├── .DS_Store ├── .gitattributes ├── Dockerfile ├── LFQ_report.Rmd ├── LICENSE ├── R ├── demo_functions.R ├── enrichment_functions.R ├── functions.R ├── tests.R └── volcano_function.R ├── README.md ├── data ├── .DS_Store ├── create_example_data.R ├── demo_data.RData ├── example_data.RData ├── exp_design_p10_0144.txt ├── lfq_results.RData └── proteinGroups.txt ├── dependencies.txt ├── docs ├── CV_plot.png ├── PCA_plot.png ├── Protein_number.png ├── Protein_overlap.png ├── _config.yml ├── correlation_plot.png ├── heatmap.png ├── imputation.png ├── index.md ├── missing_heatmap.png ├── missing_quant.png ├── pvalue_hist.png └── volcano_plot.png ├── global.R ├── google_analytics-GA4.html ├── google_analytics.js ├── server.R ├── shiny-server.conf ├── ui.R └── www ├── CV_plot.png ├── Info.Rmd ├── Info.html ├── Info.md ├── Info_cache ├── html │ ├── __packages │ ├── exp_design_b71c6ebe27cac9dec05c8ffe7432463e.RData │ ├── exp_design_b71c6ebe27cac9dec05c8ffe7432463e.rdb │ └── exp_design_b71c6ebe27cac9dec05c8ffe7432463e.rdx └── markdown_strict │ ├── __packages │ ├── exp_design_ba255ed02e26d1f4a5a14728288efd24.RData │ ├── exp_design_ba255ed02e26d1f4a5a14728288efd24.rdb │ └── exp_design_ba255ed02e26d1f4a5a14728288efd24.rdx ├── LFQ-Analyst_manual.pdf ├── LFQ-Analyst_report.pdf ├── LFQ_analyst.png ├── LFQ_analyst.svg ├── PCA_plot.png ├── Protein_number.png ├── Protein_overlap.png ├── correlation_plot.png ├── css └── custom.css ├── data ├── experimental_design_example.txt ├── experimental_design_example_feb19.txt ├── proteinGroups_example.txt └── proteinGroups_example_feb19.txt ├── google-analytics.js ├── google_analytics.js ├── heatmap.png ├── imputation.png ├── js └── google_analytics.js ├── mbpf_logo.jpg ├── mbpf_logo.png ├── missing_heatmap.png ├── missing_quant.png ├── monash_logo.png ├── pvalue_hist.png └── volcano_plot.png /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/.DS_Store -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Source files 2 | # ============ 3 | *.Rdata text 4 | *.rdb binary 5 | *.rds binary 6 | *.Rd text 7 | *.Rdx binary 8 | *.Rmd text 9 | *.R text -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # syntax=docker/dockerfile:1 2 | FROM rocker/shiny-verse:4.2.1 3 | 4 | RUN apt-get update && apt-get install -yq \ 5 | libhdf5-dev libnetcdf-dev build-essential libgd-dev libbz2-dev libudunits2-dev libproj-dev libgdal-dev \ 6 | texlive-latex-base texlive-fonts-recommended texlive-fonts-extra texlive-latex-extra 7 | 8 | RUN Rscript -e 'install.packages(c("devtools", "tidyverse", "ggrepel", "httr", "rjson", "mvtnorm", "tmvtnorm", \ 9 | "imputeLCMD", "plotly", "DT", "BiocManager","testthat", "RColorBrewer", "shiny","shinyalert","shinydashboard", \ 10 | "shinyjs", "svglite", "rhandsontable", "shinyBS", "shinyWidgets", "ggVennDiagram", "shinycssloaders","shiny.info"), dependencies=TRUE)' 11 | 12 | #FROM bioconductor/bioconductor_docker:RELEASE_3_15 13 | RUN Rscript -e 'BiocManager::install(pkgs=c("DEP", "SummarizedExperiment", "limma", "ComplexHeatmap","pcaMethods","impute"), ask=F, dependencies=TRUE)' 14 | 15 | COPY ./ /srv/shiny-server/lfq-analyst 16 | COPY shiny-server.conf /etc/shiny-server/shiny-server.conf 17 | #RUN rm -f /srv/shiny-server/lfq-analyst/.Rprofile 18 | RUN chmod -R +r /srv/shiny-server/lfq-analyst -------------------------------------------------------------------------------- /LFQ_report.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "LFQ-Analyst report" 3 | date: "`r format(Sys.time(), '%d %B, %Y')`" 4 | params: 5 | data: NA 6 | alpha: NA 7 | lfc: NA 8 | num_signif: NA 9 | tested_contrasts: NA 10 | numbers_input: NA 11 | coverage_input: NA 12 | pca_input: NA 13 | correlation_input: NA 14 | missval_input: NA 15 | detect_input: NA 16 | imputation_input: NA 17 | p_hist_input: NA 18 | heatmap_input: NA 19 | dep: NA 20 | pg_width: NA 21 | cvs_input: NA 22 | output: 23 | pdf_document: 24 | fig_caption: yes 25 | --- 26 | ```{r setup, include=FALSE} 27 | knitr::opts_chunk$set(opts.label="kill_prefix") # Remove line number and Comment "##" from printing 28 | ``` 29 | 30 | ## Method details 31 | 32 | The raw data files were analyzed using MaxQuant to obtain protein identifications and their respective label-free quantification values using in-house standard parameters. Of note, the data were normalization based on the assumption that the majority of proteins do not change between the different conditions. 33 | Statistical analysis was performed using an in-house generated R script based on the ProteinGroup.txt file. First, contaminant proteins, reverse sequences and proteins identified “only by site” were filtered out. In addition, proteins that have been only identified by a single peptide and proteins not identified/quantified consistantly in same condition have been removed as well. The LFQ data was converted to log2 scale, samples were grouped by conditions and missing values were imputed using the ‘Missing not At Random’ (MNAR) method, which uses random draws from a left-shifted Gaussian distribution of 1.8 StDev (standard deviation) apart with a width of 0.3. Protein-wise linear models combined with empirical Bayes statistics were used for the differential expression analyses. The _limma_ package from R Bioconductor was used to generate a list of differentially expressed proteins for each pair-wise comparison. A cutoff of the _adjusted p-value_ of 0.05 (Benjamini-Hochberg method) along with a |log2 fold change| of 1 has been applied to determine significantly regulated proteins in each pairwise comparison. 34 | 35 | 36 | ### Quick summary of parameters used: 37 | 38 | * Tested pairwise comparisons = `r params$tested_contrasts` 39 | * Adjusted _p-value_ cutoff <= `r params$alpha` 40 | * Log fold change cutoff >= `r params$lfc` 41 | 42 | ## Results 43 | 44 | #### MaxQuant result output contains `r nrow(params$data)` proteins groups of which _`r nrow(params$dep())`_ proteins were reproducibly quantified. 45 | 46 | #### `r params$num_signif` proteins differ significantly between samples. 47 | 48 | \pagebreak 49 | 50 | 51 | ## Exploratory Analysis (QC Plots) 52 | 53 | #### Principle Component Analysis (PCA) plot 54 | 55 | 56 | ```{r pca_plot, echo=FALSE, fig.height= 4, fig.align='center', warning=FALSE} 57 | print(params$pca_input()) 58 | ``` 59 | \pagebreak 60 | 61 | #### Sample Correlation matrix 62 | 63 | ```{r correlation_heatmap, echo=FALSE, fig.keep='first',fig.align='center'} 64 | print(params$correlation_input()) 65 | ``` 66 | \pagebreak 67 | 68 | #### Sample Coefficient of variation (CVs) 69 | 70 | ```{r sample_cv, echo=FALSE, warning=FALSE, message=FALSE, fig.align='center'} 71 | print(params$cvs_input()) 72 | ``` 73 | \pagebreak 74 | 75 | ### Proteomics Experiment Summary 76 | 77 | Protein quantified per sample (after pre-processing). 78 | 79 | ```{r numbers, echo=FALSE, warning=FALSE, results='hide', message=FALSE } 80 | print(params$numbers_input()) 81 | ``` 82 | \pagebreak 83 | 84 | Protein overlap in all samples. 85 | 86 | 87 | ```{r coverage, echo=FALSE, warning=FALSE} 88 | print(params$coverage_input()) 89 | ``` 90 | \pagebreak 91 | 92 | 93 | 94 | ## Missing Value handling 95 | 96 | #### Missing value heatmap 97 | A heatmap for proteins with missing value in each dataset. Each row represent a protein with missing value in one or more replicate. Each replicate is clustered based on presence of missing values in the sample. 98 | 99 | 100 | ```{r missing_value_heatmap, echo=FALSE, message=FALSE, results='hide',warning=FALSE, tidy=TRUE} 101 | params$missval_input() 102 | ``` 103 | \pagebreak 104 | 105 | #### Missing value distribution 106 | Protein expression distribution before and after imputation. The plot showing the effect of imputation on protein expression distribution. 107 | 108 | ```{r imputation_effect, echo=FALSE, message=FALSE, warning=FALSE, results='hide'} 109 | print(params$imputation_input()) 110 | ``` 111 | \pagebreak 112 | 113 | ## Differential Expression Analysis (Results Plots) 114 | 115 | #### Heatmap 116 | A plot representing an overview of expression of all significant (differencially expressed) proteins (rows) in all samples (columns). 117 | 118 | 119 | ```{r heatmap_2, echo=FALSE, warning=FALSE, results='hide', fig.keep='first',fig.align='center'} 120 | print(params$heatmap_input()) 121 | ``` 122 | 123 | \pagebreak 124 | 125 | #### Volcano Plots 126 | 127 | ```{r volcano, echo=FALSE, warning=FALSE, comment=NA,fig.align='center'} 128 | for(i in params$tested_contrasts){ 129 | # print(paste0('volcano_plot_',i,sep="")) 130 | print(plot_volcano_new(params$dep(),contrast = i,label_size = 2, add_names = F)) 131 | } 132 | ``` 133 | 134 | 135 | 136 | 137 | 138 | 139 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /R/demo_functions.R: -------------------------------------------------------------------------------- 1 | ### functions to deal with demo data 2 | 3 | LoadToEnvironment <- function(RData, env=new.env()) { 4 | load(RData, env) 5 | return(env) 6 | } 7 | 8 | #env<-LoadToEnvironment("data/example_data.RData") 9 | 10 | #https://stackoverflow.com/questions/31794702/r-shiny-dashboard-tabitems-not-apparing 11 | convertMenuItem <- function(mi,tabName) { 12 | mi$children[[1]]$attribs['data-toggle']="tab" 13 | mi$children[[1]]$attribs['data-value'] = tabName 14 | mi 15 | } 16 | 17 | progress_indicator<-function(text_message){ 18 | withProgress(message = text_message, 19 | detail = 'Please wait for a while', value = 0, { 20 | for (i in 1:15) { 21 | incProgress(1/15) 22 | Sys.sleep(0.25) 23 | } 24 | }) 25 | } 26 | #env<-LoadToEnvironment("data/example_data.RData") 27 | -------------------------------------------------------------------------------- /R/enrichment_functions.R: -------------------------------------------------------------------------------- 1 | enrichr_mod <- function(genes, databases = NULL) { 2 | httr::set_config(httr::config(ssl_verifypeer = 0L)) 3 | cat("Uploading data to Enrichr... ") 4 | if (is.vector(genes) & ! all(genes == "") & length(genes) != 0) { 5 | temp <- POST(url="http://maayanlab.cloud/Enrichr/enrich", 6 | body=list(list=paste(genes, collapse="\n"))) 7 | } else if (is.data.frame(genes)) { 8 | temp <- POST(url="http://maayanlab.cloud/Enrichr/enrich", 9 | body=list(list=paste(paste(genes[,1], genes[,2], sep=","), 10 | collapse="\n"))) 11 | } else { 12 | warning("genes must be a non-empty vector of gene names or a dataframe with genes and score.") 13 | } 14 | GET(url="http://maayanlab.cloud/Enrichr/share") 15 | cat("Done.\n") 16 | dbs <- as.list(databases) 17 | dfSAF <- options()$stringsAsFactors 18 | options(stringsAsFactors = FALSE) 19 | result <- lapply(dbs, function(x) { 20 | cat(" Querying ", x, "... ", sep="") 21 | r <- GET(url="http://maayanlab.cloud/Enrichr/export", 22 | query=list(file="API", backgroundType=x)) 23 | r <- gsub("'", "'", intToUtf8(r$content)) 24 | tc <- textConnection(r) 25 | r <- read.table(tc, sep = "\t", header = TRUE, quote = "", comment.char="") 26 | close(tc) 27 | cat("Done.\n") 28 | return(r) 29 | }) 30 | options(stringsAsFactors = dfSAF) 31 | cat("Parsing results... ") 32 | names(result) <- dbs 33 | cat("Done.\n") 34 | return(result) 35 | } 36 | 37 | 38 | ###### ========= Test_gsea new 39 | 40 | 41 | test_gsea_mod <- function(dep, 42 | databases, 43 | contrasts = TRUE) { 44 | # Show error if inputs are not the required classes 45 | assertthat::assert_that(inherits(dep, "SummarizedExperiment"), 46 | is.character(databases), 47 | is.logical(contrasts), 48 | length(contrasts) == 1) 49 | 50 | 51 | row_data <- rowData(dep, use.names = FALSE) 52 | # Show error if inputs do not contain required columns 53 | if(any(!c("name", "ID") %in% colnames(row_data))) { 54 | stop("'name' and/or 'ID' columns are not present in '", 55 | deparse(substitute(dep)), 56 | "'\nRun make_unique() and make_se() to obtain the required columns", 57 | call. = FALSE) 58 | } 59 | if(length(grep("_p.adj|_diff", colnames(row_data))) < 1) { 60 | stop("'[contrast]_diff' and/or '[contrast]_p.adj' columns are not present in '", 61 | deparse(substitute(dep)), 62 | "'\nRun test_diff() to obtain the required columns", 63 | call. = FALSE) 64 | } 65 | 66 | 67 | 68 | # Run background list 69 | message("Background") 70 | background <- gsub("[.].*", "", row_data$name) 71 | background_enriched <- enrichr_mod(background, databases) 72 | df_background <- NULL 73 | for(database in databases) { 74 | temp <- background_enriched[database][[1]] %>% 75 | mutate(var = database) 76 | df_background <- rbind(df_background, temp) 77 | } 78 | df_background$contrast <- "background" 79 | df_background$n <- length(background) 80 | 81 | OUT <- df_background %>% 82 | mutate(bg_IN = as.numeric(gsub("/.*", "", Overlap)), 83 | bg_OUT = n - bg_IN) %>% 84 | select(Term, bg_IN, bg_OUT) 85 | 86 | if(contrasts) { 87 | # Get gene symbols 88 | df <- row_data %>% 89 | as.data.frame() %>% 90 | select(name, ends_with("_significant")) %>% 91 | mutate(name = gsub("[.].*", "", name)) 92 | 93 | # Run enrichR for every contrast 94 | df_enrich <- NULL 95 | for(contrast in colnames(df[2:ncol(df)])) { 96 | message(gsub("_significant", "", contrast)) 97 | significant <- df[df[[contrast]],] 98 | genes <- significant$name 99 | enriched <- enrichr_mod(genes, databases) 100 | 101 | # Tidy output 102 | contrast_enrich <- NULL 103 | for(database in databases) { 104 | temp <- enriched[database][[1]] %>% 105 | mutate(var = database) 106 | contrast_enrich <- rbind(contrast_enrich, temp) 107 | } 108 | 109 | if (nrow(contrast_enrich) != 0){ 110 | contrast_enrich$contrast <- contrast 111 | contrast_enrich$n <- length(genes) 112 | 113 | # Background correction 114 | cat("Background correction... ") 115 | contrast_enrich <- contrast_enrich %>% 116 | mutate(IN = as.numeric(gsub("/.*", "", Overlap)), 117 | OUT = n - IN) %>% 118 | select(-n) %>% 119 | left_join(OUT, by = "Term") %>% 120 | mutate(log_odds = log2((IN * bg_OUT) / (OUT * bg_IN))) 121 | cat("Done.") 122 | 123 | df_enrich <- rbind(df_enrich, contrast_enrich) %>% 124 | mutate(contrast = gsub("_significant", "", contrast)) 125 | } else { 126 | cat("No enough significant genes for enrichment analysis") 127 | } 128 | } 129 | } else { 130 | # Get gene symbols 131 | significant <- row_data %>% 132 | as.data.frame() %>% 133 | select(name, significant) %>% 134 | filter(significant) %>% 135 | mutate(name = gsub("[.].*", "", name)) 136 | 137 | # Run enrichR 138 | genes <- significant$name 139 | enriched <- enrichr_mod(genes, databases) 140 | 141 | # Tidy output 142 | df_enrich <- NULL 143 | for(database in databases) { 144 | temp <- enriched[database][[1]] %>% 145 | mutate(var = database) 146 | df_enrich <- rbind(df_enrich, temp) 147 | } 148 | df_enrich$contrast <- "significant" 149 | df_enrich$n <- length(genes) 150 | 151 | # Background correction 152 | cat("Background correction... ") 153 | df_enrich <- df_enrich %>% 154 | mutate(IN = as.numeric(gsub("/.*", "", Overlap)), 155 | OUT = n - IN) %>% 156 | select(-n) %>% 157 | left_join(OUT, by = "Term") %>% 158 | mutate(log_odds = log2((IN * bg_OUT) / (OUT * bg_IN))) 159 | cat("Done.") 160 | } 161 | 162 | return(df_enrich) 163 | } 164 | 165 | 166 | #gene_names_true<-read_table("R/gene_names.txt",col_names = F) 167 | -------------------------------------------------------------------------------- /R/functions.R: -------------------------------------------------------------------------------- 1 | 2 | matrixplot_modify<-function(data, mapping, pts=list(), smt=list(), ...){ 3 | ggplot(data = data, mapping = mapping, ...) + 4 | do.call(geom_point, pts) + 5 | do.call(geom_smooth, smt) 6 | } 7 | 8 | 9 | LFQ_wrapper<-function(maxquant_data,expdesign){ 10 | 11 | ### Import input MaxQuant proteinGroups.txt file 12 | maxquant_output<- maxquant_data 13 | 14 | ## Optional experimental structure file 15 | exp_design <- expdesign 16 | 17 | ######========= DATA PREPROCESSING =========####### 18 | # Filter proteins by removing 19 | # 1. Reverse sequences 20 | # 2. Potential contaminants 21 | # 3. Proteins only identified by sites 22 | # 4. Proteins identified by single peptide 23 | data <- maxquant_output %>% 24 | dplyr:::filter(Reverse!="+", Only.identified.by.site!="+", Razor...unique.peptides>=2) 25 | 26 | ## Make data unique by combining gene names and protein ids in rows where either of them is absent 27 | data_unique<- DEP:::make_unique(data,"Gene.names","Protein.IDs",delim=";") 28 | 29 | #####======= Gnerating Summerised Experiment Object for differential expression analysis =======###### 30 | 31 | ## get the location of LFQ intensity columns 32 | lfq_columns<-grep("LFQ.", colnames(data_unique)) 33 | 34 | ## Convert the protein dataframe into Summerised experiment object for further analysis 35 | data_se<-DEP:::make_se(data_unique,lfq_columns,exp_design) 36 | 37 | 38 | ## Remove the rows with lot of missing values 39 | ## This is important step and depends of "thr" option 40 | ## If thr=0, it means all the replicates in each sample should have valid values, no missing values is allowed 41 | ## thr=1 means atleast 2 out 3 replicates should have valid values in each sample 42 | data_filter<-DEP:::filter_missval(data_se, thr = 1) 43 | 44 | # meanSdPlot(data_filter) This can be optional 45 | 46 | ######============== Normalisation ===========######## 47 | ## This function uses variance stabilizing transofmation (vsn) function for background correction 48 | ## Not much useful for proteomics datasets 49 | data_norm<-DEP:::normalize_vsn(data_filter) 50 | #meanSdPlot(data_norm)# Can be optional 51 | 52 | ######================ MISSING VALUE IMPUTATION ===============########## 53 | ## As for this dataset protein intensity plot indicated that proteins with missing value have on average low intensities, 54 | ## I choose missing value imputation to be one of MNAR method 55 | ## Following code will impute missing value with algorithm similar to perseus 56 | ## i.e. random values drawn from normal distribution of 1.8 SD apart with the width of 0.3 57 | data_imp_man<-DEP:::impute(data_filter,fun="man",shift=1.8,scale=0.3) 58 | 59 | 60 | ######============= DIFFERENTIAL EXPRESSION ANALYSIS ============########### 61 | 62 | ## This test uses protein-wise linear model with emperical Bayes statistics (used in R package limma) 63 | ## Limma is modified version of t-test and widely used for transcriptomics analysis 64 | ## Number of different options available such as "all", "control" and "man" to compare various conditions 65 | ## "all"- compares all pairwise comparisons 66 | ## "control"- asks to specify the control sample and compares every other sample against control 67 | ## "man"- manually specify which condition to test 68 | 69 | #data_diff_all_contrasts<-test_diff(data_imp_man,type='all') 70 | # If the data has multiple pairwise comparisions we need to generate volcano plot for each comparison 71 | # "comparison" is a character vectors that stores the name of all comparison and captured from 72 | # the message generated by "test_diff" function 73 | # Example of message "Tested_contrasts: "Control_vs_Condition1", "Control_vs_Condition2"" 74 | comparisons<-capture.output(data_diff_all_contrasts<-test_diff(data_imp_man,type='all'),type = "message") 75 | 76 | ## Remove "Tested contrasts:" 77 | comparisons<-gsub(".*: ","",comparisons) 78 | ## Split conditions into character vector 79 | comparisons<-unlist(strsplit(comparisons,",")) 80 | ## Remove leading and trailing spaces 81 | comparisons<-trimws(comparisons) 82 | 83 | ## Mark significantly different proteins 84 | ## Input needed to define the threshold 85 | ## "alpha"- adjusted p-value cutoff 86 | ## "lfc"- log2(Fold Change) cutoff 87 | alpha<- 0.05 88 | lfc<- 1 89 | param<- data.frame(alpha, lfc) 90 | dep<-DEP:::add_rejections(data_diff_all_contrasts,alpha = 0.05,lfc = log2(1)) 91 | 92 | 93 | ## Plot multiple scatterplots 94 | ## First get the LFQ expression data 95 | ## Use ggpairs function from GGally library to generate multiple matrix plots 96 | paired_data<-as.data.frame(assay(dep)) 97 | 98 | 99 | 100 | 101 | ######=============== ENRICHMENT ANALYSIS ============== ########## 102 | ## Protein Set Enrichment Analysis is based on EnrichR 103 | ## Need to specify the databases to perform enrichment test 104 | 105 | ## Gene Ontology Enrichment 106 | # gsea_results_GO <- test_gsea(dep) 107 | 108 | 109 | ## KEGG enrichment 110 | # results_kegg<- test_gsea(dep,databases = c("KEGG_2016")) 111 | 112 | 113 | ####=============== Write Results ===========####### 114 | data_result<-get_results(dep) 115 | # write.csv(data_result,"LFQ_results.csv",row.names = FALSE) 116 | 117 | save.image (file="data/lfq_results.RData") 118 | return(dep) 119 | 120 | } 121 | 122 | # get_pdf_plot<-function(type_of_plot){ 123 | # pdf(paste0(type_of_plot,".pdf",sep="") 124 | # plot(type_of_plot) 125 | # dev.off() 126 | # } 127 | 128 | coef_variation<-function(x){ 129 | coef=sd(x)/mean(x) 130 | } 131 | 132 | #### Plot CVs 133 | 134 | plot_cvs<-function(se) { 135 | 136 | ## backtransform data 137 | untransformed_intensity<- 2^(assay(se)) 138 | exp_design<-colData(se) 139 | 140 | ### merge untransformed to exp design and calculate cvs 141 | 142 | cvs_group<- untransformed_intensity %>% data.frame() %>% 143 | tibble::rownames_to_column() %>% 144 | tidyr::gather("ID", "Intensity", -rowname) %>% 145 | dplyr::left_join(.,data.frame(exp_design), by="ID") %>% 146 | dplyr::group_by(rowname,condition) %>% 147 | dplyr::summarise(cvs=coef_variation(Intensity)) %>% 148 | dplyr::group_by(condition)%>% 149 | dplyr::mutate(condition_median=median(cvs)) 150 | 151 | p1 <- ggplot(cvs_group, aes(cvs, color=condition, fill=condition)) + 152 | geom_histogram(alpha=.5, bins= 20, show.legend = FALSE) + 153 | facet_wrap(~condition) + 154 | geom_vline(aes(xintercept=condition_median, group=condition),color='grey40', 155 | linetype="dashed") + 156 | labs(title= 'Sample Coefficient of Variation', x="Coefficient of Variation", y="Count") + 157 | theme_DEP2() + 158 | theme(plot.title = element_text(hjust = 0.5,face = "bold")) 159 | 160 | p1 +geom_text(aes(x=max(cvs_group$cvs)-0.6, 161 | y=max(ggplot_build(p1)$data[[1]]$ymax*1.1), 162 | label=paste0("Median =",round(condition_median,2)*100,"%",by="")), 163 | show.legend = FALSE, size=4) 164 | 165 | } 166 | 167 | 168 | #### Get individual clusters from heatmap 169 | get_cluster_heatmap <- function(dep, type = c("contrast", "centered"), 170 | kmeans = FALSE, k = 6, 171 | col_limit = 6, indicate = NULL, 172 | clustering_distance = c("euclidean", "maximum", "manhattan", "canberra", 173 | "binary", "minkowski", "pearson", "spearman", "kendall", "gower"), 174 | row_font_size = 6, col_font_size = 10, plot = TRUE, ...) { 175 | 176 | # Show error if inputs are not the required classes 177 | if(is.integer(k)) k <- as.numeric(k) 178 | if(is.integer(col_limit)) col_limit <- as.numeric(col_limit) 179 | if(is.integer(row_font_size)) row_font_size <- as.numeric(row_font_size) 180 | if(is.integer(col_font_size)) col_font_size <- as.numeric(col_font_size) 181 | assertthat::assert_that(inherits(dep, "SummarizedExperiment"), 182 | is.character(type), 183 | is.logical(kmeans), 184 | is.numeric(k), 185 | length(k) == 1, 186 | is.numeric(col_limit), 187 | length(col_limit) == 1, 188 | is.numeric(row_font_size), 189 | length(row_font_size) == 1, 190 | is.numeric(col_font_size), 191 | length(col_font_size) == 1, 192 | is.logical(plot), 193 | length(plot) == 1) 194 | 195 | # Show error if inputs do not contain required columns 196 | type <- match.arg(type) 197 | clustering_distance <- match.arg(clustering_distance) 198 | 199 | # Extract row and col data 200 | row_data <- rowData(dep) 201 | col_data <- colData(dep) %>% 202 | as.data.frame() 203 | 204 | # Show error if inputs do not contain required columns 205 | if(any(!c("label", "condition", "replicate") %in% colnames(col_data))) { 206 | stop(paste0("'label', 'condition' and/or 'replicate' columns are not present in '", 207 | deparse(substitute(dep)), "'"), 208 | call. = FALSE) 209 | } 210 | if(length(grep("_diff", colnames(row_data))) < 1) { 211 | stop(paste0("'[contrast]_diff' columns are not present in '", 212 | deparse(substitute(dep)), 213 | "'.\nRun test_diff() to obtain the required columns."), 214 | call. = FALSE) 215 | } 216 | if(!"significant" %in% colnames(row_data)) { 217 | stop(paste0("'significant' column is not present in '", 218 | deparse(substitute(dep)), 219 | "'.\nRun add_rejections() to obtain the required column."), 220 | call. = FALSE) 221 | } 222 | 223 | # Heatmap annotation 224 | if(!is.null(indicate) & type == "contrast") { 225 | warning("Heatmap annotation only applicable for type = 'centered'", 226 | call. = FALSE) 227 | } 228 | if(!is.null(indicate) & type == "centered") { 229 | ha1 <- get_annotation(dep, indicate) 230 | } else { 231 | ha1 <- NULL 232 | } 233 | 234 | # Filter for significant proteins only 235 | filtered <- dep[row_data$significant, ] 236 | 237 | # Check for missing values 238 | if(any(is.na(assay(filtered)))) { 239 | warning("Missing values in '", deparse(substitute(dep)), "'. ", 240 | "Using clustering_distance = 'gower'", 241 | call. = FALSE) 242 | clustering_distance <- "gower" 243 | obs_NA <- TRUE 244 | } else { 245 | obs_NA <- FALSE 246 | } 247 | 248 | # Get centered intensity values ('centered') 249 | if(type == "centered") { 250 | rowData(filtered)$mean <- rowMeans(assay(filtered), na.rm = TRUE) 251 | df <- assay(filtered) - rowData(filtered)$mean 252 | } 253 | # Get contrast fold changes ('contrast') 254 | if(type == "contrast") { 255 | df <- rowData(filtered) %>% 256 | data.frame() %>% 257 | column_to_rownames(var = "name") %>% 258 | select(dplyr::ends_with("_diff")) 259 | colnames(df) <- 260 | gsub("_diff", "", colnames(df)) %>% 261 | gsub("_vs_", " vs ", .) 262 | } 263 | 264 | # Facultative kmeans clustering 265 | if(kmeans & obs_NA) { 266 | warning("Cannot perform kmeans clustering with missing values", 267 | call. = FALSE) 268 | kmeans <- FALSE 269 | } 270 | if(kmeans & !obs_NA) { 271 | set.seed(1) 272 | df_kmeans <- kmeans(df, k) 273 | if(type == "centered") { 274 | # Order the k-means clusters according to the maximum fold change 275 | # in all samples averaged over the proteins in the cluster 276 | order <- data.frame(df) %>% 277 | cbind(., cluster = df_kmeans$cluster) %>% 278 | dplyr::mutate(row = apply(.[, seq_len(ncol(.) - 1)], 1, function(x) max(x))) %>% 279 | dplyr::group_by(cluster) %>% 280 | dplyr::summarize(index = sum(row)/n()) %>% 281 | dplyr::arrange(desc(index)) %>% 282 | dplyr::pull(cluster) %>% 283 | match(seq_len(k), .) 284 | df_kmeans$cluster <- order[df_kmeans$cluster] 285 | } 286 | if(type == "contrast") { 287 | # Order the k-means clusters according to their average fold change 288 | order <- cbind(df, cluster = df_kmeans$cluster) %>% 289 | dplyr::gather(condition, diff, -cluster) %>% 290 | dplyr::group_by(cluster) %>% 291 | dplyr::summarize(row = mean(diff)) %>% 292 | dplyr::arrange(desc(row)) %>% 293 | dplyr::pull(cluster) %>% 294 | match(seq_len(k), .) 295 | df_kmeans$cluster <- order[df_kmeans$cluster] 296 | } 297 | } 298 | 299 | if(ncol(df) == 1) { 300 | col_clust = FALSE 301 | } else { 302 | col_clust = TRUE 303 | } 304 | if(nrow(df) == 1) { 305 | row_clust = FALSE 306 | } else { 307 | row_clust = TRUE 308 | } 309 | if(clustering_distance == "gower") { 310 | clustering_distance <- function(x) { 311 | dist <- cluster::daisy(x, metric = "gower") 312 | dist[is.na(dist)] <- max(dist, na.rm = TRUE) 313 | return(dist) 314 | } 315 | } 316 | 317 | # Legend info 318 | legend <- ifelse(type == "contrast", 319 | "log2 Fold change", 320 | "log2 Centered intensity") 321 | 322 | # Heatmap 323 | ht1 = Heatmap(df, 324 | col = circlize::colorRamp2( 325 | seq(-col_limit, col_limit, (col_limit/5)), 326 | rev(RColorBrewer::brewer.pal(11, "RdBu"))), 327 | split = if(kmeans) {df_kmeans$cluster} else {NULL}, 328 | cluster_rows = col_clust, 329 | cluster_columns = row_clust, 330 | row_names_side = "left", 331 | column_names_side = "top", 332 | clustering_distance_rows = clustering_distance, 333 | clustering_distance_columns = clustering_distance, 334 | heatmap_legend_param = list(color_bar = "continuous", 335 | legend_direction = "horizontal", 336 | legend_width = unit(5, "cm"), 337 | title_position = "lefttop"), 338 | name = legend, 339 | row_names_gp = gpar(fontsize = row_font_size), 340 | column_names_gp = gpar(fontsize = col_font_size), 341 | top_annotation = ha1, 342 | ...) 343 | # return (row_order(ht1)) 344 | # Return data.frame 345 | p <- draw(ht1, heatmap_legend_side = "top") 346 | row_clusters<- row_order(ht1) 347 | #mat<-as.matrix(df) 348 | 349 | # for (i in 1:length(row_clusters)){ 350 | # if (i==1){ 351 | # clu <-t(t(row.names(ht1[row_clusters[[i]],]))) 352 | # out <-cbind (clu, paste("cluster", i, sep="")) 353 | # colnames(out)<- c("ProteinID", "Cluster") 354 | # } 355 | # else{ 356 | # clu <- t(t(row.names(ht1[row_clusters[[i]],]))) 357 | # clu <- cbind(clu, paste("cluster", i, sep = "")) 358 | # out <- cbind(out, clu) 359 | # } 360 | # } 361 | heatmap_list <- list(p, row_clusters) 362 | return(heatmap_list) 363 | } 364 | 365 | # Internal function to get ComplexHeatmap::HeatmapAnnotation object 366 | get_annotation <- function(dep, indicate) { 367 | assertthat::assert_that( 368 | inherits(dep, "SummarizedExperiment"), 369 | is.character(indicate)) 370 | 371 | # Check indicate columns 372 | col_data <- colData(dep) %>% 373 | as.data.frame() 374 | columns <- colnames(col_data) 375 | if(all(!indicate %in% columns)) { 376 | stop("'", 377 | paste0(indicate, collapse = "' and/or '"), 378 | "' column(s) is/are not present in ", 379 | deparse(substitute(dep)), 380 | ".\nValid columns are: '", 381 | paste(columns, collapse = "', '"), 382 | "'.", 383 | call. = FALSE) 384 | } 385 | if(any(!indicate %in% columns)) { 386 | indicate <- indicate[indicate %in% columns] 387 | warning("Only used the following indicate column(s): '", 388 | paste0(indicate, collapse = "', '"), 389 | "'") 390 | } 391 | 392 | # Get annotation 393 | anno <- dplyr::select(col_data, indicate) 394 | 395 | # Annotation color 396 | names <- colnames(anno) 397 | anno_col <- vector(mode="list", length=length(names)) 398 | names(anno_col) <- names 399 | for(i in names) { 400 | var = anno[[i]] %>% unique() %>% sort() 401 | if(length(var) == 1) 402 | cols <- c("black") 403 | if(length(var) == 2) 404 | cols <- c("orangered", "cornflowerblue") 405 | if(length(var) < 7 & length(var) > 2) 406 | cols <- RColorBrewer::brewer.pal(length(var), "Pastel1") 407 | if(length(var) > 7) 408 | cols <- RColorBrewer::brewer.pal(length(var), "Set3") 409 | names(cols) <- var 410 | anno_col[[i]] <- cols 411 | } 412 | 413 | # HeatmapAnnotation object 414 | ComplexHeatmap::HeatmapAnnotation(df = anno, 415 | col = anno_col, 416 | show_annotation_name = TRUE) 417 | } 418 | 419 | 420 | #### ===== limma BH FDR ===== ##### 421 | 422 | test_limma <- function(se, type = c("control", "all", "manual"), 423 | control = NULL, test = NULL, 424 | design_formula = formula(~ 0 + condition), 425 | paired = FALSE) { 426 | #require("dplyr", "tidyr", "purrr") 427 | 428 | # Show error if inputs are not the required classes 429 | assertthat::assert_that(inherits(se, "SummarizedExperiment"), 430 | is.character(type), 431 | class(design_formula) == "formula") 432 | if (paired == FALSE){ 433 | design_formula <- design_formula 434 | }else{ 435 | design_formula<-formula(~ 0 + condition + replicate) 436 | } 437 | 438 | 439 | # Show error if inputs do not contain required columns 440 | type <- match.arg(type) 441 | 442 | col_data <- colData(se) 443 | raw <- assay(se) 444 | 445 | if(any(!c("name", "ID") %in% colnames(rowData(se)))) { 446 | stop("'name' and/or 'ID' columns are not present in '", 447 | deparse(substitute(se)), 448 | "'\nRun make_unique() and make_se() to obtain the required columns", 449 | call. = FALSE) 450 | } 451 | if(any(!c("label", "condition", "replicate") %in% colnames(col_data))) { 452 | stop("'label', 'condition' and/or 'replicate' columns are not present in '", 453 | deparse(substitute(se)), 454 | "'\nRun make_se() or make_se_parse() to obtain the required columns", 455 | call. = FALSE) 456 | } 457 | if(any(is.na(raw))) { 458 | warning("Missing values in '", deparse(substitute(se)), "'") 459 | } 460 | 461 | if(!is.null(control)) { 462 | # Show error if control input is not valid 463 | assertthat::assert_that(is.character(control), 464 | length(control) == 1) 465 | if(!control %in% unique(col_data$condition)) { 466 | stop("run test_diff() with a valid control.\nValid controls are: '", 467 | paste0(unique(col_data$condition), collapse = "', '"), "'", 468 | call. = FALSE) 469 | } 470 | } 471 | 472 | # variables in formula 473 | variables <- terms.formula(design_formula) %>% 474 | attr(., "variables") %>% 475 | as.character() %>% 476 | .[-1] 477 | 478 | # Throw error if variables are not col_data columns 479 | if(any(!variables %in% colnames(col_data))) { 480 | stop("run make_diff() with an appropriate 'design_formula'") 481 | } 482 | if(variables[1] != "condition") { 483 | stop("first factor of 'design_formula' should be 'condition'") 484 | } 485 | 486 | # Obtain variable factors 487 | for(var in variables) { 488 | temp <- factor(col_data[[var]]) 489 | assign(var, temp) 490 | } 491 | 492 | # Make an appropriate design matrix 493 | design <- model.matrix(design_formula, data = environment()) 494 | colnames(design) <- gsub("condition", "", colnames(design)) 495 | 496 | # Generate contrasts to be tested 497 | # Either make all possible combinations ("all"), 498 | # only the contrasts versus the control sample ("control") or 499 | # use manual contrasts 500 | conditions <- as.character(unique(condition)) 501 | if(type == "all") { 502 | # All possible combinations 503 | cntrst <- apply(utils::combn(conditions, 2), 2, paste, collapse = " - ") 504 | 505 | if(!is.null(control)) { 506 | # Make sure that contrast containing 507 | # the control sample have the control as denominator 508 | flip <- grep(paste("^", control, sep = ""), cntrst) 509 | if(length(flip) >= 1) { 510 | cntrst[flip] <- cntrst[flip] %>% 511 | gsub(paste(control, "- ", sep = " "), "", .) %>% 512 | paste(" - ", control, sep = "") 513 | } 514 | } 515 | 516 | } 517 | if(type == "control") { 518 | # Throw error if no control argument is present 519 | if(is.null(control)) 520 | stop("run test_diff(type = 'control') with a 'control' argument") 521 | 522 | # Make contrasts 523 | cntrst <- paste(conditions[!conditions %in% control], 524 | control, 525 | sep = " - ") 526 | } 527 | if(type == "manual") { 528 | # Throw error if no test argument is present 529 | if(is.null(test)) { 530 | stop("run test_diff(type = 'manual') with a 'test' argument") 531 | } 532 | assertthat::assert_that(is.character(test)) 533 | 534 | if(any(!unlist(strsplit(test, "_vs_")) %in% conditions)) { 535 | stop("run test_diff() with valid contrasts in 'test'", 536 | ".\nValid contrasts should contain combinations of: '", 537 | paste0(conditions, collapse = "', '"), 538 | "', for example '", paste0(conditions[1], "_vs_", conditions[2]), 539 | "'.", call. = FALSE) 540 | } 541 | 542 | cntrst <- gsub("_vs_", " - ", test) 543 | 544 | } 545 | # Print tested contrasts 546 | message("Tested contrasts: ", 547 | paste(gsub(" - ", "_vs_", cntrst), collapse = ", ")) 548 | 549 | # Test for differential expression by empirical Bayes moderation 550 | # of a linear model on the predefined contrasts 551 | fit <- lmFit(raw, design = design) 552 | made_contrasts <- makeContrasts(contrasts = cntrst, levels = design) 553 | contrast_fit <- contrasts.fit(fit, made_contrasts) 554 | 555 | if(any(is.na(raw))) { 556 | for(i in cntrst) { 557 | covariates <- strsplit(i, " - ") %>% unlist 558 | single_contrast <- makeContrasts(contrasts = i, levels = design[, covariates]) 559 | single_contrast_fit <- contrasts.fit(fit[, covariates], single_contrast) 560 | contrast_fit$coefficients[, i] <- single_contrast_fit$coefficients[, 1] 561 | contrast_fit$stdev.unscaled[, i] <- single_contrast_fit$stdev.unscaled[, 1] 562 | } 563 | } 564 | 565 | eB_fit <- eBayes(contrast_fit) 566 | 567 | # function to retrieve the results of 568 | # the differential expression test using 'fdrtool' 569 | retrieve_fun <- function(comp, fit = eB_fit){ 570 | res <- topTable(fit, sort.by = "t", adjust.method="BH", coef = comp, 571 | number = Inf, confint = TRUE) 572 | # res <- res[!is.na(res$t),] 573 | #fdr_res <- fdrtool::fdrtool(res$t, plot = FALSE, verbose = FALSE) 574 | # res$qval <- res$adj.P.Value 575 | #res$lfdr <- fdr_res$lfdr 576 | res$comparison <- rep(comp, dim(res)[1]) 577 | res <- tibble::rownames_to_column(res) 578 | return(res) 579 | } 580 | 581 | #limma_res<- topTable(eB_fit, sort.by = 'B', adjust.method="BH", coef = cntrst, number = Inf, confint = T ) 582 | # limma_res$comparison <- rep(cntrst, dim(limma_res)[1]) 583 | #limma_res <- rownames_to_column(limma_res) 584 | # Retrieve the differential expression test results 585 | limma_res <- purrr::map_df(cntrst, retrieve_fun) 586 | 587 | # Select the logFC, CI and qval variables 588 | table <- limma_res %>% 589 | dplyr::select(rowname, logFC, CI.L, CI.R, P.Value, adj.P.Val, comparison) %>% 590 | dplyr::mutate(comparison = gsub(" - ", "_vs_", comparison)) %>% 591 | tidyr::gather(variable, value, -c(rowname,comparison)) %>% 592 | dplyr::mutate(variable = dplyr::recode(variable, logFC = "diff", P.Value = "p.val", adj.P.Val = "p.adj")) %>% 593 | tidyr::unite(temp, comparison, variable) %>% 594 | tidyr::spread(temp, value) 595 | 596 | # avoid wrong order of similar comparison names 597 | comp_list <- sort(gsub(" - ", "_vs_", cntrst)) 598 | ordered_colNames <- c("rowname" , 599 | lapply(comp_list, function(x) colnames(table)[grep(paste0(x, "(_CI.L|_CI.R|_diff|_p.adj|_p.val)"), colnames(table))]) %>% unlist()) 600 | 601 | table <- table %>% select(all_of(ordered_colNames)) 602 | 603 | rowData(se) <- merge(rowData(se), table, 604 | by.x = "name", by.y = "rowname", all.x = TRUE) 605 | return(se) 606 | #return(table) 607 | } 608 | 609 | get_results_proteins <- function(dep) { 610 | # Show error if inputs are not the required classes 611 | assertthat::assert_that(inherits(dep, "SummarizedExperiment")) 612 | 613 | row_data <- rowData(dep) 614 | # Show error if inputs do not contain required columns 615 | if(any(!c("name", "ID") %in% colnames(row_data))) { 616 | stop("'name' and/or 'ID' columns are not present in '", 617 | deparse(substitute(dep)), 618 | "'\nRun make_unique() and make_se() to obtain the required columns", 619 | call. = FALSE) 620 | } 621 | if(length(grep("_p.adj|_diff", colnames(row_data))) < 1) { 622 | stop("'[contrast]_diff' and/or '[contrast]_p.adj' columns are not present in '", 623 | deparse(substitute(dep)), 624 | "'\nRun test_diff() to obtain the required columns", 625 | call. = FALSE) 626 | } 627 | 628 | # Obtain average protein-centered enrichment values per condition 629 | row_data$mean <- rowMeans(assay(dep), na.rm = TRUE) 630 | centered <- assay(dep) - row_data$mean 631 | centered <- data.frame(centered) %>% 632 | tibble::rownames_to_column() %>% 633 | tidyr::gather(ID, val, -rowname) %>% 634 | dplyr::left_join(., data.frame(colData(dep)), by = "ID") 635 | centered <- dplyr::group_by(centered, rowname, condition) %>% 636 | dplyr::summarize(val = mean(val, na.rm = TRUE)) %>% 637 | dplyr::mutate(val = signif(val, digits = 3)) %>% 638 | tidyr::spread(condition, val) 639 | colnames(centered)[2:ncol(centered)] <- 640 | paste(colnames(centered)[2:ncol(centered)], "_centered", sep = "") 641 | 642 | # Obtain average enrichments of conditions versus the control condition 643 | ratio <- as.data.frame(row_data) %>% 644 | # tibble::column_to_rownames("name") %>% 645 | dplyr::select(dplyr::ends_with("diff")) %>% 646 | signif(., digits = 3) %>% 647 | tibble::rownames_to_column() 648 | colnames(ratio)[2:ncol(ratio)] <- 649 | gsub("_diff", "_log2 fold change", colnames(ratio)[2:ncol(ratio)]) 650 | # df <- left_join(ratio, centered, by = "rowname") 651 | 652 | # Select the adjusted p-values and significance columns 653 | pval <- as.data.frame(row_data) %>% 654 | # tibble::column_to_rownames("name") %>% 655 | dplyr::select(dplyr::ends_with("p.val"), 656 | dplyr::ends_with("p.adj"), 657 | dplyr::ends_with("significant")) %>% 658 | tibble::rownames_to_column() 659 | pval[, grep("p.adj", colnames(pval))] <- 660 | pval[, grep("p.adj", colnames(pval))] %>% 661 | signif(digits = 3) 662 | pval[, grep("p.val", colnames(pval))] <- 663 | pval[, grep("p.val", colnames(pval))] %>% 664 | signif(digits = 3) 665 | 666 | # Join into a results table 667 | ids <- as.data.frame(row_data) %>% dplyr::select(name, ID) 668 | table<-dplyr::left_join(ids,ratio, by=c("name"="rowname")) 669 | table <- dplyr::left_join(table, pval, by = c("name" = "rowname")) 670 | # table <- dplyr::left_join(table, centered, by = c("name" = "rowname")) %>% 671 | # dplyr::arrange(desc(significant)) 672 | table<-as.data.frame(row_data)[, colnames(row_data) %in% c("name", "imputed", "num_NAs", "Protein.names")] %>% 673 | dplyr::left_join(table, ., by = "name") 674 | table<-table %>% dplyr::arrange(desc(significant)) 675 | colnames(table)[1]<-c("Gene Name") 676 | colnames(table)[2]<-c("Protein IDs") 677 | table <- table %>% dplyr::relocate(Protein.names, .after = last_col()) 678 | table <- table %>% dplyr::select(grep("[^Protein.names]",colnames(table)), "Protein.names") 679 | # table$Gene_name<-table$name 680 | return(table) 681 | } 682 | 683 | 684 | 685 | ####################################################### 686 | ## Plot Enrichment Results 687 | ####################################################### 688 | 689 | plot_enrichment <- function(gsea_results, number = 10, alpha = 0.05, 690 | contrasts = NULL, databases = NULL, 691 | nrow = 1,term_size = 8) { 692 | assertthat::assert_that(is.data.frame(gsea_results), 693 | is.numeric(number), 694 | length(number) == 1, 695 | is.numeric(alpha), 696 | length(alpha) == 1, 697 | is.numeric(term_size), 698 | length(term_size) == 1, 699 | is.numeric(nrow), 700 | length(nrow) == 1) 701 | 702 | # Check gsea_results object 703 | if(any(!c("Term", "var", 704 | "contrast","Adjusted.P.value") 705 | %in% colnames(gsea_results))) { 706 | stop("'", deparse(substitute(gsea_results)), 707 | "' does not contain the required columns", 708 | "\nMake sure that HGNC gene symbols are present", 709 | "\n in your 'Gene Names' column of Results table", 710 | call. = FALSE) 711 | } 712 | 713 | no_enrichment_text <- paste("Enrichment could not be performed.\n", 714 | "\nDownload enrichment result table for more details. \n") 715 | 716 | if(!is.null(contrasts)) { 717 | assertthat::assert_that(is.character(contrasts)) 718 | 719 | valid_contrasts <- unique(gsea_results$contrast) 720 | 721 | if(!all(contrasts %in% valid_contrasts)) { 722 | # valid_cntrsts_msg <- paste0("Valid contrasts are: '", 723 | # paste0(valid_contrasts, collapse = "', '"), 724 | # "'") 725 | # stop("Not a valid contrast, please run `plot_gsea()`", 726 | # "with a valid contrast as argument\n", 727 | # valid_cntrsts_msg, 728 | # call. = FALSE) 729 | return(ggplot() + 730 | annotate("text", x = 4, y = 25, size=8, label = no_enrichment_text) + 731 | theme_void() 732 | ) 733 | } 734 | if(!any(contrasts %in% valid_contrasts)) { 735 | contrasts <- contrasts[contrasts %in% valid_contrasts] 736 | message("Not all contrasts found", 737 | "\nPlotting the following contrasts: '", 738 | paste0(contrasts, collapse = "', '"), "'") 739 | } 740 | 741 | gsea_results <- filter(gsea_results, contrast %in% contrasts) 742 | } 743 | if(!is.null(databases)) { 744 | assertthat::assert_that(is.character(databases)) 745 | 746 | valid_databases <- unique(gsea_results$var) 747 | 748 | if(all(!databases %in% valid_databases)) { 749 | valid_cntrsts_msg <- paste0("Valid databases are: '", 750 | paste0(valid_databases, collapse = "', '"), 751 | "'") 752 | stop("Not a valid database, please run `plot_gsea()`", 753 | "with valid databases as argument\n", 754 | valid_cntrsts_msg, 755 | call. = FALSE) 756 | } 757 | if(any(!databases %in% valid_databases)) { 758 | databases <- databases[databases %in% valid_databases] 759 | message("Not all databases found", 760 | "\nPlotting the following databases: '", 761 | paste0(databases, collapse = "', '"), "'") 762 | } 763 | 764 | gsea_results <- filter(gsea_results, var %in% databases) 765 | } 766 | 767 | # Get top enriched gene sets 768 | terms <- gsea_results %>% 769 | dplyr::group_by(contrast, var) %>% 770 | dplyr::filter(Adjusted.P.value <= alpha) %>% 771 | dplyr::arrange(Adjusted.P.value) %>% 772 | dplyr::slice(seq_len(number)) %>% 773 | .$Term 774 | subset <- gsea_results %>% 775 | dplyr::filter(Term %in% terms) %>% 776 | dplyr::arrange(var, Adjusted.P.value) 777 | 778 | subset$Term <- readr::parse_factor(subset$Term, levels = unique(subset$Term)) 779 | subset$var <- readr::parse_factor(subset$var, levels = unique(subset$var)) 780 | 781 | # Plot top enriched gene sets 782 | if (nrow(subset) == 0){ 783 | p <- ggplot() + 784 | annotate("text", x = 4, y = 25, size=8, label = no_enrichment_text) + 785 | theme_void() 786 | } else { 787 | p<-ggplot(subset, aes(Term, 788 | y=-log10(`Adjusted.P.value`))) + 789 | geom_col(aes(fill = log_odds )) + 790 | facet_wrap(~contrast, nrow = nrow) + 791 | coord_flip() + 792 | labs(y = "-Log10 adjusted p-value", 793 | fill = "Log2 odds ratio (vs. current background)") + 794 | theme_bw() + 795 | theme(legend.position = "top", 796 | legend.text = element_text(size = 9)) + 797 | scale_fill_distiller(palette="Spectral") 798 | } 799 | } 800 | 801 | #### ==== get prefix function 802 | 803 | get_prefix <- function(words) { 804 | # Show error if input is not the required class 805 | assertthat::assert_that(is.character(words)) 806 | 807 | # Show error if 'words' contains 1 or less elements 808 | if(length(words) <= 1) { 809 | stop("'words' should contain more than one element") 810 | } 811 | # Show error if 'words' contains NA 812 | if(any(is.na(words))) { 813 | stop("'words' contains NAs") 814 | } 815 | 816 | # Truncate words to smallest name 817 | minlen <- min(nchar(words)) 818 | truncated <- substr(words, 1, minlen) 819 | 820 | # Show error if one of the elements is shorter than one character 821 | if(minlen < 1) { 822 | stop("At least one of the elements is too short") 823 | } 824 | 825 | # Get identifical characters 826 | mat <- data.frame(strsplit(truncated, ""), stringsAsFactors = FALSE) 827 | identical <- apply(mat, 1, function(x) length(unique(x)) == 1) 828 | 829 | # Obtain the longest common prefix 830 | prefix <- as.logical(cumprod(identical)) 831 | paste(mat[prefix, 1], collapse = "") 832 | } 833 | 834 | #### ===== delete prefix function 835 | 836 | delete_prefix <- function(words) { 837 | # Get prefix 838 | prefix <- get_prefix(words) 839 | # Delete prefix from words 840 | gsub(paste0("^", prefix), "", words) 841 | } 842 | 843 | ### Filter missing values use different threshold per conditions/groups 844 | threshold_detect <- function(sample_rep){ 845 | valid_keep <- trunc(sample_rep/2) + 1 846 | threshold <- sample_rep - valid_keep 847 | return(threshold) 848 | } 849 | 850 | keep_function <- function(se){ 851 | # Show error if inputs are not the required classes 852 | 853 | # Show error if inputs do not contain required columns 854 | if(any(!c("name", "ID") %in% colnames(rowData(se, use.names = FALSE)))) { 855 | stop("'name' and/or 'ID' columns are not present in '", 856 | deparse(substitute(se)), 857 | "'\nRun make_unique() and make_se() to obtain the required columns", 858 | call. = FALSE) 859 | } 860 | if(any(!c("label", "condition", "replicate") %in% colnames(colData(se)))) { 861 | stop("'label', 'condition' and/or 'replicate' columns are not present in '", 862 | deparse(substitute(se)), 863 | "'\nRun make_se() or make_se_parse() to obtain the required columns", 864 | call. = FALSE) 865 | } 866 | 867 | # Make assay values binary (1 = valid value) 868 | bin_data <- assay(se) 869 | 870 | # new, removed ref columns from dataset 871 | bin_data <- bin_data %>% data.frame() 872 | idx <- is.na(bin_data) # idx <- is.na(assay(se)) 873 | bin_data[!idx] <- 1 874 | bin_data[idx] <- 0 875 | 876 | # Filter se on the maximum allowed number of 877 | # missing values per condition (defined by thr) 878 | keep <- bin_data %>% 879 | data.frame() %>% 880 | rownames_to_column() %>% 881 | gather(ID, value, -rowname) %>% 882 | left_join(., data.frame(colData(se)), by = "ID") %>% 883 | group_by(rowname, condition) %>% 884 | summarize(miss_val = n() - sum(value)) 885 | return(keep) 886 | } 887 | 888 | filter_missval_new <- function(se,one_condition,exp_df){ 889 | threshold <- exp_df$thr[exp_df$condition==one_condition] %>% unlist() 890 | keep <- keep_function(se) 891 | keep1 <- keep %>% 892 | dplyr::filter(condition == one_condition) %>% 893 | dplyr::filter(miss_val <= threshold) 894 | keep1 <- keep1 %>% 895 | tidyr::spread(condition, miss_val) 896 | se_fltrd <- se[keep1$rowname, ] 897 | return(se_fltrd) 898 | } -------------------------------------------------------------------------------- /R/tests.R: -------------------------------------------------------------------------------- 1 | ### Test if column names are proper in experiment design file 2 | 3 | exp_design_test<-function(exp_design){ 4 | col_names<-colnames(exp_design) 5 | ## 6 | if(!"label" %in% col_names){ 7 | stop(safeError("The column 'label'(case sensitive) is not found in the Experimental Design File")) 8 | } 9 | 10 | else if (!"condition" %in% col_names){ 11 | stop(safeError("The column 'condition' (case sensitive) is not found in the Experimental Design File")) 12 | } 13 | 14 | else if (!"replicate" %in% col_names){ 15 | stop(safeError("The column 'replicate' (case sensitive) is not found in the Experimental Design File")) 16 | } 17 | 18 | } 19 | 20 | ### Test if column names are proper in maxquant ProteinGroups file 21 | maxquant_input_test<-function(maxquant_input){ 22 | col_names<-colnames(maxquant_input) 23 | ## 24 | if(!"Gene.names" %in% col_names){ 25 | stop(safeError("The column 'Gene names' is not found in the MaxQuant proteinGroups File")) 26 | } 27 | 28 | else if (any(grepl("LFQ", col_names))==FALSE){ 29 | stop(safeError("Columns starting with 'LFQ' are not found in the MaxQuant proteinGroups File")) 30 | } 31 | 32 | else if (!"Protein.IDs" %in% col_names){ 33 | stop(safeError("The column 'Protein IDs' is not found in the MaxQuant proteinGroups File")) 34 | } 35 | 36 | else if (!"Reverse" %in% col_names){ 37 | stop(safeError("The column 'Reverse' is not found in the MaxQuant proteinGroups File")) 38 | } 39 | 40 | else if (!"Potential.contaminant" %in% col_names){ 41 | stop(safeError("The column 'Potential contaminant' is not found in the MaxQuant proteinGroups File")) 42 | } 43 | 44 | else if (!"Only.identified.by.site" %in% col_names){ 45 | stop(safeError("The column 'Only identified by site' is not found in the MaxQuant proteinGroups File")) 46 | } 47 | 48 | else if (!"Razor...unique.peptides" %in% col_names){ 49 | stop(safeError("The column 'Razor + unique peptides' is not found in the MaxQuant proteinGroups File")) 50 | } 51 | 52 | else if (!"Protein.names" %in% col_names){ 53 | stop(safeError("The column 'Protein names' is not found in the MaxQuant proteinGroups File")) 54 | } 55 | 56 | } 57 | 58 | 59 | ### Test if experimental design names and LFQ column names match 60 | 61 | test_match_lfq_column_design<-function(unique_data, lfq_columns, exp_design){ 62 | # Show error if inputs are not the required classes 63 | assertthat::assert_that(is.data.frame(unique_data), 64 | is.integer(lfq_columns), 65 | is.data.frame(exp_design)) 66 | 67 | # Show error if inputs do not contain required columns 68 | if(any(!c("name", "ID") %in% colnames(unique_data))) { 69 | stop(safeError("'Gene name' and/or 'Protein ID' columns are not present in 70 | protein groups input file" 71 | )) 72 | } 73 | 74 | if(any(!c("label", "condition", "replicate") %in% colnames(exp_design))) { 75 | stop(safeError("'label', 'condition' and/or 'replicate' columns 76 | are not present in the experimental design")) 77 | } 78 | 79 | if(any(!apply(unique_data[, lfq_columns], 2, is.numeric))) { 80 | stop(safeError("specified 'columns' should be numeric 81 | Run make_se_parse() with the appropriate columns as argument")) 82 | } 83 | 84 | raw <- unique_data[, lfq_columns] 85 | 86 | expdesign <- mutate(exp_design, condition = make.names(condition)) %>% 87 | unite(ID, condition, replicate, remove = FALSE) 88 | rownames(expdesign) <- expdesign$ID 89 | 90 | matched <- match(make.names(delete_prefix(expdesign$label)), 91 | make.names(delete_prefix(colnames(raw)))) 92 | 93 | if(any(is.na(matched))) { 94 | stop(safeError("The labels/'run names' in the experimental design DID NOT match 95 | with lfq column names in maxquants proteinGroups file 96 | Run LFQ-Analyst with correct labels in the experimental design")) 97 | } 98 | } 99 | 100 | 101 | 102 | enrichment_output_test<-function(dep, database){ 103 | significant <- SummarizedExperiment::rowData(dep) %>% 104 | as.data.frame() %>% 105 | dplyr::select(name, significant) %>% 106 | dplyr::filter(significant) %>% 107 | dplyr::mutate(name = gsub("[.].*", "", name)) 108 | test_enrichment_output<-enrichr_mod(significant$name, databases = database) 109 | if(nrow(test_enrichment_output[[1]])==0) 110 | stop(safeError("Enrichment analysis failed. 111 | Please check if the gene names are in Entrenz Gene Symbol format. 112 | (eg. ASM24, MYO6)")) 113 | } 114 | 115 | null_enrichment_test<-function(gsea_result,alpha=0.05){ 116 | gsea_df<-gsea_result %>% group_by(contrast, var) %>% dplyr::filter(Adjusted.P.value <= alpha) 117 | if(nrow(gsea_df)==0){ 118 | stop(safeError("No enriched term found at FDR cutoff 0.05. 119 | Enrichment plot could not be displayed. 120 | However, the results (non-significant hits) can still be accessed 121 | through 'Download table' tab.")) 122 | } 123 | } 124 | 125 | ids_test<-function(filtered_data){ 126 | if("Evidence.IDs" %in% colnames(filtered_data)){ 127 | filtered_data$`Evidence.IDs`<-stringr::str_trunc(as.character(filtered_data$`Evidence.IDs`), 25000) 128 | } 129 | if("MS.MS.IDs" %in% colnames(filtered_data)){ 130 | filtered_data$`MS.MS.IDs`<-stringr::str_trunc(as.character(filtered_data$`MS.MS.IDs`), 25000) 131 | } 132 | 133 | return(filtered_data) 134 | 135 | } 136 | 137 | -------------------------------------------------------------------------------- /R/volcano_function.R: -------------------------------------------------------------------------------- 1 | ## New function for volcano plot 2 | #library(dplyr) 3 | plot_volcano_new <- function(dep, contrast, label_size = 3, 4 | add_names = TRUE, adjusted = FALSE, plot = TRUE) { 5 | # Show error if inputs are not the required classes 6 | if(is.integer(label_size)) label_size <- as.numeric(label_size) 7 | assertthat::assert_that(inherits(dep, "SummarizedExperiment"), 8 | is.character(contrast), 9 | length(contrast) == 1, 10 | is.numeric(label_size), 11 | length(label_size) == 1, 12 | is.logical(add_names), 13 | length(add_names) == 1, 14 | is.logical(adjusted), 15 | length(adjusted) == 1, 16 | is.logical(plot), 17 | length(plot) == 1) 18 | 19 | row_data <- rowData(dep, use.names = FALSE) 20 | 21 | # Show error if inputs do not contain required columns 22 | if(any(!c("name", "ID") %in% colnames(row_data))) { 23 | stop(paste0("'name' and/or 'ID' columns are not present in '", 24 | deparse(substitute(dep)), 25 | "'.\nRun make_unique() to obtain required columns."), 26 | call. = FALSE) 27 | } 28 | if(length(grep("_p.adj|_diff", colnames(row_data))) < 1) { 29 | stop(paste0("'[contrast]_diff' and '[contrast]_p.adj' columns are not present in '", 30 | deparse(substitute(dep)), 31 | "'.\nRun test_diff() to obtain the required columns."), 32 | call. = FALSE) 33 | } 34 | if(length(grep("_significant", colnames(row_data))) < 1) { 35 | stop(paste0("'[contrast]_significant' columns are not present in '", 36 | deparse(substitute(dep)), 37 | "'.\nRun add_rejections() to obtain the required columns."), 38 | call. = FALSE) 39 | } 40 | 41 | # Show error if an unvalid contrast is given 42 | if (length(grep(paste("^",contrast,"_diff", sep = ""), 43 | colnames(row_data))) == 0) { 44 | valid_cntrsts <- row_data %>% 45 | data.frame() %>% 46 | select(ends_with("_diff")) %>% 47 | colnames(.) %>% 48 | gsub("_diff", "", .) 49 | valid_cntrsts_msg <- paste0("Valid contrasts are: '", 50 | paste0(valid_cntrsts, collapse = "', '"), 51 | "'") 52 | stop("Not a valid contrast, please run `plot_volcano()` with a valid contrast as argument\n", 53 | valid_cntrsts_msg, 54 | call. = FALSE) 55 | } 56 | 57 | # Generate a data.frame containing all info for the volcano plot 58 | diff <- grep(paste("^",contrast,"_diff", sep = ""), 59 | colnames(row_data)) 60 | if(adjusted) { 61 | p_values <- grep(paste("^",contrast, "_p.adj", sep = ""), 62 | colnames(row_data)) 63 | } else { 64 | p_values <- grep(paste("^",contrast, "_p.val", sep = ""), 65 | colnames(row_data)) 66 | } 67 | signif <- grep(paste("^",contrast, "_significant", sep = ""), 68 | colnames(row_data)) 69 | df_tmp <- data.frame(diff = row_data[, diff], 70 | p_values = -log10(row_data[, p_values]), 71 | signif = row_data[, signif], 72 | name = row_data$name) 73 | df<- df_tmp %>% data.frame() %>% filter(!is.na(signif)) %>% 74 | arrange(signif) 75 | 76 | name1 <- gsub("_vs_.*", "", contrast) 77 | name2 <- gsub(".*_vs_", "", contrast) 78 | #return(df) 79 | # Plot volcano with or without labels 80 | p <- ggplot(df, aes(diff, p_values)) + 81 | geom_vline(xintercept = 0) + 82 | geom_point(aes(col = signif)) + 83 | geom_text(data = data.frame(), aes(x = c(Inf, -Inf), 84 | y = c(-Inf, -Inf), 85 | hjust = c(1, 0), 86 | vjust = c(-1, -1), 87 | label = c(name1, name2), 88 | size = 5, 89 | fontface = "bold")) + 90 | labs(title = contrast, 91 | x = expression(log[2]~"Fold change")) + 92 | theme_DEP1() + 93 | theme(legend.position = "none") + 94 | scale_color_manual(values = c("TRUE" = "black", "FALSE" = "grey")) 95 | if (add_names) { 96 | p <- p + ggrepel::geom_text_repel(data = filter(df, signif), 97 | aes(label = name), 98 | size = label_size, 99 | box.padding = unit(0.1, 'lines'), 100 | point.padding = unit(0.1, 'lines'), 101 | segment.size = 0.5) 102 | } 103 | if(adjusted) { 104 | p <- p + labs(y = expression(-log[10]~"Adjusted p-value")) 105 | } else { 106 | p <- p + labs(y = expression(-log[10]~"P-value")) 107 | } 108 | if(plot) { 109 | # return(list(p, df)) 110 | # return(df) 111 | return(p) 112 | } else { 113 | df <- df %>% 114 | select(name, diff, p_value, signif) %>% 115 | arrange(desc(x)) 116 | colnames(df)[c(1,2,3)] <- c("protein", "log2_fold_change", "p_value_-log10") 117 | if(adjusted) { 118 | colnames(df)[3] <- "adjusted_p_value_-log10" 119 | } 120 | return(df) 121 | } 122 | } 123 | 124 | 125 | #####====== get_volcano_df =======####### 126 | get_volcano_df <- function(dep, contrast, adjusted = FALSE) { 127 | # Show error if inputs are not the required classes 128 | assertthat::assert_that(inherits(dep, "SummarizedExperiment"), 129 | is.character(contrast), 130 | length(contrast) == 1) 131 | 132 | row_data <- rowData(dep, use.names = FALSE) 133 | 134 | # Show error if inputs do not contain required columns 135 | if(any(!c("name", "ID") %in% colnames(row_data))) { 136 | stop(paste0("'name' and/or 'ID' columns are not present in '", 137 | deparse(substitute(dep)), 138 | "'.\nRun make_unique() to obtain required columns."), 139 | call. = FALSE) 140 | } 141 | if(length(grep("_p.adj|_diff", colnames(row_data))) < 1) { 142 | stop(paste0("'[contrast]_diff' and '[contrast]_p.adj' columns are not present in '", 143 | deparse(substitute(dep)), 144 | "'.\nRun test_diff() to obtain the required columns."), 145 | call. = FALSE) 146 | } 147 | if(length(grep("_significant", colnames(row_data))) < 1) { 148 | stop(paste0("'[contrast]_significant' columns are not present in '", 149 | deparse(substitute(dep)), 150 | "'.\nRun add_rejections() to obtain the required columns."), 151 | call. = FALSE) 152 | } 153 | 154 | # Show error if an unvalid contrast is given 155 | if (length(grep(paste(contrast, "_diff", sep = ""), 156 | colnames(row_data))) == 0) { 157 | valid_cntrsts <- row_data %>% 158 | data.frame() %>% 159 | select(ends_with("_diff")) %>% 160 | colnames(.) %>% 161 | gsub("_diff", "", .) 162 | valid_cntrsts_msg <- paste0("Valid contrasts are: '", 163 | paste0(valid_cntrsts, collapse = "', '"), 164 | "'") 165 | stop("Not a valid contrast, please run `plot_volcano()` with a valid contrast as argument\n", 166 | valid_cntrsts_msg, 167 | call. = FALSE) 168 | } 169 | 170 | # Generate a data.frame containing all info for the volcano plot 171 | diff <- grep(paste(contrast, "_diff", sep = ""), 172 | colnames(row_data)) 173 | if(adjusted) { 174 | p_values <- grep(paste(contrast, "_p.adj", sep = ""), 175 | colnames(row_data)) 176 | } else { 177 | p_values <- grep(paste(contrast, "_p.val", sep = ""), 178 | colnames(row_data)) 179 | } 180 | signif <- grep(paste(contrast, "_significant", sep = ""), 181 | colnames(row_data)) 182 | df_tmp <- data.frame(diff = row_data[, diff], 183 | p_values = -log10(row_data[, p_values]), 184 | signif = row_data[, signif], 185 | name = row_data$name) 186 | df<- df_tmp %>% data.frame() %>% filter(!is.na(signif)) %>% 187 | arrange(signif) 188 | 189 | return(df) 190 | } 191 | 192 | ### Function to plot intensities of individual proteins 193 | plot_protein<-function(dep, protein, type){ 194 | assertthat::assert_that(inherits(dep, "SummarizedExperiment"), 195 | is.character(protein), 196 | is.character(type)) 197 | subset<-dep[protein] 198 | 199 | df_reps <- data.frame(assay(subset)) %>% 200 | rownames_to_column() %>% 201 | gather(ID, val, -rowname) %>% 202 | left_join(., data.frame(colData(subset)), by = "ID") 203 | df_reps$rowname <- parse_factor(as.character(df_reps$rowname), levels = protein) 204 | 205 | df_CI<- df_reps %>% 206 | group_by(condition, rowname) %>% 207 | summarize(mean = mean(val, na.rm = TRUE), 208 | sd = sd(val, na.rm = TRUE), 209 | n = n()) %>% 210 | mutate(error = qnorm(0.975) * sd / sqrt(n), 211 | CI.L = mean - error, 212 | CI.R = mean + error) %>% 213 | as.data.frame() 214 | df_CI$rowname <- parse_factor(as.character(df_CI$rowname), levels = protein) 215 | 216 | if(type=="violin"){ 217 | p<-ggplot(df_reps, aes(condition, val))+ 218 | geom_violin(fill="grey90", scale = "width", 219 | draw_quantiles = 0.5, 220 | trim =TRUE) + 221 | geom_jitter(aes(color = factor(replicate)), 222 | size = 3, position = position_dodge(width=0.3)) + 223 | labs( 224 | y = expression(log[2]~"Intensity"), 225 | col = "Replicates") + 226 | facet_wrap(~rowname) + 227 | scale_color_brewer(palette = "Dark2")+ 228 | theme_DEP1()+ 229 | theme(axis.title.x = element_blank()) 230 | } 231 | 232 | if(type=="boxplot"){ 233 | p<-ggplot(df_reps, aes(condition, val))+ 234 | geom_boxplot()+ 235 | geom_jitter(aes(color = factor(replicate)), 236 | size = 3, position = position_dodge(width=0.3)) + 237 | labs( 238 | y = expression(log[2]~"Intensity"), 239 | col = "Replicates") + 240 | facet_wrap(~rowname) + 241 | scale_color_brewer(palette = "Dark2")+ 242 | theme_DEP1() + 243 | theme(axis.title.x = element_blank()) 244 | } 245 | 246 | if(type=="interaction"){ 247 | p<-ggplot(df_reps, aes(condition, val))+ 248 | geom_point(aes(color = factor(replicate)), 249 | size = 3) + 250 | geom_line(aes(group= factor(replicate), color= factor(replicate)))+ 251 | labs( 252 | y = expression(log[2]~"Intensity"), 253 | col = "Replicates") + 254 | facet_wrap(~rowname) + 255 | scale_color_brewer(palette = "Dark2")+ 256 | theme_DEP1()+ 257 | theme(axis.title.x = element_blank()) 258 | } 259 | 260 | if(type=="dot"){ 261 | p<-ggplot(df_CI, aes(condition, mean))+ 262 | geom_point(data=df_reps, aes(x=condition, y=val, color = factor(replicate)), 263 | size = 3, position= position_dodge(width = 0.2)) + 264 | geom_errorbar(aes(ymin = CI.L, ymax = CI.R), width = 0.2)+ 265 | labs( 266 | y = expression(log[2]~"Intensity"~"(\u00B195% CI)"), 267 | col = "Replicates") + 268 | facet_wrap(~rowname) + 269 | scale_color_brewer(palette = "Dark2")+ 270 | theme_DEP1() + 271 | theme(axis.title.x = element_blank()) 272 | } 273 | 274 | return(p) 275 | } 276 | 277 | plot_volcano_mod <- function(dep, contrast, label_size = 3, 278 | add_names = TRUE, adjusted = FALSE, plot = TRUE) { 279 | # Show error if inputs are not the required classes 280 | if(is.integer(label_size)) label_size <- as.numeric(label_size) 281 | assertthat::assert_that(inherits(dep, "SummarizedExperiment"), 282 | is.character(contrast), 283 | length(contrast) == 1, 284 | is.numeric(label_size), 285 | length(label_size) == 1, 286 | is.logical(add_names), 287 | length(add_names) == 1, 288 | is.logical(adjusted), 289 | length(adjusted) == 1, 290 | is.logical(plot), 291 | length(plot) == 1) 292 | 293 | row_data <- rowData(dep, use.names = FALSE) 294 | 295 | # Show error if inputs do not contain required columns 296 | if(any(!c("name", "ID") %in% colnames(row_data))) { 297 | stop(paste0("'name' and/or 'ID' columns are not present in '", 298 | deparse(substitute(dep)), 299 | "'.\nRun make_unique() to obtain required columns."), 300 | call. = FALSE) 301 | } 302 | if(length(grep("_p.adj|_diff", colnames(row_data))) < 1) { 303 | stop(paste0("'[contrast]_diff' and '[contrast]_p.adj' columns are not present in '", 304 | deparse(substitute(dep)), 305 | "'.\nRun test_diff() to obtain the required columns."), 306 | call. = FALSE) 307 | } 308 | if(length(grep("_significant", colnames(row_data))) < 1) { 309 | stop(paste0("'[contrast]_significant' columns are not present in '", 310 | deparse(substitute(dep)), 311 | "'.\nRun add_rejections() to obtain the required columns."), 312 | call. = FALSE) 313 | } 314 | 315 | # Show error if an unvalid contrast is given 316 | if (length(grep(paste("^",contrast, "_diff", sep = ""), 317 | colnames(row_data))) == 0) { 318 | valid_cntrsts <- row_data %>% 319 | data.frame() %>% 320 | select(ends_with("_diff")) %>% 321 | colnames(.) %>% 322 | gsub("_diff", "", .) 323 | valid_cntrsts_msg <- paste0("Valid contrasts are: '", 324 | paste0(valid_cntrsts, collapse = "', '"), 325 | "'") 326 | stop("Not a valid contrast, please run `plot_volcano()` with a valid contrast as argument\n", 327 | valid_cntrsts_msg, 328 | call. = FALSE) 329 | } 330 | 331 | # Generate a data.frame containing all info for the volcano plot 332 | diff <- grep(paste("^",contrast, "_diff", sep = ""), 333 | colnames(row_data)) 334 | if(adjusted) { 335 | p_values <- grep(paste("^",contrast, "_p.adj", sep = ""), 336 | colnames(row_data)) 337 | } else { 338 | p_values <- grep(paste("^", contrast, "_p.val", sep = ""), 339 | colnames(row_data)) 340 | } 341 | signif <- grep(paste("^",contrast, "_significant", sep = ""), 342 | colnames(row_data)) 343 | df <- data.frame(x = row_data[, diff], 344 | y = -log10(row_data[, p_values]), 345 | significant = row_data[, signif], 346 | name = row_data$name) %>% 347 | filter(!is.na(significant)) %>% 348 | arrange(significant) 349 | 350 | name1 <- gsub("_vs_.*", "", contrast) 351 | name2 <- gsub(".*_vs_", "", contrast) 352 | 353 | # Plot volcano with or without labels 354 | p <- ggplot(df, aes(x, y)) + 355 | geom_vline(xintercept = 0) + 356 | geom_point(aes(col = significant)) + 357 | geom_text(data = data.frame(), aes(x = c(Inf, -Inf), 358 | y = c(-Inf, -Inf), 359 | hjust = c(1, 0), 360 | vjust = c(-1, -1), 361 | label = c(name1, name2), 362 | size = 5, 363 | fontface = "bold")) + 364 | labs(title = contrast, 365 | x = expression(log[2]~"Fold change")) + 366 | theme_DEP1() + 367 | theme(legend.position = "none") + 368 | scale_color_manual(values = c("TRUE" = "black", "FALSE" = "grey")) 369 | if (add_names) { 370 | p <- p + ggrepel::geom_text_repel(data = filter(df, significant), 371 | aes(label = name), 372 | size = label_size, 373 | box.padding = unit(0.1, 'lines'), 374 | point.padding = unit(0.1, 'lines'), 375 | segment.size = 0.5) 376 | } 377 | if(adjusted) { 378 | p <- p + labs(y = expression(-log[10]~"Adjusted p-value")) 379 | } else { 380 | p <- p + labs(y = expression(-log[10]~"P-value")) 381 | } 382 | if(plot) { 383 | return(p) 384 | } else { 385 | df <- df %>% 386 | select(name, x, y, significant) %>% 387 | arrange(desc(x)) 388 | colnames(df)[c(1,2,3)] <- c("protein", "log2_fold_change", "p_value_-log10") 389 | if(adjusted) { 390 | colnames(df)[3] <- "adjusted_p_value_-log10" 391 | } 392 | return(df) 393 | } 394 | } 395 | 396 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) 2 | ![GitHub](https://img.shields.io/github/license/Monashbioinformaticsplatform/LFQ-Analyst?color=brightgreen) 3 | ![R](https://img.shields.io/badge/R-%3E4.2-brightgreen) 4 | 5 | # LFQ-Analyst 6 | A tool for analysing label-free quantitative proteomics dataset https://bioinformatics.erc.monash.edu/apps/LFQ-Analyst/ 7 | 8 | ![LFQ-analyst_pipeline](./www/LFQ_analyst.svg) 9 | 10 | 11 | 12 | 13 | 14 | 15 | ## Motivation 16 | 17 | - Automate downstream statistical analysis of Label free quantitative proteomics data (generated by MaxQuant) 18 | 19 | 20 | ### Input 21 | 22 | - MaxQuant **proteinGroups.txt** file 23 | - An experiment design table (tab separated file) containing three columns ("label", "condition", "replicate") 24 | 25 | #### Data pre-filtering criteria 26 | 27 | - Remove potential contaminants 28 | - Remove reverse sequences 29 | - Remove proteins identified only by sites 30 | - Remove proteins identified/quantified by a single Razor or unique peptide 31 | - Remove observation with high proportion of missing values (intensity values must be present 32 | at least 2 out of three replicates) 33 | 34 | #### Advanced parameters to choose 35 | 36 | - Differencial expression cutoff 37 | - Adjusted p-value cutoff (FDR cutoff on quantitation) 38 | - Log2 fold change cutoff 39 | - Option to choose paired test for matched pair data 40 | - Types of imputation 41 | - A number of missing value imputation options including knn, Minpob etc. 42 | - Type of FDR correction 43 | - Benjamin Hochberg (BH) method 44 | - t-statistics correction: Implemented in 45 | [fdrtools](http://strimmerlab.org/software/fdrtool/) 46 | - Option to include proteins identified/quantified with a single unique peptide. 47 | - Select how many clusters of differentially expressed proteins needed for the heatmap (default is 6) 48 | 49 | 50 | 51 | ### Outputs 52 | 53 | #### Result table 54 | 55 | - **LFQ Results Table:** Includes names (Gene names), Protein Ids, Log 56 | fold changes/ ratios (each pairwise comparisons), Adjusted 57 | *p-values* (applying FDR corrections), *p-values*, Boolean values 58 | for significance, average protein intensity (log transformed) in 59 | each sample. 60 | 61 | #### Result Plots 62 | 1. Interactive volcano plot for each pairwise comparison. 63 | 2. Heatmap of differencially expressed proteins 64 | 3. Protein intensity plots for a single or group of selected proteins from table. 65 | 66 | #### QC Plots 67 | 1. PCA plot (Could move to QC section) 68 | 2. Sample Correlation (pearson correlation) 69 | 3. Sample Coefficient of variations (CVs) 70 | 4. Number of proteins per sample 71 | 5. Sample coverage (overlap of identified proteins across every sample) 72 | 6. Missing value heatmap 73 | 7. Imputation effect on sample distribution 74 | 75 | ### Download options 76 | 77 | **Download tables** (csv format) 78 | 79 | 1. Results: Same as *LFQ Results Table* 80 | 2. Unimputed data matrix: Original protein intensities before 81 | imputation in each sample. 82 | 3. Imputed data matrix: Protein intensities after performing selected 83 | imputation method 84 | 4. Full results: Combined table of all above data outputs i.e. with and 85 | without imputation information, along with fold change and p-values. 86 | 87 | **Download Report** 88 | - A summary report for each analysis that 89 | includes method, summary statistics and plots. 90 | 91 | 92 | ### Local installation 93 | 94 | The current version of LFQ-Analyst is hosted on `R - 4.2.1`. The detailed dependency information can be found in the `dependencies.txt` file. 95 | 96 | Once installed all the dependencies following steps to run the server locally. 97 | 98 | - Using git and Rstudio 99 | ``` 100 | ## Clone the repository 101 | git clone https://github.com/MonashBioinformaticsPlatform/LFQ-Analyst.git 102 | 103 | ## Move to the folder 104 | cd LFQ-Analyst 105 | 106 | ## Inside R console or R studio 107 | > library("shiny") 108 | 109 | > runApp() 110 | 111 | ``` 112 | 113 | - Using Docker 114 | 115 | Install & start Docker demon on your PC 116 | 117 | ``` 118 | ## Option one： 119 | ## Pull LFQ-Analyst image from Docker Hub (From terminal) 120 | > docker pull haileyzhang/lfq-analyst:tagname 121 | 122 | ## Option two： 123 | ## Clone the repository 124 | git clone https://github.com/MonashBioinformaticsPlatform/LFQ-Analyst.git 125 | 126 | ## Move to the folder 127 | cd LFQ-Analyst 128 | 129 | ## Build LFQ-Analyst (Any name after -t) 130 | > docker build -f Dockerfile -t LFQ-Analyst . 131 | 132 | ## Run LFQ-Analyst (From terminal) 133 | 134 | > docker run -p 3838:3838 LFQ-Analyst 135 | 136 | ## Open local interface 137 | 138 | https://localhost:3838/LFQ-Analyst 139 | 140 | 141 | ``` 142 | -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/data/.DS_Store -------------------------------------------------------------------------------- /data/create_example_data.R: -------------------------------------------------------------------------------- 1 | ### create example data rds 2 | 3 | maxquant_output<-read.table("data/proteinGroups.txt",header=TRUE,sep="\t") 4 | 5 | exp_design <- read.table("data/exp_design_p10_0144.txt", 6 | header=TRUE, 7 | sep = "\t", 8 | stringsAsFactors=FALSE) 9 | 10 | save(maxquant_output, exp_design, file = "data/example_data.RData") 11 | 12 | -------------------------------------------------------------------------------- /data/demo_data.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/data/demo_data.RData -------------------------------------------------------------------------------- /data/example_data.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/data/example_data.RData -------------------------------------------------------------------------------- /data/exp_design_p10_0144.txt: -------------------------------------------------------------------------------- 1 | label condition replicate 2 | H1 CD34High 1 3 | H2 CD34High 2 4 | H3 CD34High 3 5 | H4 CD34High 4 6 | L1 CD34Low 1 7 | L2 CD34Low 2 8 | L3 CD34Low 3 9 | L4 CD34Low 4 10 | N1 CD34Neg 1 11 | N2 CD34Neg 2 12 | N3 CD34Neg 3 13 | N4 CD34Neg 4 -------------------------------------------------------------------------------- /data/lfq_results.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/data/lfq_results.RData -------------------------------------------------------------------------------- /dependencies.txt: -------------------------------------------------------------------------------- 1 | R version 4.2.1 (2022-06-23) 2 | Platform: x86_64-pc-linux-gnu (64-bit) 3 | Running under: Ubuntu 16.04.3 LTS 4 | 5 | Matrix products: default 6 | LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib 7 | 8 | locale: 9 | [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C 10 | [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8 11 | [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8 12 | [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C 13 | [9] LC_ADDRESS=C LC_TELEPHONE=C 14 | [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C 15 | 16 | attached base packages: 17 | [1] grid parallel stats4 stats graphics grDevices utils 18 | [8] datasets methods base 19 | 20 | other attached packages: 21 | [1] shinycssloaders_1.0.0 svglite_2.1.0 22 | [3] rjson_0.2.21 httr_1.4.4 23 | [5] ggrepel_0.9.1 DT_0.25 24 | [7] limma_3.52.4 ComplexHeatmap_2.12.1 25 | [9] shinyalert_3.0.0 shinyjs_2.1.0 26 | [11] shinydashboard_0.7.2 testthat_3.1.5 27 | [13] DEP_1.18.0 forcats_0.5.2 28 | [15] stringr_1.4.1 dplyr_1.0.10 29 | [17] purrr_0.3.5 readr_2.1.3 30 | [19] tidyr_1.2.1 tibble_3.1.8 31 | [21] ggplot2_3.3.6 tidyverse_1.3.2 32 | [23] shiny_1.7.2 SummarizedExperiment_1.26.1 33 | [25] Biobase_2.56.0 GenomicRanges_1.48.0 34 | [27] GenomeInfoDb_1.32.4 IRanges_2.30.1 35 | [29] S4Vectors_0.34.0 BiocGenerics_0.42.0 36 | [31] MatrixGenerics_1.8.1 matrixStats_0.62.0 37 | 38 | loaded via a namespace (and not attached): 39 | [1] uuid_1.1-0 readxl_1.4.1 40 | [3] backports_1.4.1 circlize_0.4.15 41 | [5] systemfonts_1.0.4 plyr_1.8.7 42 | [7] gmm_1.7 crosstalk_1.2.0 43 | [9] BiocParallel_1.30.4 digest_0.6.30 44 | [11] foreach_1.5.2 htmltools_0.5.3 45 | [13] magick_2.7.3 fansi_1.0.3 46 | [15] memoise_2.0.1 magrittr_2.0.3 47 | [17] googlesheets4_1.0.1 cluster_2.1.4 48 | [19] doParallel_1.0.17 tzdb_0.3.0 49 | [21] modelr_0.1.9 imputeLCMD_2.1 50 | [23] sandwich_3.0-2 colorspace_2.0-3 51 | [25] rvest_1.0.3 haven_2.5.1 52 | [27] xfun_0.34 crayon_1.5.2 53 | [29] RCurl_1.98-1.9 jsonlite_1.8.2 54 | [31] impute_1.70.0 zoo_1.8-11 55 | [33] iterators_1.0.14 glue_1.6.2 56 | [35] gtable_0.3.1 gargle_1.2.1 57 | [37] zlibbioc_1.42.0 XVector_0.36.0 58 | [39] GetoptLong_1.0.5 DelayedArray_0.22.0 59 | [41] shape_1.4.6 scales_1.2.1 60 | [43] vsn_3.64.0 mvtnorm_1.1-3 61 | [45] DBI_1.1.3 Rcpp_1.0.9 62 | [47] mzR_2.30.0 xtable_1.8-4 63 | [49] clue_0.3-62 preprocessCore_1.58.0 64 | [51] MsCoreUtils_1.8.0 htmlwidgets_1.5.4 65 | [53] RColorBrewer_1.1-3 ellipsis_0.3.2 66 | [55] farver_2.1.1 pkgconfig_2.0.3 67 | [57] XML_3.99-0.11 sass_0.4.2 68 | [59] dbplyr_2.2.1 utf8_1.2.2 69 | [61] labeling_0.4.2 tidyselect_1.2.0 70 | [63] rlang_1.0.6 later_1.3.0 71 | [65] munsell_0.5.0 cellranger_1.1.0 72 | [67] tools_4.2.1 cachem_1.0.6 73 | [69] cli_3.4.1 generics_0.1.3 74 | [71] broom_1.0.1 fdrtool_1.2.17 75 | [73] evaluate_0.17 fastmap_1.1.0 76 | [75] mzID_1.34.0 yaml_2.3.6 77 | [77] knitr_1.40 fs_1.5.2 78 | [79] ncdf4_1.19 mime_0.12 79 | [81] xml2_1.3.3 brio_1.1.3 80 | [83] compiler_4.2.1 rstudioapi_0.14 81 | [85] png_0.1-7 affyio_1.66.0 82 | [87] reprex_2.0.2 bslib_0.4.0 83 | [89] stringi_1.7.8 highr_0.9 84 | [91] MSnbase_2.22.0 lattice_0.20-45 85 | [93] ProtGenerics_1.28.0 Matrix_1.5-1 86 | [95] tmvtnorm_1.5 vctrs_0.4.2 87 | [97] pillar_1.8.1 norm_1.0-10.0 88 | [99] lifecycle_1.0.3 BiocManager_1.30.18 89 | [101] jquerylib_0.1.4 MALDIquant_1.21 90 | [103] GlobalOptions_0.1.2 bitops_1.0-7 91 | [105] httpuv_1.6.6 R6_2.5.1 92 | [107] pcaMethods_1.88.0 affy_1.74.0 93 | [109] renv_0.16.0 promises_1.2.0.1 94 | [111] codetools_0.2-18 MASS_7.3-58.1 95 | [113] assertthat_0.2.1 fontawesome_0.3.0 96 | [115] withr_2.5.0 GenomeInfoDbData_1.2.8 97 | [117] parallel_4.2.1 hms_1.1.2 98 | [119] rmarkdown_2.17 googledrive_2.0.0 99 | [121] Cairo_1.6-0 lubridate_1.8.0 100 | [123] tinytex_0.42 101 | 102 | -------------------------------------------------------------------------------- /docs/CV_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/docs/CV_plot.png -------------------------------------------------------------------------------- /docs/PCA_plot.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | Differential expression analysis of label-free proteomics data 2 | =========================================================== 3 | 4 | # Introduction 5 | ------------ 6 | 7 | This tool is developed to automate downstream statistical analysis of 8 | quantitative proteomics (label-free) datasets generated by MaxQuant. 9 | 10 | # Input 11 | ----- 12 | 13 | - MaxQuant **proteinGroups.txt** file that **Must** contain *Gene 14 | name* and *Protein IDs* column. 15 | - An **experiment design table**: A tab separated file **only** 16 | containing **three** columns namely: "*label*", "*condition*", 17 | "*replicate*". The column names are **case sensitive** 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 |
labelconditionreplicate
H1CD34High1
H2CD34High2
H3CD34High3
H4CD34High4
L1CD34Low1
L2CD34Low2
L3CD34Low3
L4CD34Low4
70 | 71 | **Note:** The label column must match the labels present in **LFQ 72 | Intensity** columns of **proteinGroups.txt** file. For example, include 73 | **"H1"** in label column if **"LFQ Intensity H1"** column present in 74 | your proteinGroups file. 75 | 76 | ## Advanced Options 77 | ---------------- 78 | 79 | #### Significant protein filtering criteria 80 | 81 | - Adjusted p-value cutoff: default is **0.05** 82 | - Log fold change cutoff: default is **1** 83 | 84 | #### Missing value imputation options 85 | 86 | - **Perseus-type:** This method is based on popular missing value 87 | imputation procedure implemented in *Perseus* software by MaxQuant 88 | team. The missing values are replaced by random numbers drawn from a 89 | normal distribution of *1.8* standard deviation down shift and with a 90 | width of *0.3* of each sample. 91 | - **bpca:** Bayesian missing value imputation 92 | - **knn:** Missing values replace by nearest neighbor averaging 93 | technique 94 | - **QRILC:** A missing data imputation method that performs the 95 | imputation of left-censored missing data using random draws from a 96 | truncated distribution with parameters estimated using quantile 97 | regression. 98 | - **MinDet:** Performs the imputation of left-censored missing data 99 | using a deterministic minimal value approach. Considering a 100 | expression data with n samples and p features, for each sample, the 101 | missing entries are replaced with a minimal value observed in that 102 | sample. The minimal value observed is estimated as being the q-th 103 | quantile (default q = 0.01) of the observed values in that sample. 104 | - **MinProb:** Performs the imputation of left-censored missing data 105 | by random draws from a Gaussian distribution centered to a minimal 106 | value. Considering an expression data matrix with n samples and p 107 | features, for each sample, the mean value of the Gaussian 108 | distribution is set to a minimal observed value in that sample. The 109 | minimal value observed is estimated as being the q-th quantile 110 | (default q = 0.01) of the observed values in that sample. The 111 | standard deviation is estimated as the median of the feature 112 | standard deviations. Note that when estimating the standard 113 | deviation of the Gaussian distribution, only the peptides/proteins 114 | which present more than 50% recorded values are considered. 115 | - **min:** Replaces the missing values by the smallest non-missing 116 | value in the data. 117 | - **zero:** Replaces the missing values by **0**. 118 | 119 | #### False Discovery Rate (FDR) correction option 120 | 121 | - Benjamin Hochberg (BH) method 122 | - t-statistics correction: Implemented in 123 | [fdrtools](http://strimmerlab.org/software/fdrtool/) 124 | 125 | #### Data pre-filtering criteria 126 | 127 | Following data-cleaning criteria is applied before performing 128 | differential expression analysis. 129 | 130 | - Remove potential contaminants 131 | - Remove reverse sequences 132 | - Remove proteins identified only by sites 133 | - Remove proteins identified/quantified by a single Razor or unique 134 | peptide 135 | - Remove observation with high proportion of missing values (intensity 136 | values must be present at least 2 out of three replicates) 137 | 138 | #### Differential expression analysis 139 | 140 | Protein-wise linear models combined with empirical Bayes statistics are 141 | used for the differential expression analysis. We use a *Bioconductor* 142 | package *limma* to carry out the analysis using automatically generates 143 | the contrasts from experiment design table provided by the user allowing 144 | the generation of results for all possible comparisons. It also take 145 | into account user defined cutoffs to filter significantly different 146 | proteins. 147 | 148 | Output 149 | ------ 150 | 151 | #### Result table 152 | 153 | - **LFQ Results Table:** Includes names (Gene names), Protein Ids, Log 154 | fold changes/ ratios (each pairwise comparisons), Adjusted 155 | *p-values* (applying FDR corrections), *p-values*, Boolean values 156 | for significance, average protein intensity (log transformed) in 157 | each sample. 158 | 159 | #### Result Plots 160 | 161 | 1. **PCA plot**: A Principal Component Analysis(PCA) is a technique 162 | used to emphasize variation and bring out strong patterns in a 163 | dataset. In brief, the more similar 2 samples are, the closer they 164 | cluster together. Of course, this means that biological replicates 165 | (and in particular technical replicates) should cluster tightly 166 | together. For further information, here are a few links, which 167 | explains the principals of PCAs: 168 | [Info](ttp://ordination.okstate.edu/PCA.htm) and [Basic 169 | introduction](http://setosa.io/ev/principal-component-analysis/) 170 | 171 | ![PCA\_plot](PCA_plot.png) 172 | 173 | 1. **Heatmap**: The heatmap representation gives an overview of all 174 | significant/differentially expressed proteins (rows) in all samples 175 | (columns). This visualization allows the identification of general 176 | trends such as if one sample or replicate is highly different 177 | compared to the others and might be considered as an outlier. 178 | Additionally, the hierarchical clustering of samples (columns) 179 | indicates how related the different samples are and 180 | hierarchical clustering of proteins (rows) identifies similarly 181 | behaving proteins. This analysis divides differentially expressed 182 | proteins into *six* clusters/groups. User also have option to 183 | download protein information from individual cluster. 184 | 185 | ![heatmap](heatmap.png) 186 | 187 | 1. **Volcano plot**: A volcano plot is generated for each pairwise 188 | comparison. It is a graphical visualization by plotting the “Fold 189 | Change (Log2)” on the x-axis versus the –log10 of the “ *p-value*” 190 | on the y-axis. Interesting candidate proteins are located in the 191 | left and right upper quadrant. User can toggle the display name 192 | checkbox to highlight names of differentially expressed proteins or 193 | use 'adjusted *p-value*' as y-axis. Importantly, user can highlight 194 | protein or their interest (colored maroon) by selecting the row from 195 | "**LFQ Results Table**". This highlighted plot can be downloaded 196 | using " *Save Highlighted Plot*" button. 197 | 198 | ![volcano\_plot](volcano_plot.png) 199 | 200 | #### QC plots 201 | 202 | 1. **Sample Correlation Plot**: A correlation matrix is plotted as a 203 | heatmap to visualize the Pearson correlation coefficients between 204 | the different samples. ![corr\_plot](correlation_plot.png) 205 | 2. **Sample CVs Plots**: A plot representing distribution of protein 206 | level coefficient of variation for each condition. Each plot also 207 | contains a vertical line representing median CVs percentage within 208 | that condition. ![cv\_plot](CV_plot.png) 209 | 3. **Protein Numbers**: A bar-plot representing number of proteins 210 | identified and quantified in each sample. 211 | ![proteins](Protein_number.png) 212 | 4. **Sample coverage**: A plot highlighting overlap between identified 213 | proteins across all samples in the experiment. 214 | ![overlap](Protein_overlap.png) 215 | 5. **Normalization**: Two plots representing the effect of variant 216 | stabilizing normalization (vsn) method on protein intensity 217 | distribution in each sample. **Note**: As MaxQuant protein intensity 218 | is already been normalized using MaxLFQ algorithm, further 219 | normalisation is not done during data analysis. This plot is just 220 | for visualisation purpose. 221 | 6. **Missing values- Quant**: To check whether missing values are 222 | biased to lower intense proteins, the densities and cumulative 223 | fractions are plotted for proteins with and without missing values. 224 | ![missing\_quant](missing_quant.png) 225 | 7. **Missing values- Heatmap**: To explore the pattern of missing 226 | values in the data, a heatmap is plotted indicating whether values 227 | are missing (0) or not (1). Only proteins with at least one missing 228 | value are visualized. ![missing\_heatmap](missing_heatmap.png) 229 | 8. **Imputation**: A density plot of protein intensity (log2) 230 | distribution for each condition after and before missing value 231 | imputation being performed. ![imputation](imputation.png) 232 | 9. ***p-value* Histogram**: A histogram of p-value distribution for all 233 | the proteins across all pairwise comparison. 234 | ![histo\_p](pvalue_hist.png) 235 | 236 | ## Download options 237 | 238 | - **Download tables** (csv format) 239 | 240 | 1. Results: Same as *LFQ Results Table* 241 | 2. Unimputed data matrix: Original protein intensities before 242 | imputation in each sample. 243 | 3. Imputed data matrix: Protein intensities after performing selected 244 | imputation method 245 | 4. Full results: Combined table of all above data outputs i.e. with and 246 | without imputation information, along with fold change and p-values. 247 | 248 | - **Download Report** (word format) A summary report document 249 | including some statistics and plots. 250 | 251 | - **Download Plots** (PDF format) A PDF document containing all the 252 | plots generated during the analysis. 253 | -------------------------------------------------------------------------------- /docs/missing_heatmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/docs/missing_heatmap.png -------------------------------------------------------------------------------- /docs/missing_quant.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/docs/missing_quant.png -------------------------------------------------------------------------------- /docs/pvalue_hist.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/docs/pvalue_hist.png -------------------------------------------------------------------------------- /docs/volcano_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/docs/volcano_plot.png -------------------------------------------------------------------------------- /global.R: -------------------------------------------------------------------------------- 1 | VERSION <- "v1.3" 2 | 3 | library("SummarizedExperiment") 4 | library("tidyverse") 5 | library("DEP") 6 | library("testthat") 7 | library("shiny") 8 | library("shinydashboard") 9 | library("shinyjs") 10 | library("shinyalert") 11 | library("ComplexHeatmap") 12 | library("limma") 13 | library("DT") 14 | library("ggrepel") 15 | library("httr") 16 | library("rjson") 17 | library("svglite") 18 | library("shinycssloaders") 19 | library("shiny.info") 20 | source("R/functions.R") 21 | source("R/volcano_function.R") 22 | source("R/tests.R") 23 | source("R/demo_functions.R") 24 | source("R/enrichment_functions.R") 25 | 26 | 27 | 28 | -------------------------------------------------------------------------------- /google_analytics-GA4.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | -------------------------------------------------------------------------------- /google_analytics.js: -------------------------------------------------------------------------------- 1 | // ============ NOTE ================= 2 | // Users of this app are encouraged to keep this analytics script in the app directory 3 | // This will help developer track the usage for future funding persective. 4 | 5 | // Initial Tracking Code 6 | (function(i,s,o,g,r,a,m){ 7 | i['GoogleAnalyticsObject']=r; 8 | i[r]=i[r] || 9 | function(){ 10 | (i[r].q=i[r].q||[]).push(arguments); 11 | },i[r].l=1*new Date(); 12 | a=s.createElement(o), 13 | m=s.getElementsByTagName(o)[0]; 14 | a.async=1; 15 | a.src=g; 16 | m.parentNode.insertBefore(a,m); 17 | })(window,document,'script', 18 | 'https://www.google-analytics.com/analytics.js','ga'); 19 | 20 | ga('create', 'UA-66833365-2', 'auto'); 21 | ga('send', 'pageview'); 22 | 23 | // Event Tracking Code 24 | $(document).on('shiny:inputchanged', function(event) { 25 | if(event.name == 'bins' || event.name == 'col'){ 26 | ga('send', 'event', 'input', 27 | 'updates', event.name, event.value); 28 | } 29 | }); 30 | 31 | // User Tracking Code 32 | $(document).one('shiny:idle', function() { 33 | ga('set','userId', Shiny.user); 34 | }); 35 | -------------------------------------------------------------------------------- /shiny-server.conf: -------------------------------------------------------------------------------- 1 | # Instruct Shiny Server to run applications as the user "shiny" 2 | run_as shiny; 3 | http_keepalive_timeout 600; 4 | preserve_logs true; 5 | 6 | # Define a server that listens on port 3838 7 | server { 8 | listen 3838; 9 | 10 | # Define a location at the base URL 11 | location / { 12 | 13 | # Host the directory of Shiny Apps stored in this directory 14 | site_dir /srv/shiny-server; 15 | 16 | # Log all Shiny output to files in this directory 17 | log_dir /var/log/shiny-server; 18 | 19 | # When a user visits the base URL rather than a particular application, 20 | # an index of the applications available in this directory will be shown. 21 | directory_index on; 22 | disable_websockets off; 23 | app_init_timeout 1800; 24 | app_idle_timeout 1800; 25 | } 26 | } 27 | -------------------------------------------------------------------------------- /ui.R: -------------------------------------------------------------------------------- 1 | # Define UI for data upload app ---- 2 | ui <- function(request){shinyUI( 3 | dashboardPage( 4 | skin = "blue", 5 | dashboardHeader(title = "LFQ-Analyst"), 6 | # disable = TRUE),# Disable title bar 7 | dashboardSidebar( 8 | useShinyalert(), 9 | sidebarMenu( 10 | id="tabs_selected", 11 | convertMenuItem(menuItem('Home', icon=icon("home"), selected = TRUE, tabName = "home"), tabName = "home"), 12 | convertMenuItem(menuItem("Analysis", tabName="analysis", icon=icon("flask"), 13 | #menuItem("Input Files", tabName="file", icon=icon("file"), #selected = TRUE, 14 | fileInput('file1', 15 | 'Upload MaxQuant ProteinGroups.txt', 16 | accept=c('text/csv', 17 | 'text/comma-separated-values,text/plain', 18 | '.csv')), 19 | 20 | fileInput('file2', 21 | 'Upload Experimental Design Matrix', 22 | accept=c('text/csv', 23 | 'text/comma-separated-values,text/plain', 24 | '.csv')), 25 | 26 | 27 | # ), 28 | tags$hr(), 29 | menuItem("Advanced Options",tabName="advanced", icon = icon("cogs"), 30 | numericInput("p", 31 | "Adjusted p-value cutoff", 32 | min = 0.0001, max = 0.1, value = 0.05), 33 | numericInput("lfc", 34 | "Log2 fold change cutoff", 35 | min = 0, max = 10, value = 1), 36 | checkboxInput("paired", 37 | "Paired test", FALSE), 38 | 39 | radioButtons("imputation", 40 | "Imputation type", 41 | choices = c("Perseus-type"="man", MsCoreUtils::imputeMethods())[1:9], 42 | selected = "man"), 43 | 44 | radioButtons("fdr_correction", 45 | "Type of FDR correction", 46 | choices = c("Benjamini Hochberg"="BH", 47 | "t-statistics-based"="fdrtool" 48 | ), selected= "BH"), 49 | checkboxInput("single_peptide", 50 | "Include single peptide identifications", FALSE), 51 | numericInput("k_number", 52 | "Number of clusters in heatmap", 53 | min = 1, max = 20, value = 6) 54 | ), 55 | tags$hr(), 56 | actionButton("analyze", "Start Analysis"), 57 | tags$hr(), 58 | p(a("Example LFQ data", target= "_blank", 59 | href="data/proteinGroups_example.txt", 60 | download="proteinGroups_example.txt")), 61 | p(a("Example Experimental Design file", target= "_blank", 62 | href="data/experimental_design_example.txt", 63 | download="experimental_design_example.txt")) 64 | 65 | #, 66 | #actionButton("load_data", "Load example data") 67 | ), tabName = 'analysis'), 68 | 69 | convertMenuItem(menuItem('Demo', icon=icon("eye"), tabName = "demo"), tabName = "demo"), 70 | convertMenuItem(menuItem('User Guide', icon=icon("question"), 71 | #href = "https://monashbioinformaticsplatform.github.io/LFQ-Analyst/", 72 | tabName = "info"), tabName = "info") 73 | ) 74 | ), # sidebar close 75 | 76 | ################################################################ 77 | ## DASHBOARD BODY 78 | ################################################################ 79 | 80 | dashboardBody( 81 | useShinyjs(), #imp to use shinyjs functions 82 | tags$head(includeScript("google_analytics.js")), 83 | tags$head(includeHTML(("google_analytics-GA4.html"))), 84 | 85 | tags$head( 86 | tags$link(rel = "stylesheet", type = "text/css", href = "./css/custom.css") 87 | ), 88 | 89 | tags$style( 90 | ".box { 91 | border-top: none; 92 | box-shadow: 0 0px 0px rgb(0 0 0 / 10%); 93 | }" 94 | ), 95 | 96 | # Add logo to the body 97 | # tags$img(src="mbpf_logo.jpg",height=50, align="right"), 98 | 99 | ## Add tabItems 100 | # id="body", 101 | tabItems( 102 | 103 | tabItem(tabName = "home", 104 | fluidRow( 105 | box( 106 | title = "Important Updates", 107 | # h3("LFQ-Analyst: An easy-to-use interactive web-platform to analyze and visualize proteomics data 108 | # preprocessed with MaxQuant."), 109 | # tags$hr(), 110 | h4(tags$b("Analyst Suites")," Website available now"), 111 | h5(tags$ul( 112 | "Get access to additional Analyst Apps: ", tags$a(href = "https://analyst-suites.org/", 113 | target = "_blank", "https://analyst-suites.org/") 114 | )), 115 | # br(), 116 | h4(tags$b("Developer Version (recommended)")), 117 | h5(tags$ul( 118 | "Includes more features: ", tags$a(href = "https://analyst-suites.org/apps/lfq-analyst-dev/", 119 | target = "_blank", "LFQ-Analyst(Dev.)") 120 | )), 121 | # br(), 122 | h4(tags$b("Questions/Suggestions/Bug reports: ")), 123 | h5(tags$ul( 124 | "Leave comments to our GitHub: ", tags$a(href = "https://github.com/MonashBioinformaticsPlatform/LFQ-Analyst", 125 | target = "_blank", "here") 126 | )), 127 | width = 12, 128 | solidHeader = TRUE, 129 | status = "primary" 130 | ), # box 1 closed 131 | box( 132 | title = "Overview", 133 | h3("LFQ-Analyst: An easy-to-use interactive web-platform to analyze and visualize proteomics data 134 | preprocessed with MaxQuant."), 135 | p("LFQ-Analyst is an easy-to-use, interactive web application developed to perform 136 | differential expression analysis with “one click” and to visualize label-free quantitative proteomic 137 | datasets preprocessed with MaxQuant. LFQ-Analyst provides a wealth of user-analytic features 138 | and offers numerous publication-quality result output graphics and tables to facilitate statistical 139 | and exploratory analysis of label-free quantitative datasets. "), 140 | br(), 141 | HTML('
'), 142 | br(), 143 | h4("Sidebar tabs"), 144 | tags$ul( 145 | tags$li(tags$b("Analysis: "),"perform your own analysis"), 146 | tags$li(tags$b("Demo: "),"familiarise yourself with LFQ-Analyst by browsing through pre-analysed results"), 147 | tags$li(tags$b("User Guide: "), "download an in-depth manual") 148 | ), 149 | width = 12, 150 | solidHeader = TRUE, 151 | collapsed = TRUE, 152 | # collapsible = TRUE, 153 | status = "success" 154 | )#box 2 closed 155 | ) #fluidrow close 156 | ), # home tab close 157 | tabItem(tabName = "analysis", 158 | div(id="quickstart_info", 159 | fluidPage( 160 | box( 161 | title = "Getting Started", 162 | h3(tags$b(span("Quick Start", style="text-decoration:underline"))), 163 | tags$ul( 164 | tags$li("Upload your ", tags$b("proteinGroups.txt "), "generated by MaxQuant."), 165 | tags$li("Upload your ", tags$b(" experimental design "),"table. "), 166 | 167 | tags$li(tags$b("Optional: "),"Adjust the p-value cut-off, the log2 fold change cut-off, 168 | the imputation type, FDR correction method and/or number of clusters in heatmap 169 | in the", tags$b("Advanced Options")), 170 | tags$li("Press ", tags$b("'Start Analysis' ")), 171 | tags$li(tags$b("Hint: "), " Use the ", tags$b("User Guide ")," tab for a detailed explanation of inputs, 172 | advanced options and outputs"), 173 | tags$li(tags$b("Note: "), " The experimental design file is not the" , tags$b("'mqpar.xml' "),"file 174 | from MaxQuant. Use the example file template provided.") 175 | ), 176 | br(), 177 | HTML('
'), 178 | width = 12, 179 | solidHeader = TRUE, 180 | status = "danger" 181 | ) 182 | ) 183 | ), # QUICKSTART INFO CLOSE 184 | shinyjs::hidden(div(id="downloadbox", 185 | fluidRow( 186 | box( 187 | column(6,uiOutput("downloadTable"),offset = 1), 188 | column(4,uiOutput("downloadButton")), # make the button on same line 189 | width = 4), 190 | 191 | infoBoxOutput("significantBox",width = 4), 192 | box( 193 | column(5,uiOutput("downloadreport")), # offset for dist between buttons 194 | #tags$br(), 195 | #column(5,uiOutput('downloadPlots')), 196 | width = 4 197 | ) 198 | ))), #close div and first row 199 | 200 | # align save button 201 | tags$style(type='text/css', "#downloadButton { width:100%; margin-top: 25px;}"), 202 | tags$style(type='text/css', "#downloadreport { width:100%; vertical-align- middle; margin-top: 25px; 203 | margin-bottom: 25px;}"), 204 | #tags$style(type='text/css', "#downloadPlots { width:100%; margin-top: 25px;}"), 205 | 206 | tags$br(), # Blank lines 207 | tags$br(), 208 | 209 | ## Data table and result plots box 210 | fluidRow( 211 | shinyjs::hidden(div(id="results_tab", 212 | box( 213 | title = "LFQ Results Table", 214 | DT::dataTableOutput("contents"), 215 | # actionButton("clear", "Deselect Rows"), 216 | actionButton("original", "Refresh Table"), 217 | width = 6, 218 | status = "success", 219 | #color="" 220 | solidHeader = TRUE 221 | ), 222 | # column( 223 | box( 224 | width= 6, 225 | collapsible = TRUE, 226 | #status="primary", 227 | #solidHeader=TRUE, 228 | tabBox( 229 | title = "Result Plots", 230 | width = 12, 231 | tabPanel(title = "Volcano plot", 232 | fluidRow( 233 | box(uiOutput("volcano_cntrst"), width = 5), 234 | box(numericInput("fontsize", 235 | "Font size", 236 | min = 0, max = 8, value = 4), 237 | width = 3), 238 | box(checkboxInput("check_names", 239 | "Display names", 240 | value = FALSE), 241 | checkboxInput("p_adj", 242 | "Adjusted p values", 243 | value = FALSE), 244 | width = 4), 245 | tags$p("Select protein from LFQ Results Table to highlight on the plot OR 246 | drag the mouse on plot to show expression of proteins in Table") 247 | #Add text line 248 | # tags$p("OR"), 249 | # tags$p("Drag the mouse on plot to show expression of proteins in Table") 250 | ), 251 | 252 | fluidRow( 253 | plotOutput("volcano", height = 600, 254 | # hover = "protein_hover"), 255 | #), 256 | # click = "protein_click"), 257 | brush = "protein_brush", 258 | click = "protein_click"), 259 | downloadButton('downloadVolcano', 'Save Highlighted Plot'), 260 | actionButton("resetPlot", "Clear Selection") 261 | #)), 262 | )), 263 | tabPanel(title= "Heatmap", 264 | fluidRow( 265 | plotOutput("heatmap", height = 600) 266 | ), 267 | fluidRow( 268 | box(numericInput("cluster_number", 269 | "Cluster to download", 270 | min=1, max=6, value = 1), width = 6), 271 | box(downloadButton('downloadCluster',"Save Cluster"), 272 | downloadButton('download_hm_svg', "Save svg"), 273 | width = 5), 274 | # align save button 275 | tags$style(type='text/css', "#downloadCluster {margin-top: 25px;}"), 276 | tags$style(type='text/css', "#download_hm_svg {margin-top: 25px;}") 277 | ) 278 | ), 279 | tabPanel(title = "Protein Plot", 280 | fluidRow( 281 | box(radioButtons("type", 282 | "Plot type", 283 | choices = c("Box Plot"= "boxplot", 284 | "Violin Plot"="violin", 285 | "Interaction Plot"= "interaction", 286 | "Intensity Plot"="dot" 287 | ), 288 | selected = "boxplot", 289 | inline = TRUE), 290 | width = 12 291 | ), 292 | tags$p("Select one or more rows from LFQ Results Table to plot individual 293 | protein intesities across conditions and replicates") 294 | ), 295 | fluidRow( 296 | plotOutput("protein_plot"), 297 | downloadButton('downloadProtein', 'Download Plot') 298 | ) 299 | ) 300 | # verbatimTextOutput("protein_info")) 301 | ) 302 | ) # box or column end 303 | ))), 304 | 305 | ## QC Box 306 | fluidRow( 307 | shinyjs::hidden(div(id="qc_tab", 308 | column( 309 | width=6, 310 | tabBox(title = "QC Plots", width = 12, 311 | tabPanel(title = "PCA Plot", 312 | plotOutput("pca_plot", height=600), 313 | downloadButton('download_pca_svg', "Save svg") 314 | ), 315 | tabPanel(title="Sample Correlation", 316 | plotOutput("sample_corr", height = 600), 317 | downloadButton('download_corr_svg', "Save svg") 318 | ), 319 | tabPanel(title= "Sample CVs", 320 | plotOutput("sample_cvs", height = 600), 321 | downloadButton('download_cvs_svg', "Save svg") 322 | ), 323 | tabPanel(title = "Protein Numbers", 324 | plotOutput("numbers", height = 600), 325 | downloadButton('download_num_svg', "Save svg") 326 | ), 327 | 328 | tabPanel(title = "Sample coverage", 329 | plotOutput("coverage", height = 600), 330 | downloadButton('download_cov_svg', "Save svg") 331 | ), 332 | tabPanel(title = "Normalization", 333 | plotOutput("norm", height = 600), 334 | downloadButton('download_norm_svg', "Save svg") 335 | ), 336 | # tabPanel(title = "Missing values - Quant", 337 | # plotOutput("detect", height = 600) 338 | # ), 339 | tabPanel(title = "Missing values - Heatmap", 340 | plotOutput("missval", height = 600), 341 | downloadButton('download_missval_svg', "Save svg") 342 | ), 343 | tabPanel(title = "Imputation", 344 | plotOutput("imputation", height = 600), 345 | downloadButton('download_imp_svg', "Save svg") 346 | )#, 347 | # tabPanel(title = "p-value Histogram", 348 | # plotOutput("p_hist", height = 600) 349 | # ) 350 | ) # Tab box close 351 | ), 352 | column( 353 | width=6, 354 | tabBox(title = "Enrichment", width = 12, 355 | tabPanel(title="Gene Ontology", 356 | fluidRow( 357 | column(6, 358 | uiOutput("contrast")), 359 | column(6, 360 | selectInput("go_database", "GO database:", 361 | c("Molecular Function"="GO_Molecular_Function_2021", 362 | "Cellular Component"="GO_Cellular_Component_2021", 363 | "Biological Process"="GO_Biological_Process_2021")) 364 | ), 365 | column(12,actionButton("go_analysis", "Run Enrichment")), 366 | column(12, 367 | box(width = 12,uiOutput("spinner_go"),height = 400) 368 | ), 369 | column(12,downloadButton('downloadGO', 'Download Table')) 370 | ) 371 | ), 372 | tabPanel(title= "Pathway enrichment", 373 | fluidRow( 374 | column(6, 375 | uiOutput("contrast_1")), 376 | column(6, 377 | selectInput("pathway_database", "Pathway database:", 378 | c("KEGG"="KEGG_2021_Human", 379 | "Reactome"="Reactome_2022")) 380 | ), 381 | column(12,actionButton("pathway_analysis", "Run Enrichment")), 382 | column(12, 383 | box(width = 12,uiOutput("spinner_pa"),height = 400) 384 | ), 385 | column(12,downloadButton('downloadPA', 'Download Table')) 386 | ) 387 | ) 388 | 389 | ) # Tab box close 390 | ) 391 | ))) # fluidrow qc close 392 | 393 | 394 | #bookmarkButton() 395 | ), #analysis tab close 396 | 397 | tabItem(tabName = "info", 398 | fluidRow( 399 | box( 400 | title = "User Guide", 401 | h3("LFQ-Analyst: Manual"), 402 | # div(p(HTML(paste0('A detail online user manual can be accessed ', 403 | # a(href = 'https://monashbioinformaticsplatform.github.io/LFQ-Analyst/', 404 | # target='_blank', 'here'))))), 405 | div(p(HTML(paste0("A detailed user manual can be accessed", 406 | a(href = './LFQ-Analyst_manual.pdf', 407 | target='_blank', tags$b("here.")))))), 408 | h4("Contact Us"), 409 | p("For any feedback or question regarding LFQ-Analyst, please contact the 410 | Monash Proteomics and Metabolomics Platform:"), 411 | tags$ul( 412 | # tags$li("Anup Shah: anup.shah(at)monash.edu"), 413 | tags$li("Ralf Schittenhelm: ralf.schittenhelm(at)monash.edu"), 414 | tags$li("Haijian Zhang: hailey.zhang1(at)monash.edu") 415 | ), 416 | 417 | h4("How to Cite LFQ-Analyst?"), 418 | 419 | div(p(HTML(paste0("Please Cite: Shah AD, Goode RJA, Huang C, Powell DR, Schittenhelm RB. 420 | LFQ-Analyst: An easy-to-use interactive web-platform to analyze and 421 | visualize proteomics data preprocessed with MaxQuant. DOI:", 422 | a(href = 'https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00496', 423 | target='_blank', tags$b("10.1021/acs.jproteome.9b00496")))))), 424 | 425 | 426 | h4("News and Updates"), 427 | 428 | tags$ul( 429 | tags$li("27-12-2021: LFQ-Analyst being accessed by more than 5000 users worldwide"), 430 | tags$li("30-09-2021: LFQ-Analyst being accessed by more than 4000 users worldwide"), 431 | tags$li("03-05-2021: LFQ-Analyst being accessed by more than 3000 users worldwide"), 432 | tags$li("24-02-2021: Correlation plot now use all protein expression data"), 433 | tags$li("25-11-2020: LFQ-Analyst being accessed by more than 2000 users worldwide"), 434 | tags$li("07-04-2020: LFQ-Analyst being accessed by more than 1000 users worldwide"), 435 | tags$li("03-01-2020: LFQ-Analyst manuscript published in volume 19 of JPR"), 436 | tags$li("28-10-2019: LFQ-Analyst paper published online in Journal of Proteome Research (JPR)"), 437 | tags$li("02-10-2019: Svg figures download feature added"), 438 | tags$li("09-09-2019: Paired test support added"), 439 | tags$li("09-09-2019: Option to include single peptide observations in the analysis"), 440 | tags$li("19-02-2019: LFQ-Analyst made public") 441 | ), 442 | width = 12, 443 | solidHeader = TRUE, 444 | status = "primary" 445 | ) #includeMarkdown("www/Info.md") 446 | ) 447 | ),# info tab close 448 | 449 | tabItem(tabName = "demo", 450 | div(id="downloadbox_dm", 451 | fluidRow( 452 | box( 453 | column(6,uiOutput("downloadTable_dm"),offset = 1), 454 | column(4,uiOutput("downloadButton_dm")), # make the button on same line 455 | width = 4), 456 | 457 | infoBoxOutput("significantBox_dm",width = 4), 458 | box( 459 | column(5,uiOutput("downloadreport_dm")), # offset for dist between buttons 460 | #tags$br(), 461 | # column(5,uiOutput('downloadPlots_dm')), 462 | width = 4 463 | ) 464 | )), #close div and first row 465 | 466 | # align save button 467 | tags$style(type='text/css', "#downloadButton_dm { width:100%; margin-top: 25px;}"), 468 | tags$style(type='text/css', "#downloadreport_dm { width:100%; margin-top: 25px; margin-bottom: 25px;}"), 469 | # tags$style(type='text/css', "#downloadPlots_dm { width:100%; margin-top: 25px;}"), 470 | 471 | tags$br(), # Blank lines 472 | tags$br(), 473 | 474 | ## Data table and result plots box 475 | fluidRow( 476 | div(id="results_tab_dm", 477 | box( 478 | title = "LFQ Results Table", 479 | DT::dataTableOutput("contents_dm"), 480 | # actionButton("clear", "Deselect Rows"), 481 | actionButton("original_dm", "Refresh Table"), 482 | width = 6, 483 | status = "success", 484 | #color="" 485 | solidHeader = TRUE 486 | ), 487 | # column( 488 | box( 489 | width= 6, 490 | collapsible = TRUE, 491 | #status="primary", 492 | #solidHeader=TRUE, 493 | tabBox( 494 | title = "Result Plots", 495 | width = 12, 496 | tabPanel(title = "Volcano plot", 497 | fluidRow( 498 | box(uiOutput("volcano_cntrst_dm"), width = 5), 499 | box(numericInput("fontsize_dm", 500 | "Font size", 501 | min = 0, max = 8, value = 4), 502 | width = 3), 503 | box(checkboxInput("check_names_dm", 504 | "Display names", 505 | value = FALSE), 506 | checkboxInput("p_adj_dm", 507 | "Adjusted p values", 508 | value = FALSE), 509 | width = 4), 510 | tags$p("Select protein from LFQ Results Table to highlight on the plot OR 511 | drag the mouse on plot to show expression of proteins in Table") 512 | #Add text line 513 | # tags$p("OR"), 514 | # tags$p("Drag the mouse on plot to show expression of proteins in Table") 515 | ), 516 | 517 | fluidRow( 518 | plotOutput("volcano_dm", height = 600, 519 | # hover = "protein_hover"), 520 | #), 521 | # click = "protein_click"), 522 | brush = "protein_brush_dm", 523 | click = "protein_click_dm"), 524 | downloadButton('downloadVolcano_dm', 'Save Highlighted Plot'), 525 | actionButton("resetPlot_dm", "Clear Selection") 526 | #)), 527 | )), 528 | tabPanel(title= "Heatmap", 529 | fluidRow( 530 | plotOutput("heatmap_dm", height = 600) 531 | ), 532 | fluidRow( 533 | box(numericInput("cluster_number_dm", 534 | "Cluster to download", 535 | min=1, max=6, value = 1), width = 6), 536 | box(downloadButton('downloadCluster_dm',"Save Cluster"),width = 3), 537 | # align save button 538 | tags$style(type='text/css', "#downloadCluster_dm {margin-top: 25px;}"), 539 | ) 540 | ), 541 | tabPanel(title = "Protein Plot", 542 | fluidRow( 543 | box(radioButtons("type_dm", 544 | "Plot type", 545 | choices = c("Box Plot"= "boxplot", 546 | "Violin Plot"="violin", 547 | "Interaction Plot"= "interaction", 548 | "Intensity Plot"="dot" 549 | ), 550 | selected = "boxplot", 551 | inline = TRUE), 552 | width = 12 553 | ), 554 | tags$p("Select one or more rows from LFQ Results Table to plot individual 555 | protein intesities across conditions and replicates") 556 | ), 557 | fluidRow( 558 | plotOutput("protein_plot_dm"), 559 | downloadButton('downloadProtein_dm', 'Download Plot') 560 | ) 561 | ) 562 | # verbatimTextOutput("protein_info")) 563 | ) 564 | ) # box or column end 565 | )), 566 | 567 | ## QC Box 568 | fluidRow( 569 | div(id="qc_tab_dm", 570 | column( 571 | width=6, 572 | tabBox(title = "QC Plots", width = 12, 573 | tabPanel(title = "PCA Plot", 574 | plotOutput("pca_plot_dm"), height=600), 575 | tabPanel(title="Sample Correlation", 576 | plotOutput("sample_corr_dm", height = 600) 577 | ), 578 | tabPanel(title= "Sample CVs", 579 | plotOutput("sample_cvs_dm", height = 600) 580 | ), 581 | tabPanel(title = "Protein Numbers", 582 | plotOutput("numbers_dm", height = 600) 583 | ), 584 | 585 | tabPanel(title = "Sample coverage", 586 | plotOutput("coverage_dm", height = 600) 587 | ), 588 | tabPanel(title = "Normalization", 589 | plotOutput("norm_dm", height = 600) 590 | ), 591 | # tabPanel(title = "Missing values - Quant", 592 | # plotOutput("detect_dm", height = 600) 593 | # ), 594 | tabPanel(title = "Missing values - Heatmap", 595 | plotOutput("missval_dm", height = 600) 596 | ), 597 | tabPanel(title = "Imputation", 598 | plotOutput("imputation_dm", height = 600) 599 | )#, 600 | # tabPanel(title = "p-value Histogram", 601 | # plotOutput("p_hist_dm", height = 600) 602 | # ) 603 | ) # Tab box close 604 | ), 605 | column( 606 | width=6, 607 | tabBox(title = "Enrichment", width = 12, 608 | tabPanel(title="Gene Ontology", 609 | fluidRow( 610 | column(6, 611 | uiOutput("contrast_dm") 612 | ), 613 | column(6, 614 | selectInput("go_database_dm", "GO database:", 615 | c("Molecular Function"="GO_Molecular_Function_2021", 616 | "Cellular Component"="GO_Cellular_Component_2021", 617 | "Biological Process"="GO_Biological_Process_2021")) 618 | ), 619 | column(12,actionButton("go_analysis_dm", "Run Enrichment")), 620 | column(12, 621 | box(width = 12,uiOutput("spinner_go_dm"),height = 400) 622 | ), 623 | column(12,downloadButton('downloadGO_dm', 'Download Table')) 624 | ) 625 | 626 | ), 627 | tabPanel(title= "Pathway enrichment", 628 | fluidRow( 629 | column(6, 630 | uiOutput("contrast_dm_1") 631 | ), 632 | column(6, 633 | selectInput("pathway_database_dm", "Pathway database:", 634 | c("KEGG"="KEGG_2021_Human", 635 | "Reactome"="Reactome_2022")) 636 | ), 637 | column(12,actionButton("pathway_analysis_dm", "Run Enrichment")), 638 | column(12, 639 | box(width = 12,uiOutput("spinner_pa_dm"),height = 400) 640 | ), 641 | column(12,downloadButton('downloadPA_dm', 'Download Table')) 642 | ) 643 | ) #### Tab demo closed 644 | 645 | ) # Tab box close 646 | ) 647 | )) # fluidrow qc close 648 | # tabItems( 649 | ) # Tab items close 650 | 651 | #)# info tab lose 652 | # )#tabitems close 653 | ), 654 | tags$footer( 655 | tags$p("Supported by: Monash Proteomics and Metabolomics Platform & Monash Bioinformatics Platform, 656 | Monash University"), 657 | align = "right"), # Dasbboardbody close 658 | shiny.info::version(position = "bottom right") 659 | 660 | ) #Dashboard page close 661 | ) 662 | )#Shiny U Close 663 | } 664 | -------------------------------------------------------------------------------- /www/CV_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MonashBioinformaticsPlatform/LFQ-Analyst/65a12a303483fddbd3f80e6a4fe537e3ca960823/www/CV_plot.png -------------------------------------------------------------------------------- /www/Info.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "LFQ Analysis" 3 | output: md_document 4 | keep_md: true 5 | #runtime: shiny 6 | # rmdformats::material: 7 | # highlight: kate 8 | # output: 9 | # theme: cayman 10 | # prettydoc::html_pretty: 11 | # highlight: github 12 | # keep_md: true 13 | # editor_options: 14 | # chunk_output_type: inline 15 | --- 16 | 17 | ```{r knitr_init, echo=FALSE, cache=FALSE} 18 | library(knitr) 19 | library(rmdformats) 20 | 21 | ## Global options 22 | options(max.print="50") 23 | opts_chunk$set(echo=FALSE, 24 | cache=TRUE, 25 | prompt=FALSE, 26 | tidy=TRUE, 27 | comment=NA, 28 | message=FALSE, 29 | warning=FALSE) 30 | opts_knit$set(width=50) 31 | ``` 32 | 33 | # Differential experssion analysis label free proteomics data 34 | 35 | ## Introduction 36 | This tool is developed to automate downstream statistical analysis of quantitative proteomics (label-free) datasets generated by MaxQuant. 37 | 38 | ## Input 39 | - MaxQuant **proteinGroups.txt** file that **Must** contain *Gene name* and _Protein IDs_ column. 40 | - An **experiment design table**: A tab separated file **only** containing **three** columns namely: "_label_", "_condition_", "_replicate_". The column names are **case sensitive** 41 | 42 | 43 | ```{r exp_design, echo=FALSE, results='asis'} 44 | exp_design<-read.delim("../data/exp_design_p10_0144.txt", 45 | sep = "\t") 46 | kable(exp_design[1:8,]) 47 | ``` 48 | 49 | |label |condition | replicate| 50 | |:--------|:------------|---------:| 51 | |H1 |CD34High | 1| 52 | |H2 |CD34High | 2| 53 | |H3 |CD34High | 3| 54 | |H4 |CD34High | 4| 55 | |L1 |CD34Low | 1| 56 | |L2 |CD34Low | 2| 57 | |L3 |CD34Low | 3| 58 | |L4 |CD34Low | 4| 59 | 60 | 61 | 62 | **Note:** The label column must match the lables present in **LFQ Intensity** columns of **proteinGroups.txt** file. For example, include **"H1"** in lable column if **"LFQ Intensity H1"** column present in your proteinGroups file. 63 | 64 | ## Advanced Options 65 | 66 | #### Significant protein filtering criteria 67 | - Adjusted p-value cutoff: default is **0.05** 68 | - Log fold change cutoff: default is **1** 69 | 70 | #### Missing value imputation options 71 | - **Perseus-type:** This method is based on popular missing value imputation procedure implemented in _Perseus_ software by MaxQuant team. The missing values are replaced by random numbers drawn from a normal distribution of _1.8_ standard devation down shift and with a width of _0.3_ of each sample. 72 | - **bpca:** Bayesian missing value imputation 73 | - **knn:** Missing values replace by nearest neighbour averaging technique 74 | - **QRILC:** A missing data imputation method that performs the imputation of left-censored missing data using random draws from a truncated distribution with parameters estimated using quantile regression. 75 | - **MinDet:** Performs the imputation of left-censored missing data using a deterministic minimal value approach. Considering a expression data with n samples and p features, for each sample, the missing entries are replaced with a minimal value observed in that sample. The minimal value observed is estimated as being the q-th quantile (default q = 0.01) of the observed values in that sample. 76 | - **MinProb:** Performs the imputation of left-censored missing data by random draws from a Gaussian distribution centred to a minimal value. Considering an expression data matrix with n samples and p features, for each sample, the mean value of the Gaussian distribution is set to a minimal observed value in that sample. The minimal value observed is estimated as being the q-th quantile (default q = 0.01) of the observed values in that sample. The standard deviation is estimated as the median of the feature standard deviations. Note that when estimating the standard deviation of the Gaussian distribution, only the peptides/proteins which present more than 50% recorded values are considered. 77 | - **min:** Replaces the missing values by the smallest non-missing value in the data. 78 | - **zero:** Replaces the missing values by **0**. 79 | 80 | #### False Discovery Rate (FDR) correction option 81 | - Benjamin Hocheberg (BH) method 82 | - t-statistics correction: Implemented in [fdrtools](http://strimmerlab.org/software/fdrtool/) 83 | 84 | 85 | #### Data pre-filtering criteria 86 | Following data-cleaning criteria is applied before performing differential experssion analysis. 87 | 88 | - Remove potential contaminants 89 | - Remove reverse sequences 90 | - Remove proteins identified only by sites 91 | - Remove proteins identified/quantified by a single Razor or unique peptide 92 | - Remove observation with high proportion of missing values (intensity values must be present 93 | at least 2 out of three replicates) 94 | 95 | #### Differential expression analysis 96 | Protein-wise linear models combined with empirical Bayes statistics are used for the differential expression analysis. We use a _bioconductor_ package _limma_ to carry out the analysis using automatically generates the contrasts from experiment design table provided by the user allowing the generation of results for all possible comparisons. It also take into account user defined cutoffs to filter significantly different proteins. 97 | 98 | ## Output 99 | 100 | #### Result table 101 | - **LFQ Results Table:** Includes names (Gene names), Protein Ids, Log fold changes/ ratios (each pairwise comparisons), Adjusted _p-values_ (applying FDR corrections), _p-values_, boolean values for significance, average protein intensity (log transformed) in each sample. 102 | 103 | #### Result Plots 104 | 1. **PCA plot**: A Principal Componant Analysis(PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. In brief, the more similar 2 samples are, the closer they cluster together. Of course, this means that biological replicates (and in particular technical replicates) should cluster tightly together. For further information, here are a few links, which explains the principals of PCAs: [Info](ttp://ordination.okstate.edu/PCA.htm) and [Basic introduction](http://setosa.io/ev/principal-component-analysis/) 105 | 106 | ![PCA_plot](PCA_plot.png) 107 | 108 | 109 | 2. **Heatmap**: The heatmap representation gives an overview of all significant/differentially expressed proteins (rows) in all samples (columns). This visualization allows the identfication of general trends such as if one sample or replicate is highly different compared to the others and might be considerd as an outlier. Additionally, the hierarchical clustering of samples (columns) indicates how related the different samples are and hierarchicalclustering of proteins (rows) identifies similarly behaving proteins. This analysis divides differencially expressed proteins into _six_ clusters/groups. User also have option to download protein information from individual cluster. 110 | 111 | ![heatmap](heatmap.png) 112 | 113 | 3. **Volcano plot**: A volcano plot is generated for each pairwise comparison. It is a graphical visualization by plotting the “Fold Change (Log2)” on the x-axis versus the –log10 of the “ _p-value_” on the y-axis. Interesting candidate proteins are located in the left and right upper quadrant. User can toggle the display name checkbox to highlight names of differencially expressed proteins or use 'adjusted _p-value_' as y-axis. Importantly, user can highlight protein or their interest (colored moroon) by selecting the row from "**LFQ Results Table**". This highlighted plot can be downloaded using " _Save Highlited Plot_" button. 114 | 115 | ![volcano_plot](volcano_plot.png) 116 | 117 | #### QC plots 118 | 1. **Sample Correlation Plot**: A correlation matrix is plotted as a heatmap to visualize the Pearson correlation coefficients between the different samples. 119 | ![corr_plot](correlation_plot.png) 120 | 2. **Sample CVs Plots**: A plot representing distribution of protein level coefficient of variation for each condition. Each plot also contains a vertical line representing median CVs percentage withing that condition. 121 | ![cv_plot](CV_plot.png) 122 | 3. **Protein Numbers**: A bar-plot representing number of proteins identified and quantified in each sample. 123 | ![proteins](Protein_number.png) 124 | 4. **Sample coverage**: A plot highlighting overlap between identified proteins across all samples in the experiment. 125 | ![overlap](Protein_overlap.png) 126 | 5. **Normalization**: Two plots representing the effect of variant stabilising normalisation (vsn) method on protein intensity distribution in each sample. **Note**: As MaxQuant protein intensity is already been normalised using MaxLFQ algorithm, further normalisation is not done during data analysis. This plot is just for visualisation puporse. 127 | 6. **Missing values- Quant**: To check whether missing values are biased to lower intense proteins, the densities and cumulative fractions are plotted for proteins with and without missing values. 128 | ![missing_quant](missing_quant.png) 129 | 7. **Missing values- Heatmap**: To explore the pattern of missing values in the data, a heatmap is plotted indicating whether values are missing (0) or not (1). Only proteins with at least one missing value are visualized. 130 | ![missing_heatmap](missing_heatmap.png) 131 | 8. **Imputation**: A desity plot of protein intensity (log2) distrubution for each condition after and before missing value imputation being performed. 132 | ![imputation](imputation.png) 133 | 9. **_p-value_ Histogram**: A histogram of p-value distribution for all the proteins across all pairwise comparison. 134 | ![histo_p](pvalue_hist.png) 135 | 136 | 137 | 138 | #### Download options 139 | 140 | - **Download tables** (csv format) 141 | 1. Results: Same as _LFQ Results Table_ 142 | 2. Unimputed data matrix: Original protein intensities before imputation in each sample. 143 | 3. Imputed data matrix: Protein intensities after performing selected imputaion method 144 | 4. Full results: Combined table of all above data outputs i.e. with and without imputation information along with fold change and p-values. 145 | 146 | - **Download Report** (word format) 147 | A summary report document including some statistics and plots. 148 | 149 | - **Download Plots** (PDF format) 150 | A PDF document containing all the plots generated during the analysis. 151 | 152 | -------------------------------------------------------------------------------- /www/Info.md: -------------------------------------------------------------------------------- 1 | Differential experssion analysis label free proteomics data 2 | =========================================================== 3 | 4 | # Introduction 5 | ------------ 6 | 7 | This tool is developed to automate downstream statistical analysis of 8 | quantitative proteomics (label-free) datasets generated by MaxQuant. 9 | 10 | # Input 11 | ----- 12 | 13 | - MaxQuant **proteinGroups.txt** file that **Must** contain *Gene 14 | name* and *Protein IDs* column. 15 | - An **experiment design table**: A tab separated file **only** 16 | containing **three** columns namely: "*label*", "*condition*", 17 | "*replicate*". The column names are **case sensitive** 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 |
labelconditionreplicate
H1CD34High1
H2CD34High2
H3CD34High3
H4CD34High4
L1CD34Low1
L2CD34Low2
L3CD34Low3
L4CD34Low4
70 | 71 | **Note:** The label column must match the lables present in **LFQ 72 | Intensity** columns of **proteinGroups.txt** file. For example, include 73 | **"H1"** in lable column if **"LFQ Intensity H1"** column present in 74 | your proteinGroups file. 75 | 76 | ## Advanced Options 77 | ---------------- 78 | 79 | #### Significant protein filtering criteria 80 | 81 | - Adjusted p-value cutoff: default is **0.05** 82 | - Log fold change cutoff: default is **1** 83 | 84 | #### Missing value imputation options 85 | 86 | - **Perseus-type:** This method is based on popular missing value 87 | imputation procedure implemented in *Perseus* software by MaxQuant 88 | team. The missing values are replaced by random numbers drawn from a 89 | normal distribution of *1.8* standard devation down shift and with a 90 | width of *0.3* of each sample. 91 | - **bpca:** Bayesian missing value imputation 92 | - **knn:** Missing values replace by nearest neighbour averaging 93 | technique 94 | - **QRILC:** A missing data imputation method that performs the 95 | imputation of left-censored missing data using random draws from a 96 | truncated distribution with parameters estimated using quantile 97 | regression. 98 | - **MinDet:** Performs the imputation of left-censored missing data 99 | using a deterministic minimal value approach. Considering a 100 | expression data with n samples and p features, for each sample, the 101 | missing entries are replaced with a minimal value observed in that 102 | sample. The minimal value observed is estimated as being the q-th 103 | quantile (default q = 0.01) of the observed values in that sample. 104 | - **MinProb:** Performs the imputation of left-censored missing data 105 | by random draws from a Gaussian distribution centred to a minimal 106 | value. Considering an expression data matrix with n samples and p 107 | features, for each sample, the mean value of the Gaussian 108 | distribution is set to a minimal observed value in that sample. The 109 | minimal value observed is estimated as being the q-th quantile 110 | (default q = 0.01) of the observed values in that sample. The 111 | standard deviation is estimated as the median of the feature 112 | standard deviations. Note that when estimating the standard 113 | deviation of the Gaussian distribution, only the peptides/proteins 114 | which present more than 50% recorded values are considered. 115 | - **min:** Replaces the missing values by the smallest non-missing 116 | value in the data. 117 | - **zero:** Replaces the missing values by **0**. 118 | 119 | #### False Discovery Rate (FDR) correction option 120 | 121 | - Benjamin Hocheberg (BH) method 122 | - t-statistics correction: Implemented in 123 | [fdrtools](http://strimmerlab.org/software/fdrtool/) 124 | 125 | #### Data pre-filtering criteria 126 | 127 | Following data-cleaning criteria is applied before performing 128 | differential experssion analysis. 129 | 130 | - Remove potential contaminants 131 | - Remove reverse sequences 132 | - Remove proteins identified only by sites 133 | - Remove proteins identified/quantified by a single Razor or unique 134 | peptide 135 | - Remove observation with high proportion of missing values (intensity 136 | values must be present at least 2 out of three replicates) 137 | 138 | #### Differential expression analysis 139 | 140 | Protein-wise linear models combined with empirical Bayes statistics are 141 | used for the differential expression analysis. We use a *bioconductor* 142 | package *limma* to carry out the analysis using automatically generates 143 | the contrasts from experiment design table provided by the user allowing 144 | the generation of results for all possible comparisons. It also take 145 | into account user defined cutoffs to filter significantly different 146 | proteins. 147 | 148 | Output 149 | ------ 150 | 151 | #### Result table 152 | 153 | - **LFQ Results Table:** Includes names (Gene names), Protein Ids, Log 154 | fold changes/ ratios (each pairwise comparisons), Adjusted 155 | *p-values* (applying FDR corrections), *p-values*, boolean values 156 | for significance, average protein intensity (log transformed) in 157 | each sample. 158 | 159 | #### Result Plots 160 | 161 | 1. **PCA plot**: A Principal Componant Analysis(PCA) is a technique 162 | used to emphasize variation and bring out strong patterns in a 163 | dataset. In brief, the more similar 2 samples are, the closer they 164 | cluster together. Of course, this means that biological replicates 165 | (and in particular technical replicates) should cluster tightly 166 | together. For further information, here are a few links, which 167 | explains the principals of PCAs: 168 | [Info](ttp://ordination.okstate.edu/PCA.htm) and [Basic 169 | introduction](http://setosa.io/ev/principal-component-analysis/) 170 | 171 | ![PCA\_plot](PCA_plot.png) 172 | 173 | 1. **Heatmap**: The heatmap representation gives an overview of all 174 | significant/differentially expressed proteins (rows) in all samples 175 | (columns). This visualization allows the identfication of general 176 | trends such as if one sample or replicate is highly different 177 | compared to the others and might be considerd as an outlier. 178 | Additionally, the hierarchical clustering of samples (columns) 179 | indicates how related the different samples are and 180 | hierarchicalclustering of proteins (rows) identifies similarly 181 | behaving proteins. This analysis divides differncially expressed 182 | proteins into *six* clusters/groups. User also have option to 183 | download protein information from individual cluster. 184 | 185 | ![heatmap](heatmap.png) 186 | 187 | 1. **Volcano plot**: A volcano plot is generated for each pairwise 188 | comparison. It is a graphical visualization by plotting the “Fold 189 | Change (Log2)” on the x-axis versus the –log10 of the “ *p-value*” 190 | on the y-axis. Interesting candidate proteins are located in the 191 | left and right upper quadrant. User can toggle the display name 192 | checkbox to highlight names of differencially expressed proteins or 193 | use 'adjusted *p-value*' as y-axis. Importantly, user can highlight 194 | protein or their interest (colored moroon) by selecting the row from 195 | "**LFQ Results Table**". This highlighted plot can be downloaded 196 | using " *Save Highlited Plot*" button. 197 | 198 | ![volcano\_plot](volcano_plot.png) 199 | 200 | #### QC plots 201 | 202 | 1. **Sample Correlation Plot**: A correlation matrix is plotted as a 203 | heatmap to visualize the Pearson correlation coefficients between 204 | the different samples. ![corr\_plot](correlation_plot.png) 205 | 2. **Sample CVs Plots**: A plot representing distribution of protein 206 | level coefficient of variation for each condition. Each plot also 207 | contains a vertical line representing median CVs percentage withing 208 | that condition. ![cv\_plot](CV_plot.png) 209 | 3. **Protein Numbers**: A bar-plot representing number of proteins 210 | identified and quantified in each sample. 211 | ![proteins](Protein_number.png) 212 | 4. **Sample coverage**: A plot highlighting overlap between identified 213 | proteins across all samples in the experiment. 214 | ![overlap](Protein_overlap.png) 215 | 5. **Normalization**: Two plots representing the effect of variant 216 | stabilising normalisation (vsn) method on protein intensity 217 | distribution in each sample. **Note**: As MaxQuant protein intensity 218 | is already been normalised using MaxLFQ algorithm, further 219 | normalisation is not done during data analysis. This plot is just 220 | for visualisation puporse. 221 | 6. **Missing values- Quant**: To check whether missing values are 222 | biased to lower intense proteins, the densities and cumulative 223 | fractions are plotted for proteins with and without missing values. 224 | ![missing\_quant](missing_quant.png) 225 | 7. **Missing values- Heatmap**: To explore the pattern of missing 226 | values in the data, a heatmap is plotted indicating whether values 227 | are missing (0) or not (1). Only proteins with at least one missing 228 | value are visualized. ![missing\_heatmap](missing_heatmap.png) 229 | 8. **Imputation**: A desity plot of protein intensity (log2) 230 | distrubution for each condition after and before missing value 231 | imputation being performed. ![imputation](imputation.png) 232 | 9. ***p-value* Histogram**: A histogram of p-value distribution for all 233 | the proteins across all pairwise comparison. 234 | ![histo\_p](pvalue_hist.png) 235 | 236 | ## Download options 237 | 238 | - **Download tables** (csv format) 239 | 240 | 1. Results: Same as *LFQ Results Table* 241 | 2. Unimputed data matrix: Original protein intensities before 242 | imputation in each sample. 243 | 3. Imputed data matrix: Protein intensities after performing selected 244 | imputaion method 245 | 4. Full results: Combined table of all above data outputs i.e. with and 246 | without imputation information along with fold change and p-values. 247 | 248 | - **Download Report** (word format) A summary report document 249 | including some statistics and plots. 250 | 251 | - **Download Plots** (PDF format) A PDF document containing all the 252 | plots generated during the analysis. 253 | -------------------------------------------------------------------------------- /www/Info_cache/html/__packages: -------------------------------------------------------------------------------- 1 | base 2 | methods 3 | datasets 4 | utils 5 | grDevices 6 | graphics 7 | stats 8 | knitr 9 | rmdformats 10 | -------------------------------------------------------------------------------- /www/Info_cache/html/exp_design_b71c6ebe27cac9dec05c8ffe7432463e.RData: -------------------------------------------------------------------------------- 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