expect_true(file.info("2d.html")$size>1000000) 52 | }) 53 | 54 | unlink("2d.html") 55 | unlink("2d.pdf") 56 | 57 | # 3d 58 | data(rna,k9a,k36a,genesetsExample) 59 | x<-list("rna"=rna,"k9a"=k9a,"k36a"=k36a) 60 | y<-mitch_import(x,DEtype="edgeR") 61 | res<-mitch_calc(y,genesetsExample,cores=2) 62 | mitch_plots(res,outfile="3d.pdf") 63 | if (file.exists("3d.html")) { unlink("3d.html") } 64 | mitch_report(res,"3d.html") 65 | 66 | test_that("3d works", { 67 | expect_equal( length(which(res$enrichment_result$p.adjustMANOVA<0.1)) ,1) 68 | expect_true(file.info("3d.pdf")$size>100000) 69 | expect_true(file.info("3d.html")$size>1000000) 70 | }) 71 | 72 | unlink("3d.html") 73 | unlink("3d.pdf") 74 | 75 | 76 | -------------------------------------------------------------------------------- /vignettes/infiniumMethArrayWorkflow.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Applying mitch to pathway analysis of Infinium Methylation array data" 3 | author: "Antony Kaspi & Mark Ziemann" 4 | date: "`r Sys.Date()`" 5 | output: 6 | rmarkdown::html_vignette: 7 | toc: true 8 | vignette: > 9 | %\VignetteIndexEntry{Applying mitch to pathway analysis of Infinium Methylation array data} 10 | %\VignetteEngine{knitr::rmarkdown} 11 | \usepackage[utf8]{inputenc} 12 | --- 13 | 14 | ## Background 15 | 16 | Applying enrichment analysis to methylation array data is difficult due to the presence 17 | of a variable number of probes per gene and the fact that a probe could belong to 18 | overlapping genes. 19 | There are existing over-representation based approaches to this, however they appear to 20 | lack sensitivity. 21 | To address this, we have developed a simple approach to aggregating differential 22 | methylation data to make it suitable for downstream use by mitch. 23 | The process begins with the differential probe methylation results from limma. 24 | Here, we summarise the limma t-statistics by gene using the arithmetic mean. 25 | The resulting gene level differential methylation scores then undergo mitch as usual. 26 | 27 | ## Requirements 28 | 29 | In addition to mitch v1.15.0 of higher, you will need an annotation set for the array you are using. 30 | These are conveniently provided as Bioconductor packages for HM450K and EPIC arrays. 31 | 32 | HM450k: IlluminaHumanMethylation450kanno.ilmn12.hg19 33 | 34 | EPIC: IlluminaHumanMethylationEPICanno.ilm10b4.hg19 35 | 36 | One issue with these is that the annotations are quite old, which means that some of the gene 37 | names have changed (~12%), so it is a good idea to screen the list of gene names and update 38 | obsolete names using the HGNChelper package. 39 | 40 | ```{r, install, eval = FALSE} 41 | if(!requireNamespace("BiocManager", quietly = TRUE)) 42 | install.packages("BiocManager") 43 | BiocManager::install("mitch") 44 | ``` 45 | 46 | ```{r,lib} 47 | 48 | suppressPackageStartupMessages({ 49 | library("mitch") 50 | library("dplyr") 51 | library("HGNChelper") 52 | library("IlluminaHumanMethylation450kanno.ilmn12.hg19") 53 | library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19") 54 | }) 55 | ``` 56 | 57 | ## Gene sets 58 | 59 | In this cut down example we will be using a sample of 200 Reactome gene sets. 60 | 61 | ```{r,genesetsExample} 62 | data(genesetsExample) 63 | head(genesetsExample,3) 64 | ``` 65 | 66 | ## Probe gene relationship for HM450K array data 67 | 68 | In order to get mitch working, we need a 2 column table that maps probes to genes. 69 | The workflow shown here is for the HM450k array, and an EPIC example is show at the end of the report. 70 | 71 | ```{r,anno1} 72 | anno <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19) 73 | myann <- data.frame(anno[,c("UCSC_RefGene_Name","UCSC_RefGene_Group","Islands_Name","Relation_to_Island")]) 74 | head(myann) 75 | gp <- myann[,"UCSC_RefGene_Name",drop=FALSE] 76 | gp2 <- strsplit(gp$UCSC_RefGene_Name,";") 77 | names(gp2) <- rownames(gp) 78 | gp2 <- lapply(gp2,unique) 79 | gt1 <- stack(gp2) 80 | colnames(gt1) <- c("gene","probe") 81 | gt1$probe <- as.character(gt1$probe) 82 | dim(gt1) 83 | head(gt1) 84 | ``` 85 | 86 | ## Update deprecated gene symbols 87 | 88 | Update old gene symbols using HGNChelper (13% of 21k names). 89 | Last updated 4th Sep 2024. 90 | 91 | ```{r,genenames} 92 | #new.hgnc.table <- getCurrentHumanMap() 93 | new.hgnc.table <- readRDS("../inst/extdata/new.hgnc.table.rds") 94 | fix <- checkGeneSymbols(gt1$gene,map=new.hgnc.table) 95 | fix2 <- fix[which(fix$x != fix$Suggested.Symbol),] 96 | length(unique(fix2$x)) 97 | gt1$gene <- fix$Suggested.Symbol 98 | head(gt1) 99 | ``` 100 | 101 | ## Importing profiling data 102 | Here we will read in a table of differential probe methylation data generated by limma. 103 | We will use the t-statistics for downstream analysis. 104 | 105 | ```{r,import1} 106 | x <- read.table("https://ziemann-lab.net/public/gmea/dma3a.tsv",header=TRUE,row.names=1) 107 | head(x) 108 | ``` 109 | 110 | Now that the profiling data is loaded, we need to import with mitch package, which 111 | establishes the probe-gene relationships and aggregates the data to gene level scores. 112 | As you can see, the input was a table of 422k probes and the output is 19,380 gene scores. 113 | Many probes not annotated to genes are discarded. 114 | 115 | ```{r,import2} 116 | y <- mitch_import(x,DEtype="limma",geneTable=gt1) 117 | head(y) 118 | dim(y) 119 | ``` 120 | 121 | ## Calculating enrichment 122 | 123 | The `mitch_calc` function performs an enrichment test. 124 | If you imported multiple data tables in the previous step, mitch will conduct a 125 | multivariate enrichment test. 126 | The results can be prioritised by significance or effect size. 127 | My recommendation is to discard results with FDR>0.05 then prioritise by effect 128 | size, which for us is the mitch enrichment score called S distance. 129 | In this example I also set the minimum gene set size to 5. 130 | 131 | ```{r,enrich1} 132 | res<-mitch_calc(y,genesetsExample,priority="effect",cores=2,minsetsize=5) 133 | head(res$enrichment_result,10) 134 | ``` 135 | 136 | ## Downstream presentation 137 | 138 | For presentation of the results you could consider making a volcano plot and/or 139 | making a barplot of S.dist values for selected gene sets that meet the FDR cutoff. 140 | You can also use the built in functions to make a set of charts and html report. 141 | It is vitally important to inspect the detailed resports to understand which 142 | genes are driving the enrichment in your dataset. 143 | 144 | ```{r,downstream1} 145 | 146 | mitch_plots(res,outfile="methcharts.pdf") 147 | 148 | ``` 149 | 150 | ```{r,downstream2,results=FALSE} 151 | 152 | mitch_report(res,"methreport.html") 153 | 154 | ``` 155 | 156 | We have a `networkplot()` function that produces two network plots, one for increased 157 | methylation (nodes shaded red), and one for decreased methylation (nodes shaded blue). 158 | See `?networkplot()` for more details. 159 | 160 | Lists of enriched gene set members are shown. 161 | 162 | ```{r,meth_network,fig.width=14,fig.height=6} 163 | 164 | networkplot(res,FDR=0.1,n_sets=10) 165 | 166 | network_genes(res,FDR=1,n_sets=10) 167 | 168 | ``` 169 | 170 | ## Probe gene relationship for EPIC array data 171 | 172 | Here is the code to create a probe-gene table for EPIC array data. 173 | Be sure to update the gene symbols with `HGNChelper` before conducting enrichment analysis. 174 | 175 | ```{r,EPIC} 176 | anno <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19) 177 | myann <- data.frame(anno[,c("UCSC_RefGene_Name","UCSC_RefGene_Group","Islands_Name","Relation_to_Island")]) 178 | gp <- myann[,"UCSC_RefGene_Name",drop=FALSE] 179 | gp2 <- strsplit(gp$UCSC_RefGene_Name,";") 180 | names(gp2) <- rownames(gp) 181 | gp2 <- lapply(gp2,unique) 182 | gt <- stack(gp2) 183 | colnames(gt) <- c("gene","probe") 184 | gt$probe <- as.character(gt$probe) 185 | dim(gt) 186 | str(gt) 187 | ``` 188 | 189 | ## Session Info 190 | ```{r,sessioninfo,message=FALSE} 191 | sessionInfo() 192 | ``` 193 | 194 | -------------------------------------------------------------------------------- /vignettes/mitchWorkflow.Rmd: -------------------------------------------------------------------------------- 1 | 2 | --- 3 | title: "mitch Workflow" 4 | author: "Antony Kaspi & Mark Ziemann" 5 | date: "`r Sys.Date()`" 6 | output: 7 | rmarkdown::html_vignette: 8 | toc: true 9 | vignette: > 10 | %\VignetteIndexEntry{mitch Workflow} 11 | %\VignetteEngine{knitr::rmarkdown} 12 | \usepackage[utf8]{inputenc} 13 | --- 14 | 15 | ## Background 16 | 17 | mitch is an R package for multi-contrast enrichment analysis. At 18 | it’s heart, it uses a rank-MANOVA based statistical approach to detect sets 19 | of genes that exhibit enrichment in the multidimensional space as compared 20 | to the background. The rank-MANOVA concept dates to work by Cox and Mann 21 | (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for 22 | pathway analysis of profiling studies with one, two or more contrasts, 23 | or in studies with multiple omics profiling, for example proteomic, 24 | transcriptomic, epigenomic analysis of the same samples. mitch is perfectly 25 | suited for pathway level differential analysis of scRNA-seq data. 26 | 27 | The main strengths of mitch are that it can import datasets easily 28 | from many upstream tools and has advanced plotting features to 29 | visualise these enrichments. mitch consists of five functions. A 30 | typical mitch workflow would consist of: 31 | 32 | * 1: Import gene sets with `gmt_import()` 33 | 34 | * 2: Import profiling data with `mitch_import()` 35 | 36 | * 3: Calculate enrichments with `mitch_calc()` 37 | 38 | * 4: And generate plots and reports with `mitch_plots()`, `mitch_report()` and 39 | `networkplot()`. 40 | 41 | ```{r, install, eval = FALSE} 42 | if(!requireNamespace("BiocManager", quietly = TRUE)) 43 | install.packages("BiocManager") 44 | BiocManager::install("mitch") 45 | ``` 46 | 47 | ```{r, lib} 48 | library("mitch") 49 | ``` 50 | 51 | ## Importing gene sets 52 | mitch has a function to import GMT files to R lists (adapted from Yu et al, 2012 53 | in the [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) 54 | package). For example we can grab some gene sets from Reactome.org: 55 | 56 | ```{r,gsets} 57 | download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", 58 | destfile="ReactomePathways.gmt.zip") 59 | unzip("ReactomePathways.gmt.zip") 60 | genesets <- gmt_import("ReactomePathways.gmt") 61 | ``` 62 | 63 | In this cut down example we will be using a sample of 200 Reactome gene sets: 64 | 65 | ```{r,genesetsExample} 66 | data(genesetsExample) 67 | head(genesetsExample,3) 68 | ``` 69 | 70 | ## Importing profiling data 71 | mitch accepts pre-ranked data supplied by the user, but also has a function 72 | called `mitch_import` for importing tables generated by limma, edgeR, DESeq2, 73 | ABSSeq, Sleuth, Seurat, Muscat and several other upstream tools. By default, 74 | only the genes that are detected in all contrasts are included, but this 75 | behaviour can be modified for sparse data setting `joinType=full`. The below 76 | example imports two edgeR tables called "rna" and "k9a" Where gene 77 | identifiers are present as row names. Note that if there is more than one 78 | profile being imported, they need to be part of a list. 79 | 80 | ```{r,import11} 81 | data(rna,k9a) 82 | x <- list("rna"=rna,"k9a"=k9a) 83 | y <- mitch_import(x,"edgeR") 84 | head(y) 85 | ``` 86 | 87 | mitch can do unidimensional analysis if you provide it a single profile as a 88 | dataframe (not in a list). 89 | 90 | ```{r,import4} 91 | y <- mitch_import(rna,DEtype="edger") 92 | head(y) 93 | ``` 94 | 95 | If the gene identifiers are not given in the rownames, then the column can be 96 | specified with the `geneIDcol` parameter like this: 97 | 98 | ```{r,import5} 99 | # first rearrange cols 100 | rna_mod <- rna 101 | rna_mod$MyGeneIDs <- rownames(rna_mod) 102 | rownames(rna_mod) <- seq(nrow(rna_mod)) 103 | head(rna_mod) 104 | # now import with geneIDcol 105 | y <- mitch_import(rna_mod,DEtype="edgeR",geneIDcol="MyGeneIDs") 106 | head(y) 107 | ``` 108 | 109 | By default, differential gene activity is scored using a supplied test 110 | statistic or directional p-value (D): 111 | 112 | D = sgn(logFC) * -log10(p-value) 113 | 114 | If this is not desired, then users can perform their own custom scoring 115 | procedure and import with `DEtype="prescored"`. 116 | 117 | There are many cases where the gene IDs don't match the gene sets. To overcome 118 | this, `mitch_import` also accepts a two-column table (gt here) that relates gene 119 | identifiers in the profiling data to those in the gene sets. In this example 120 | we can create some fake gene accession numbers to demonstrate this feature. 121 | 122 | ```{r,import6} 123 | library("stringi") 124 | # obtain vector of gene names 125 | genenames <- rownames(rna) 126 | # create fake accession numbers 127 | accessions <- paste("Gene0",stri_rand_strings(nrow(rna)*2, 6, pattern = "[0-9]"),sep="") 128 | accessions <- head(unique(accessions),nrow(rna)) 129 | # create a gene table file that relates gene names to accession numbers 130 | gt <- data.frame(genenames,accessions) 131 | 132 | # now swap gene names for accessions 133 | rna2 <- merge(rna,gt,by.x=0,by.y="genenames") 134 | rownames(rna2) <- rna2$accessions 135 | rna2 <- rna2[,2:5] 136 | 137 | k9a2 <- merge(k9a,gt,by.x=0,by.y="genenames") 138 | rownames(k9a2) <- k9a2$accessions 139 | k9a2 <- k9a2[,2:5] 140 | 141 | # now have a peek at the input data before importing 142 | head(rna2,3) 143 | head(k9a2,3) 144 | head(gt,3) 145 | x <- list("rna2"=rna2,"k9a2"=k9a2) 146 | y <- mitch_import(x,DEtype="edgeR",geneTable=gt) 147 | head(y,3) 148 | ``` 149 | 150 | `?mitch_import` provides more instructions on using this feature. 151 | 152 | ## Calculating enrichment 153 | The `mitch_calc` function performs multivariate enrichment analysis of the 154 | supplied gene sets in the scored profiling data. At its simpest form 155 | `mitch_calc` function accepts the scored data as the first argument and the 156 | genesets as the second argument. Users can prioritise enrichments based on 157 | small adjusted p-values, by the observed effect size ("s.dist", the magnitude 158 | of "s", the enrichment score) or the standard deviation of the s scores. 159 | For most analyses, you will see more specific results when prioritising by 160 | effect, but it is worth looking at both. 161 | 162 | Note that the number of parallel threads ("cores") is set here to cores=2 but 163 | the default is to use just one. 164 | If you have many available threads, then a good number is 8-12. 165 | Note that the more CPU threads used, the more RAM is required, so monitor the 166 | available memory on your system. 167 | 168 | ```{r,calc1,results="hide"} 169 | # prioritisation by significance 170 | res <- mitch_calc(y,genesetsExample,priority="significance",cores=2) 171 | ``` 172 | 173 | ```{r,calc2} 174 | # peek at the results 175 | head(res$enrichment_result) 176 | ``` 177 | 178 | ```{r,calc3,results="hide"} 179 | # prioritisation by effect size 180 | res <- mitch_calc(y,genesetsExample,priority="effect",cores=2) 181 | ``` 182 | 183 | ```{r,calc4} 184 | head(res$enrichment_result) 185 | ``` 186 | 187 | By default, gene sets with fewer than 10 members present in the profiling data 188 | are discarded. This threshold can be modified using the minsetsize option. 189 | There is no upper limit of gene set size. 190 | 191 | ```{r,calc5,results="hide"} 192 | res <- mitch_calc(y,genesetsExample,priority="significance",minsetsize=5,cores=2) 193 | ``` 194 | 195 | By default, in downstream visualisation steps, charts are made from the top 50 gene 196 | sets, but this can be modified using the resrows option. 197 | 198 | ```{r,calc6,results="hide"} 199 | res<-mitch_calc(y,genesetsExample,priority="significance",resrows=3,cores=2) 200 | ``` 201 | 202 | ## Generate a HTML report 203 | The HTML reports contain several plots as raster images and interactive charts 204 | which are useful as a first-pass visualisation. These can be generated like 205 | this: 206 | 207 | ```{r,report,results="hide"} 208 | mitch_report(res,"myreport.html",overwrite=TRUE) 209 | ``` 210 | 211 | ## Generate high resolution plots 212 | In case you want the charts in PDF format, for example for publications, these 213 | can be generated as such: 214 | 215 | ```{r,plots,results="hide"} 216 | mitch_plots(res,outfile="mycharts.pdf") 217 | ``` 218 | 219 | ## Network plot 220 | 221 | For one-dimensional analysis, you can generate a network plot to visualise the 222 | similarity of the driver gene members for the top (20) up- and downregulated sets 223 | meeting a significance cut-off (FDR<0.05). 224 | These cut-offs can be altrered. 225 | 226 | By default, this includes genes scored in the top and bottom tertiles, otherwise 227 | they are discarded. 228 | The intensity of the colour is proportional to the s.dist value (enrichment score). 229 | Circle size is proportional to the number of genes in the set. 230 | Line thickness is proportional to the Jaccard similarity value. 231 | This function works best after prioritisation with "effect" when running 232 | `mitch_calc()`. 233 | Note that the circle (node) size and the line width shown in the legend is approximate, 234 | although the values shown are exactly the smallest and largest respectively. 235 | Note that this chart works best when the width is double the height, otherwise 236 | many of the long gene set names could be cut off. 237 | There is an element of stochasticity with regard to the network projection, so 238 | it could be a good idea to repeat it a few times until you get a nice layout. 239 | 240 | Lists of enriched gene set members are shown. 241 | 242 | ```{r,network_demo,fig.width=14,fig.height=6} 243 | 244 | networkplot(res,FDR=1,n_sets=10) 245 | 246 | network_genes(res,FDR=1,n_sets=10) 247 | 248 | ``` 249 | 250 | ## Session Info 251 | 252 | ```{r,sessioninfo,message=FALSE} 253 | sessionInfo() 254 | ``` 255 | --------------------------------------------------------------------------------