├── DESCRIPTION ├── NAMESPACE ├── NEWS ├── QBRC.jpg ├── R ├── EM_functions.R ├── EM_interface.R └── EM_model.R ├── README.md ├── SCINA_code_nonpublish.zip ├── exp_example.jpg ├── exp_signature.jpg ├── inst └── extdata │ ├── example_expmat.RData │ ├── example_expmat.csv │ ├── example_results.RData │ ├── example_results.pdf │ ├── example_signatures.RData │ └── example_signatures.csv ├── man ├── SCINA.Rd ├── figures │ ├── sub1.png │ ├── sub2.png │ ├── sub3.png │ └── sub4.png ├── plotheat.SCINA.Rd └── preprocess.signatures.Rd └── plot_example.jpg /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: SCINA 2 | Type: Package 3 | Title: A Semi-Supervised Category Identification and Assignment Tool 4 | Version: 1.0.0 5 | Date: 2018-09-18 6 | Author: Ze Zhang 7 | Maintainer: Ze Zhang 8 | Depends: R (>= 2.15.0), MASS, gplots 9 | Description: An automatic cell type detection and assignment algorithm for single cell RNA-Seq and Cytof/FACS data. SCINA is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of markers, such as genes and protein symbols that are highly or lowly expressed in each category. See Ze Z, Danni L, et al (2018) for more details. 10 | URL: http://lce.biohpc.swmed.edu/scina/ 11 | https://github.com/jcao89757/SCINA 12 | License: GPL-2 13 | NeedsCompilation: no 14 | RoxygenNote: 6.1.0 15 | Packaged: 2018-09-20 21:02:58 UTC; s421955 16 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(SCINA) 4 | export(plotheat.SCINA) 5 | export(preprocess.signatures) 6 | import(MASS) 7 | importFrom(gplots,heatmap.2) 8 | importFrom("grDevices", "cm.colors", "topo.colors") 9 | importFrom("graphics", "legend", "par") 10 | importFrom("stats", "quantile", "sd", "var") 11 | importFrom("utils", "read.csv") 12 | -------------------------------------------------------------------------------- /NEWS: -------------------------------------------------------------------------------- 1 | 1.0.0 2 | * Experimental version 3 | -------------------------------------------------------------------------------- /QBRC.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/QBRC.jpg -------------------------------------------------------------------------------- /R/EM_functions.R: -------------------------------------------------------------------------------- 1 | ############################################################ 2 | #Internal functions and methods for SCINA 3 | #Maintainer: Ze Zhang, 4 | ############################################################ 5 | 6 | #Dependencies: 7 | #' @import MASS 8 | #' @importFrom gplots heatmap.2 9 | 10 | #Description: 11 | #' An internal function called by SCINA. 12 | #' Checking input parameters' formats, integrity, and whether their values are within the designed value range. 13 | 14 | #Parameters: 15 | #' See more details in EM_model.R 16 | 17 | #Outputs: 18 | #' @return qual A binary value, 0 (FALSE) represents the SCINA is not able to use this input sets. 1 (TRUE) represents that the inputs' quality is satisfied. 19 | #' @return sig Signatures treated up to user requirements. 20 | #' @return para Other parameters, marked in log_file if they are out of range and are changed to defaults. 21 | 22 | check.inputs=function(exp, signatures, max_iter, convergence_n, convergence_rate, sensitivity_cutoff, rm_overlap, log_file) 23 | { 24 | # Initialize parameters. 25 | quality=1 26 | def_max_iter=1000 27 | def_conv_n=10 28 | def_conv_rate=0.99 29 | def_dummycut=0.33 30 | allgenes=row.names(exp) 31 | # Check sequence matrices. 32 | if (any(is.na(exp))){ 33 | cat('NA exists in expression matrix.',file=log_file,append=T) 34 | cat('\n',file=log_file,append=T) 35 | quality=0 36 | } 37 | # Check signatures. 38 | if (any(is.na(signatures))){ 39 | cat('Null cell type signature genes.',file=log_file,append=T) 40 | cat('\n',file=log_file,append=T) 41 | quality=0 42 | }else{ 43 | signatures=sapply(signatures,function(x) unique(x[(!is.na(x)) & (x %in% allgenes)]),simplify=F) 44 | # Remove duplicate genes. 45 | if(rm_overlap==1){ 46 | tmp=table(unlist(signatures)) 47 | signatures=sapply(signatures,function(x) x[x %in% names(tmp[tmp==1])],simplify=F) 48 | } 49 | # Check if any genes have all 0 counts 50 | signatures=sapply(signatures,function(x) x[apply(exp[x,,drop=F],1,sd)>0],simplify=F) 51 | } 52 | # Clean other parameters. 53 | if (is.na(convergence_n)){ 54 | cat('Using convergence_n=default',file=log_file,append=T) 55 | cat('\n',file=log_file,append=T) 56 | convergence_n=def_conv_n 57 | } 58 | if (is.na(max_iter)){ 59 | cat('Using max_iter=default',file=log_file,append=T) 60 | cat('\n',file=log_file,append=T) 61 | max_iter=def_max_iter 62 | }else{ 63 | if (max_iter1e200]=1e200 94 | tmp[tmp<1e-200]=1e-200 95 | return(tmp) 96 | } 97 | 98 | 99 | #Description: 100 | #' A function to plot SCINA results in a heatmap. 101 | 102 | #Parameters: 103 | #' @param exp See more details in \code{\link{SCINA}} 104 | #' @param results An output object returned from SCINA. 105 | #' @param signatures See more details in \code{\link{SCINA}} 106 | 107 | #Output: 108 | #' @return A heatmap showing signature genes' expression level and SCINA predicted cell types. 109 | 110 | #Usage: 111 | #' plotheat.SCINA(exp,results,signatures) 112 | 113 | plotheat.SCINA=function(exp,results,signatures){ 114 | # Remove nonexist signature genes. 115 | allgenes=row.names(exp) 116 | signatures=sapply(signatures,function(x) unique(x[(!is.na(x)) & (x %in% allgenes)]),simplify=F) 117 | # Build side color bars. 118 | col_row=topo.colors(length(signatures)) 119 | col_col=cm.colors(length(unique(results$cell_labels))) 120 | # Build matrices to heatmap.2 function. 121 | exp2plot=exp[unlist(signatures),order(factor(results$cell_labels,levels=c(names(signatures),'unknown')),decreasing=F)] 122 | exp2plot=as.matrix(exp2plot) 123 | col_colside=col_col[as.factor(results$cell_labels[order(factor(results$cell_labels,levels=c(names(signatures),'unknown')),decreasing=F)])] 124 | col_rowside=col_row[unlist(sapply(1:length(signatures),function(i) rep(i, length(signatures[[i]]))))] 125 | # Plot heatmaps. 126 | par(mar=rep(1,4)) 127 | plot=heatmap.2(exp2plot,trace='none',col=c('white','mistyrose1','lightpink1','lightpink2','lightpink3','lightpink4'),Rowv=FALSE,Colv=FALSE,dendrogram='none',xlab='Cells',ylab='Genes',labCol=NA,ColSideColors=col_colside,RowSideColors=col_rowside,key = F,margins = c(6.5,6.5)) 128 | legend_text=c(paste('Gene identifiers',names(signatures),sep='_'),names(signatures),'unknown') 129 | legend_cor=c(col_row,unique(col_colside)) 130 | legend('topright',legend=legend_text,fill=legend_cor,cex=0.5) 131 | return(invisible(plot)) 132 | } 133 | 134 | 135 | #Description: 136 | #' A function to convert signatures uploaded via .csv files to lists used by SCINA. 137 | 138 | #Parameter: 139 | #' @param file_path The path where the .csv file stores. The first row of the file should be cell type names. Each column is occupied by the signature genes/protein markers for the cell type in the first row. Please find more details in \code{\link{SCINA}}. 140 | 141 | #Output: 142 | #' @return signatures A list of signature gene lists as an input for SCINA. 143 | 144 | #Usage: 145 | #' signatures=preprocess.signatures('./example/example_signatures.csv') 146 | 147 | preprocess.signatures=function(file_path){ 148 | csv_signatures=read.csv(file_path,stringsAsFactors = F,header = T,fill = F,quote ='') 149 | signatures=as.list(csv_signatures) 150 | signatures=sapply(signatures,function(x) x[(!is.na(x)) & x!=""],simplify = F) 151 | return(signatures) 152 | } 153 | 154 | -------------------------------------------------------------------------------- /R/EM_interface.R: -------------------------------------------------------------------------------- 1 | ########################### 2 | #Sep04,2018 3 | #Maintainer: Ze Zhang 4 | ########################### 5 | 6 | #@' Description: Shell command line tool for EM model, wrap up input parameters to the script, receive results and saved in the system. 7 | 8 | #@' Parameters: See more in EM_model.R. The parameter ranges are the same, exp and signatures are supposed to be .csv files. 9 | #@' Parameters: output The RData file to store the output results and plots. Paths may be included. 10 | #@' Parameters: job_id Any string could work. Used to avoid overwritting. 11 | 12 | #@' Usage: Rscript EM_interface.R example_expmat_test.csv example_signatures.csv 100 10 0.99 1 1 1 test.RData 1 13 | 14 | 15 | setwd('$HOME/SCINA_test') 16 | # A test folder on Ze Zhang's system. Contains all input .csv files and scripts. 17 | source('EM_model.R') 18 | source('EM_functions.R') 19 | suppressMessages(library('gplots')) 20 | #Initiate input parameters 21 | args=commandArgs(trailingOnly = T) 22 | exp_csv=args[1];signatures_csv=args[2];max_iter=args[3];convergence_n=args[4];convergence_rate=args[5] 23 | sensitivity_cutoff=args[6];rm_overlap=args[7];allow_unknown=args[8];output=args[9];job_id=args[10] 24 | max_iter=as.numeric(max_iter);convergence_n=as.numeric(convergence_n);convergence_rate=as.numeric(convergence_rate) 25 | sensitivity_cutoff=as.numeric(sensitivity_cutoff);rm_overlap=as.numeric(rm_overlap);allow_unknown=as.numeric(allow_unknown) 26 | #Read data 27 | exp=read.csv(exp_csv,row.names=1,stringsAsFactors = F) 28 | signatures=preprocess.signatures(signatures_csv) 29 | #Run SCINA 30 | results=SCINA(exp,signatures,max_iter,convergence_n,convergence_rate,sensitivity_cutoff,rm_overlap,allow_unknown,log_file=paste(job_id,'SCINA.log',sep='_')) 31 | #Draw output figures 32 | jpeg(paste(job_id,'output_plot.jpg',sep='_'),width =2444,height =2444,res=600 ) 33 | par(mar=rep(3,4)) 34 | plotheat.SCINA(exp,results,signatures) 35 | dev.off() 36 | #Save results 37 | base_name=basename(output) 38 | base_path=gsub(base_name,'',output) 39 | save(results,file=paste(base_path,job_id,'_',base_name,sep='')) 40 | -------------------------------------------------------------------------------- /R/EM_model.R: -------------------------------------------------------------------------------- 1 | ####################################################################### 2 | #SCINA: A Semi-Supervised Category Identification and Assignment Tool. 3 | #Maintainer: Ze Zhang, 4 | ####################################################################### 5 | 6 | #Description: 7 | #' An automatic cell type detection and assignment algorithm for single cell RNA-Seq (scRNA-seq) and Cytof/FACS data. 8 | #' SCINA is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of identifiers, 9 | #' such as genes and protein symbols,that are highly or lowly expressed in each category. 10 | #' See Ze Z, Danni L, et al (2018) for more details. 11 | 12 | #Dependencies: 13 | #' @import MASS 14 | #' @importFrom gplots heatmap.2 15 | 16 | #Parameters: 17 | #' @param exp A normalized matrix representing the target dataset. Columns correpond to objects (cell barcodes for example), rows correspond to attributes or variables (gene symbols for example). 18 | #' @param signatures A list contains multiple signature identifier lists. Each signature identifier list (genes for example) represents prior knowledge for one category (cell type for example), containing genes or protein symbols with high degree of detection. 19 | #' @param max_iter An integer > 0. Default is 100. Max iterations allowed for the EM algorithm. 20 | #' @param covergence_n An integer > 0. Default is 10. Stop the EM algorithm if during the last n rounds of iterations, cell type assignment keeps steady above the convergence_rate. 21 | #' @param convergence_rate A float between 0 and 1. Default is 0.99. Percentage of cells for which the type assignment remains stable for the last n rounds. 22 | #' @param sensitivity_cutoff A float between 0 and 1. Default is 1. The cutoff to remove signatures whose cells types are deemed as non-existent at all in the data by the SCINA algorithm. 23 | #' @param rm_overlap A binary value, default 1 (TRUE), denotes that shared symbols between signature lists will be removed. If 0 (FALSE) then allows different cell types to share the same identifiers. 24 | #' @param allow_unknown A binary value, default 1 (TRUE). If 0 (FALSE) then no cell will be assigned to the 'unknown' category. 25 | #' @param log_file A string names the record of the running status of the SCINA algorithem, default 'SCINA.log'. 26 | 27 | #Outputs: 28 | #' @return cell_labels A vector contains cell type mapping results for each cell. 29 | #' @return probabilities A probability matrix indicating the predicted probability for each cell belongs to each cell type respectively. 30 | 31 | #Usage: 32 | #' SCINA(exp,signatures,max_iter,convergence_n,convergence_rate,sensitivity_cutoff,rm_overlap,allow_unknown,log_file) 33 | 34 | SCINA=function(exp,signatures,max_iter=100,convergence_n=10,convergence_rate=0.99, 35 | sensitivity_cutoff=1,rm_overlap=1,allow_unknown=1,log_file='SCINA.log'){ 36 | cat('Start running SCINA.',file=log_file,append=F) 37 | cat('\n',file=log_file,append=T) 38 | #Create a status file for the webserver 39 | status_file=paste(log_file,'status',sep='.') 40 | all_sig=unique(unlist(signatures)) 41 | # Create low-expression signatures. 42 | invert_sigs=grep('^low_',all_sig,value=T) 43 | if(!identical(invert_sigs, character(0))){ 44 | cat('Converting expression matrix for low_genes.',file=log_file,append=T) 45 | cat('\n',file=log_file,append=T) 46 | invert_sigs_2add=unlist(lapply(invert_sigs,function(x) strsplit(x,'_')[[1]][2])) 47 | invert_sigs=invert_sigs[invert_sigs_2add%in%row.names(exp)] 48 | invert_sigs_2add=invert_sigs_2add[invert_sigs_2add%in%row.names(exp)] 49 | sub_exp=-exp[invert_sigs_2add,,drop=F] 50 | row.names(sub_exp)=invert_sigs 51 | exp=rbind(exp,sub_exp) 52 | rm(sub_exp,all_sig,invert_sigs,invert_sigs_2add) 53 | } 54 | # Check input parameters. 55 | quality=check.inputs(exp,signatures,max_iter,convergence_n,convergence_rate,sensitivity_cutoff,rm_overlap,log_file) 56 | if(quality$qual==0){ 57 | cat('EXITING due to invalid parameters.',file=log_file,append=T) 58 | cat('\n',file=log_file,append=T) 59 | cat('0',file=status_file,append=F) 60 | stop('SCINA stopped.') 61 | } 62 | signatures=quality$sig 63 | max_iter=quality$para[1] 64 | convergence_n=quality$para[2] 65 | convergence_rate=quality$para[3] 66 | sensitivity_cutoff=quality$para[4] 67 | # Initialize variables. 68 | exp=as.matrix(exp) 69 | exp=exp[unlist(signatures),,drop=F] 70 | labels=matrix(0,ncol=convergence_n, nrow=dim(exp)[2]) 71 | unsatisfied=1 72 | if(allow_unknown==1){ 73 | tao=rep(1/(length(signatures)+1),length(signatures)) 74 | }else{tao=rep(1/(length(signatures)),length(signatures))} 75 | theta=list() 76 | for(i in 1:length(signatures)){ 77 | theta[[i]]=list() 78 | theta[[i]]$mean=t(apply(exp[signatures[[i]],,drop=F],1,function(x) quantile(x,c(0.7,0.3)))) 79 | tmp=apply(exp[signatures[[i]],,drop=F],1,var) 80 | theta[[i]]$sigma1=diag(tmp,ncol = length(tmp)) 81 | theta[[i]]$sigma2=theta[[i]]$sigma1 82 | } 83 | for(marker_set in 1:length(theta)){ 84 | if(is_empty(theta[[marker_set]]$sigma1) == TRUE){ 85 | theta <- theta[-marker_set] 86 | } 87 | } 88 | sigma_min=min(sapply(theta,function(x) min(c(diag(x$sigma1),diag(x$sigma2)))))/100 89 | remove_times=0 90 | # Run SCINA algorithm. 91 | while(unsatisfied==1){ 92 | prob_mat=matrix(tao,ncol=dim(exp)[2],nrow=length(tao)) 93 | row.names(prob_mat)=names(signatures) 94 | iter=0 95 | labels_i=1 96 | remove_times=remove_times+1 97 | while(iter=convergence_rate){ 128 | cat('Job finished successfully.',file=log_file,append=T) 129 | cat('\n',file=log_file,append=T) 130 | cat('1',file=status_file,append=F) 131 | break 132 | } 133 | labels_i=labels_i+1 134 | if(labels_i==convergence_n+1){ 135 | labels_i=1 136 | } 137 | if(iter==max_iter){ 138 | cat('Maximum iterations, breaking out.',file=log_file,append=T) 139 | cat('\n',file=log_file,append=T) 140 | } 141 | } 142 | #Build result matrices. 143 | colnames(prob_mat)=colnames(exp) 144 | row.names(prob_mat)=names(signatures) 145 | row.names(labels)=colnames(exp) 146 | # Attempt to remove unused signatures. 147 | dummytest=sapply(1:length(signatures),function(i) mean(theta[[i]]$mean[,1]-theta[[i]]$mean[,2]==0)) 148 | if(all(dummytest<=sensitivity_cutoff)){ 149 | unsatisfied=0 150 | }else{ 151 | rev=which(dummytest>sensitivity_cutoff); 152 | cat(paste('Remove dummy signatures:',rev,sep=' '),file=log_file,append=T) 153 | cat('\n',file=log_file,append=T) 154 | signatures=signatures[-rev] 155 | tmp=1-sum(tao) 156 | tao=tao[-rev] 157 | tao=tao/(tmp+sum(tao)) 158 | theta=theta[-rev] 159 | } 160 | } 161 | return(list(cell_labels=c("unknown",names(signatures))[1+labels[,labels_i]],probabilities=prob_mat)) 162 | } 163 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![SCINA_logo](QBRC.jpg) 2 | # SCINA 3 | ## Introduction 4 | SCINA is an automatic cell type detection and assignment algorithm for single cell RNA-Seq (scRNA-seq) and Cytof/FACS data. SCINA is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of signatures, such as genes and protein symbols, that are highly-expressed (or lowly-expressed) in each category. 5 | 6 | Please refer to our paper for more details of SCINA: 7 | ["SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples"](https://www.mdpi.com/2073-4425/10/7/531), Zhang Z, Luo D, et al., 2018 8 | 9 | Or please check our web server to run SCINA on the clould: http://lce.biohpc.swmed.edu/scina 10 | 11 | Researchers searching for more bioinformatics tools please visit our lab website: https://qbrc.swmed.edu/labs/wanglab/index.php 12 | ## Getting started with SCINA 13 | The SCINA algorithm is implemented in R. Users who are not famaliar with basic R programming are suggested to use our web server to run SCINA with a user-friendly GUI. 14 | ### Installation Instructions 15 | #### Install from CRAN 16 | ```{r} 17 | install.packages('SCINA') 18 | library('SCINA') 19 | ``` 20 | #### Install from GitHub 21 | ```{r} 22 | library('devtools') 23 | install_github('jcao89757/SCINA') 24 | library('SCINA') 25 | ``` 26 | ### Dependencies 27 | R (version 2.15.0 or later) 28 | 29 | **R Packages** 30 | 31 | MASS, gplots, devtools (installation from GitHub only) 32 | ## Guided Tutorial 33 | For this tutorial, we will be predicting cell types from a toy example, a single-cell RNA sequencing (scRNA-seq) data matrix freely available from 10X Genomics. The signatures we used as prior knowledge are extracted from the eTME signatures. For more detail please refer to our paper:["An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors."](http://cancerdiscovery.aacrjournals.org/content/early/2018/06/08/2159-8290.CD-17-1246). All the data we need for this tutorial is availiable [here](https://github.com/jcao89757/SCINA/tree/master/inst/extdata). 34 | ### Prepare input data 35 | The SCINA model takes at least two input data matrices to predict categories. 36 | 1. A normalized matrix representing the gene expression levels. Columns correspond to cells, rows correspond to genes or protein symbols. Please find the [.RData example](https://github.com/jcao89757/SCINA/tree/master/inst/extdata/example_expmat.RData) and the [.csv example](https://github.com/jcao89757/SCINA/tree/master/inst/extdata/example_expmat.csv) as examples. 37 | 2. A list contains multiple signature vectors. Each vector represents prior knowledge for one cell type, containing genes or protein symbols with high degree of detection. Please find the [.RData example](https://github.com/jcao89757/SCINA/tree/master/inst/extdata/example_signatures.RData) and the [.csv example](https://github.com/jcao89757/SCINA/tree/master/inst/extdata/example_signatures.csv) for the list of signatures. 38 | 39 | Both matrices can be uploaded from .Rdata files or .csv files. If the gene expression matrix is uploaded with .csv files, the format requirements are the same as the descriptions above **(Fig.1)**. If the signature identifier list is uploaded with .csv files, each column of the .csv file contains one signature list, and its column name should be the name of the cell type. Each signature vector contains genes or protein symbols. The signature vectors do not need to have the same length **(Fig.2)**. Improper signature lists may cause problems when running SCINA. Please check the **Trouble shooting** 40 | section at the end of this tutorial. 41 | 42 | ![exp_example](exp_example.jpg) 43 | 44 | **Fig.1 |** An example of a target dataset in .csv format. 45 | 46 | ![exp_signature](exp_signature.jpg) 47 | 48 | **Fig.2 |** An example of signature lists in .csv format. 49 | 50 | After downloading, please load the matrices to your R environment. 51 | ```{r} 52 | #.Rdata examples 53 | load(system.file('extdata','example_expmat.RData', package = "SCINA")) 54 | load(system.file('extdata','example_signatures.RData', package = "SCINA")) 55 | exp = exp_test$exp_data 56 | 57 | # Or .csv examples 58 | exp=read.csv('your/path/to/example_expmat.csv',row.names=1,stringsAsFactors = F) 59 | signatures=preprocess.signatures('your/path/to/example_signatures.csv') 60 | ``` 61 | ### Standard pre-processing workflow 62 | The example expression matrix we provided here is a normalized example. In most scenarios, users are encouraged to preprocess raw count outputs of their sequencing data. Considering the features of scRNA-seq data, we suggest that users may follow the pre-processing code below to achieve the best performance on their scRNA-seq raw counts. The log-transformation is always suggested to avoid heavy-tailed datasets. 63 | ```{r} 64 | #Install preprocessCore if required 65 | source("http://bioconductor.org/biocLite.R") 66 | biocLite("preprocessCore") 67 | library('preprocessCore') 68 | #Read data 69 | exp_raw=read.csv('your/path/to/raw/expression_matrix.csv',row.names=1,stringsAsFactors = F) 70 | #Log scale and quantile normalization 71 | exp_raw=log(exp_raw+1) 72 | exp[]=normalize.quantiles(exp_raw) 73 | ``` 74 | ### Set model parameters 75 | The SCINA algorithm has multiple parameters that users may tune to achieve a better performance. The table below contains description of those parameters. 76 | 77 | |Parameters|Details| 78 | |----------|-------| 79 | |max_iter|An integer > 0. Default is 100. Max iterations allowed for SCINA algorithm.| 80 | |convergence_n|An integer > 0. Default is 10. Stop SCINA if during the last n rounds of iterations, cell type assignment keeps steady above the convergence_rate.| 81 | |convergence_rate|A float between 0 and 1. Default is 0.99. Percentage of cells for which the type assignment remains stable for the last n rounds.| 82 | |sensitivity_cutoff|A float between 0 and 1. Default is 1. The cutoff to remove signatures whose cells types are deemed as non-existent at all in the data by SCINA.| 83 | |rm_overlap|A binary value, default 1 (TRUE), denotes that shared symbols between signature lists will be removed. If 0 (FALSE) then allows different cell types to share the same identifiers.| 84 | |allow_unknown|A binary value, default 1 (TRUE). If 0 (FALSE) then no cell will be assigned to the 'unknown' category.| 85 | |log_file|A name string denoting a record for the SCINA running status, path may be included. Default is 'SCINA.log'.| 86 | 87 | **Note 1**: If all of the genes in one or more signatures are overlapped with other signatures in the same list, please incldue unoverlapped genes or set **rm_overlap=TRUE** to avoid errors. 88 | 89 | **Note 2**: If unclassified cells in the result are more than your expectation, and your aim is not seaching for rare cell types, please set **allow_unknown=TRUE**. 90 | 91 | ### Predict cell types with SCINA 92 | SCINA can generate two output matrices in a result list. 93 | ```{r} 94 | results = SCINA(exp, signatures, max_iter = 100, convergence_n = 10, 95 | convergence_rate = 0.999, sensitivity_cutoff = 0.9, rm_overlap=TRUE, allow_unknown=TRUE, log_file='SCINA.log') 96 | View(results$cell_labels) 97 | View(results$probabilities) 98 | ``` 99 | More detail of the result is described in the table below. 100 | 101 | |Output|Details| 102 | |------|-------| 103 | |cell_labels|A vector contains cell type predictions for each cell.| 104 | |probabilities|A probability matrix indicating the predicted probability for each cell belonging to each cell type, respectively.| 105 | ### Result visualization 106 | We provide a function to plot SCINA results in a heatmap. The function takes the expression matrix, the signature lists and the SCINA results as input, and returns a heatmap showing signature genes' expression levels, grouped with SCINA predicted cell types **(Fig.3)**. 107 | ```{r} 108 | plotheat.SCINA(exp, results, signatures) 109 | ``` 110 | 111 | ![plot_example](plot_example.jpg) 112 | 113 | **Fig.3 |** An example of the output heatmap. 114 | 115 | ## Trouble shooting 116 | According to the experience and issues from our users, we found two frequent conditions when SCINA may not work as expected. 117 | 1. If any lists of signature contain small numbers of genes and the parameter 'rm_overlap' is set to 'TRUE' (which is also the default setting), there are chances that those gene signatures become null lists after the overlapped genes are removed. We suggest that each signature list may have at least one unique gene that does not appear in any other signature lists, or users may set 'rm_overlap' to 'FALSE' to avoid such problems. 118 | 2. If any lists of signature contain large numbers of genes (about 500 or more), users may fail on running SCINA. That happens because one of the intermediate variables becomes a large enough number to overflow the memory with too many signature genes. We suggest that keep the number of genes in each signature list 10 to 50 to achieve the best performance of SCINA. 119 | ## Version update 120 | 1.0.0: First release. (09-20-2018) 121 | 1.1.0: Notation update. (09-30-2018) 122 | 1.2.0: Bugs fixed. (07-18-2019) 123 | -------------------------------------------------------------------------------- /SCINA_code_nonpublish.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/SCINA_code_nonpublish.zip -------------------------------------------------------------------------------- /exp_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/exp_example.jpg -------------------------------------------------------------------------------- /exp_signature.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/exp_signature.jpg -------------------------------------------------------------------------------- /inst/extdata/example_expmat.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/inst/extdata/example_expmat.RData -------------------------------------------------------------------------------- /inst/extdata/example_expmat.csv: -------------------------------------------------------------------------------- 1 | "","TCGTTATGGGTGGA.1","TGACGAACTGGTGT.1","CTGAAGTGCAGTCA.1","GTAGACTGTACGAC.1","AGCATGACAAGTAG.1","TCCATAACAGGAGC.1","CATGGATGTCTAGG.1","ACACATCTGGCAAG.1","GTGCTAGATACTCT.1","GTAGGTACCATTTC.1","TACGCGCTTATCTC.1","GTGTCAGATTGAGC.1","CTTACATGGGTCTA.1","TACGGCCTCGGTAT.1","ATGCAGTGAGGAGC.1","AAGAAGACCTTGGA.1","GACAGTTGAAGGGC.1","GGAGCCACGTATCG.1","GAAGTAGAGGAGTG.1","CGCGAGACCTGTTT.1","GCTCACTGAGAACA.1","AGTTGTCTGGTGAG.1","AGGACACTCAGATC.1","TAATGATGTCAGTG.1","ACACCAGAAGCCTA.1","TGAGACACACAGTC.1","GACTGAACGGGCAA.1","GGGAAGTGGTTCTT.1","ATAATGACTCCCAC.1","GCCATCACTGGAGG.1","TTCCCACTTCTCCG.1","GGAGCCACTACTGG.1","TCCTAAACGTAGGG.1","GTAAGCACATTCCT.1","TCCATAACAGAGAT.1","ATTAGTGATGTTTC.1","ACATCACTAGAGGC.1","GGACATTGGCATCA.1","GATTCGGACCTTCG.1","TATACAGACTCAGA.1","CACGCTACAAGTGA.1","TAACCGGAGGACTT.1","CTAGTTACCCTAAG.1","GACAACACCGAATC.1","TACTACTGCGTAGT.1","CGTCGACTGAGAGC.1","GTGATCGACACTAG.1","TGAAATTGGTCATG.1","CCTGCAACCATGCA.1","ATAGCGTGGTTCTT.1","CCTGACTGGTACAC.1","TAAGTCCTGTTACG.1","ATTAAGACAAGGTA.1","CATTTGTGTTATCC.1","ACGGCGTGGGAAGC.1","TTAACCACTACGCA.1","AATCCGGATCGTGA.1","TGATTCACTGGCAT.1","GTGATCGACTGCTC.1","ATGCGATGGTAGCT.1","CTACTATGCCGTAA.1","GTAGCATGCGTTAG.1","TTCATTCTCCCACT.1","CTGTATACGTTCAG.1","GGAGCAGAAAGCCT.1","GTCAACGACATGCA.1","ATGTACCTCTCGAA.1","ACATCACTCTCAAG.1","GCATGTGAAGTACC.1","ACTCGAGAGGGCAA.1","AACTTGCTGGAAAT.1","AGCGCCGACTGACA.1","GCAATTCTTGCTTT.1","TGGTATCTTCGATG.1","AAAGCCTGCAACCA.1","GACGTATGAAAAGC.1","GCTACGCTAGTGTC.1","CGAAGGGAAGACTC.1","AGACTTCTCAGTCA.1","ATTATGGAGCGTTA.1","GTATTCACCACTTT.1","TCCCAGACGGATTC.1","AAATACTGACGGTT.1","CATCGGCTAGCGTT.1","ATTCCAACATCTTC.1","CCTGACTGCACTGA.1","TGATAAACGGTAAA.1","CCCGAACTGACGAG.1","GGTAGTACGTTTCT.1","CTTACAACAGGTCT.1","GTAGTCGATCAGAC.1","TTCAAAGATGCTCC.1","AGAGGTCTTGGTGT.1","TTATGAGAGACGAG.1","TGCACGCTAAGATG.1","TACAAATGAAACGA.1","GGCGACTGCGTAAC.1","TGAGGTACGCATCA.1","CCTCATCTGGAGGT.1","GGACCTCTGCCCTT.1","GGACAGGACGTTAG.1","TCACCTCTCTTGGA.1","CACCTGACAAGGTA.1","CTAGGTGAGAACTC.1","CATATAGAGTCGAT.1","ACTCGCACAGTTCG.1","AAGATGGATTCCGC.1","ACCTCCGAAACCGT.1","ACGGCTCTCTACCC.1","ATATGCCTGATAGA.1","ATCACGGAAAGGCG.1","GCGGGACTGTTGCA.1","CGAATCGATGGATC.1","CATGCCACTATCTC.1","TGTGATCTGTTACG.1","AGATATTGTCCTGC.1","TGCAAGACAGGCGA.1","TTTAGCTGTACTTC.1","AACCGCCTTCTCCG.1","TACTTGACTCCTAT.1","GTCCCATGCTCAGA.1","CTTGAACTTTCATC.1","CACAATCTTGTCCC.1","AGACTTCTGAGACG.1","TAGTCACTAAGGGC.1","TCACTATGGCATAC.1","TCTCTAGAGGACAG.1","GTTATAGACTCAGA.1","TGAATAACAACAGA.1","GAGGGCCTTCCCGT.1","TACCGAGAAATCGC.1","GACGTAACTCGATG.1","TGTAGGTGTGTGGT.1","TCCGAAGACTCGCT.1","TCAGCAGATTCATC.1","ATAGTTGACGTGAT.1","TCTACAACTGAAGA.1","TCAATCACAAGATG.1","GTTATCTGGTAGGG.1","CACAGCCTCGGTAT.1","ATTGATGATTTGTC.1","CACTAACTATCAGC.1","ATCACGGACTCCCA.1","GAGGACGAGGTTAC.1","GAGGGAACGGACAG.1","GGACCCGAACCTCC.1","TGACGAACCGGGAA.1","GCGTAAACACAGCT.1","TACTACACGTATCG.1","ATTCTGACTTTCAC.1","TTCATTCTCGTAAC.1","TTGGGAACCAGATC.1","AACACGTGTACGCA.1","TCTCCACTCTCTTA.1","ACTCTCCTAGTTCG.1","GCTCACTGAAGGTA.1","GGGCAAGAGTTAGC.1","CGCGGATGCCTCAC.1","GCCCAACTTCCAGA.1","CCAGCGGAACGCAT.1","CCGCTATGGAATGA.1","AGTAGAGAATCGAC.1","GTGGAGGAGTACAC.1","TATGGGACAAGGCG.1","CGTAACGACAGAAA.1","AAATCAACGGGAGT.1","ATATGAACTATGGC.1","TGACGAACATGTCG.1","CATAACCTTCCAAG.1","CGTACCTGCTTGGA.1","CGTCGACTCGTGAT.1","GTACGTGAGGTAGG.1","TATTGCTGACCCTC.1","CTTGATTGTTGCAG.1","GGCGCATGATGGTC.1","CTTAGGGATACAGC.1","TTTAGAGACTGCAA.1","AGTCAGACCGACAT.1","TATGCGGATCGTTT.1","ATGTCGGAAGAAGT.1","CAATAATGGGAGCA.1","TCACCGTGCTCAGA.1","CCTCTACTGGTAGG.1","CATACTACTGGTAC.1","AACGGTTGCCATAG.1","CCTATAACTTGCAG.1","AGAAACGATCGATG.1","ACTTCTGAGCATCA.1","TGTATGCTCCTACC.1","CGCATAGAGGTAGG.1","TTACTCGACTACCC.1","CAATGGACTGCACA.1","CTTAAGCTTTAGGC.1","ACAGGTACCCTTTA.1","ATCGTTTGCGAACT.1","ATGAAACTAACCAC.1","AACATTGATTCTCA.1","GCCTCATGTTCCAT.1","GGAACTTGTTTCTG.1","ACGAGTACCAGAGG.1","TCCTAAACGCCAAT.1","TATCTCGAGTCTAG.1","AGGGCCACCTGCTC.1","AATTGATGACCCTC.1","CACTAGGAAGAGAT.1","TTTCAGTGGAGATA.1","TGCACGCTCGGTAT.1","AACTGTCTGTTGCA.1","GTTACTACATTTCC.1","GTAGCTGAGAGCAG.1","GCAGATACAATGCC.1","AGCATTCTGGTAGG.1","TCTTCAGAGGCATT.1","GCCAAAACCAGTCA.1","CGGATATGGCTATG.1","GGTATCGATCTGGA.1","ATTTCTCTCCTATT.1","ACAGTGTGCCTGTC.1","CACAACGACTGTTT.1","GCACGGACTCAAGC.1","GGTACAACGACGTT.1","ATTTCGTGGGAAGC.1","GTATCACTTGGTTG.1","TAGTATGACATCAG.1","GTAGCTGATAACCG.1","CATGGATGGCAAGG.1","GAGAAATGCGCTAA.1","CTGAAGACACCTAG.1","AATGGCTGCCAAGT.1","CCTAGAGACCATGA.1","GTACGTGATCCGAA.1","GACACTGATTGGCA.1","GACAGGGACCGCTT.1","TCATTGACGAACCT.1","GTAGTGACACGCAT.1","CGTAACGAAAGAGT.1","ACCATTTGCTATTC.1","CGGACCGAAGGCGA.1","TAAGGGCTTCTTCA.1","TTTATCCTACGGGA.1","TAGGTGTGTTTGCT.1","CGATCCACCATCAG.1","TGCGCACTTTGTGG.1","AGTCTACTGAGGAC.1","TCCTAAACTAAAGG.1","GATTGGTGCATGGT.1","GTAGCATGTGTCTT.1","ATCACACTCGACAT.1","ACGGTAACCGTGTA.1","CGAAGGGATGGTGT.1","GCACTAGACTACCC.1","CTAGAGACTAGAGA.1","CAATATGATACTCT.1","ACGACAACCTACCC.1","TAACACCTTCCGAA.1","GGACTATGTCCTAT.1","TTTATCCTTAGACC.1","TAATGAACCAGCTA.1","AAGAAGACTCCTGC.1","ACGGTATGACTAGC.1","CTGATTTGTCAGAC.1","GGAGAGACGGTGGA.1","AAAGTTTGTCCTAT.1","TAGAATTGCGACAT.1","AGTAATACATCGTG.1","GTGTAGTGGTCTTT.1","TGCGTAGAGTTCAG.1","GAGGTACTTCGCCT.1","TTATCCGATCCTGC.1","TTCCAAACAACGGG.1","CCATTAACGGATCT.1","TTATTCCTGGTACT.1","GCACACCTTGGAGG.1","AGTGTTCTTTCTAC.1","ACTTGTACGTTGGT.1","ATACCACTGTTACG.1","GAGGGATGCCATAG.1","TTCATGACTGCTCC.1","ACTGCCTGATCAGC.1","CATTTGACACGGAG.1","GAACCAACGTCAAC.1","CACAGAACCTATTC.1","CGTGAAACTCCAGA.1","AACGTTCTCCCTCA.1","TGTAGTCTCATACG.1","AGTAGGCTGCGAAG.1","AATGTTGAAGTTCG.1","TGCGTAGACCTCCA.1","ATGCGCCTGTTTCT.1","GAGATGCTCTTCCG.1","GTCACAGAACGGAG.1","AGTAGAGATGGTCA.1","CCACTTCTTTCGTT.1","CTCATTGAACCGAT.1","GCAACCCTCAACCA.1","AGCGGCTGCAGTTG.1","GAGTCAACTTCACT.1","TACTCAACATCGTG.1","CATACTACTGACCA.1","GCACCTTGGGCATT.1","GTGGTAACCTTTAC.1","TCGCACACATCGGT.1","ATCTTTCTACTAGC.1","ATGCAGTGAGAAGT.1","ACAGCAACTGCTGA.1","CGAATCGAGTTGCA.1","TCTTCAGACGTGAT.1","AGCTTACTCCCACT.1","CTGACAGAAACCAC.1","TCATTCGATAGAGA.1","ACTTAAGATTGGTG.1","AGCAAGCTGGACTT.1","CGTAGCCTTGACCA.1","GCAGTCCTAAGTGA.1","AGAGTCACTAAGCC.1","ATGTTAGAACGCTA.1","CATTCCCTCAGTCA.1","ATACGGACGAGATA.1","CGAGCCGACGAATC.1","GGCTAAACGACGGA.1","GCGTATGAGCCTTC.1","GATAAGGAATTCCT.1","ATACTCTGGGAAGC.1","CAGGTATGTTGAGC.1","CTGAATCTCGAGTT.1","GAGGATCTCACAAC.1","TAACATGAGGGCAA.1","TTCCATGAAAGTGA.1","GGGTAACTGTTTGG.1","AGGCAGGATGATGC.1","CACAGATGGGTTTG.1","CACAGAACCGAGTT.1","AGAGCGGAACAGTC.1","TGGTACGACTGGAT.1","TCCCATCTGAGGTG.1","TGACGCCTCAGATC.1","GGAGCGCTGGGTGA.1","AGAAGATGTAAAGG.1","ATTAGTGAGCGGAA.1","CTTTACGAACGGGA.1","TCGCCATGTGCACA.1","TTCAGTTGTCTAGG.1","CATTGACTTCACCC.1","GATAGAGAGCTATG.1","GACATTCTGACTAC.1","CATCATACTGAGAA.1","TGCGCACTCGAGAG.1","GCGGACTGTACTGG.1","GGCGACTGTGTCGA.1","ATCGACGAGGGTGA.1","GGTAAAGAAGTAGA.1","GAGGATCTTTTCAC.1","CATACTACTCCCGT.1","GAGTTGTGAGGCGA.1","GTGATCGAAGAGTA.1","TGCGATGAGAAAGT.1","TAGGACTGGGAAGC.1","GCAAGACTAAACAG.1","GCAGCCGATAAGCC.1","CACCCATGCTAGAC.1","ACGCTGCTGTCTAG.1","CAGTTTACATTCTC.1","TCAGCGCTATTCGG.1","ACTCAGGAGACGGA.1","ATGCAGACTTCATC.1","GGAGACGAGGTACT.1","GATGCATGAGAAGT.1","CGGCACGACGGTAT.1","CAGGTATGTCCCGT.1","GATTCTACCCTACC.1","AGTATCCTGTTCGA.1","TCCCGATGGAGGCA.1","GTTGGATGAAGGTA.1","ATACCTACAAGGTA.1","CACGGGTGACGGTT.1","ACGTGCCTGAAACA.1","ATGACGTGCACTGA.1","GAAGTAGAACTGGT.1","GAGTGTTGGTTGAC.1","AGAAACGATCCTAT.1","ACCCACTGAGAAGT.1","CCCAACTGACACTG.1","CATAAATGACCACA.1","CCAGATGAGTCGTA.1","ATTTCCGATGTCAG.1","GGGAAGTGCATGGT.1","CTACTCCTCACTGA.1","ATGAGAGAGCATCA.1","ATCACACTCCGTTC.1","TACTACACTGTCCC.1","GTGTCAGAGGTGTT.1","CATGTTACCCTTTA.1","CAGGCCGACTGCTC.1","CATGCGCTCAGGAG.1","TGGTAGACACCACA.1","ACCGCGGAAACAGA.1","GAAAGTGAAGTCGT.1","CGCTACACTTCAGG.1","GAGGGCCTGCTTCC.1","GGGCCAACTCCCGT.1" 2 | 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3 | 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-------------------------------------------------------------------------------- 1 | cd14_monocytes,b_cells,cd56_nk 2 | AIF1,CD37,CLIC3 3 | CST3,CD74,CST7 4 | FCN1,CD79A,FGFBP2 5 | FTH1,CD79B,GNLY 6 | FTL,HLA-DPA1,GZMA 7 | GPX1,HLA-DRA,GZMB 8 | LST1,,HOPX 9 | LYZ,,IFITM2 10 | S100A8,,KLRB1 11 | S100A9,,NKG7 12 | TYMP,,PRF1 13 | -------------------------------------------------------------------------------- /man/SCINA.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/EM_model.R 3 | \name{SCINA} 4 | \alias{SCINA} 5 | \title{A semi-supervised category identification and assignment tool.} 6 | \usage{ 7 | SCINA(exp, signatures, max_iter = 100, convergence_n = 10, convergence_rate = 0.99, 8 | sensitivity_cutoff = 1, rm_overlap = 1, allow_unknown = 1, log_file = "SCINA.log") 9 | } 10 | \arguments{ 11 | \item{exp}{A normalized matrix representing the target dataset. Columns correpond to objects (cell barcodes for example), rows correspond to attributes or variables (gene symbols for example).} 12 | 13 | \item{signatures}{A list contains multiple signature identifier lists. Each signature identifier list (genes for example) represents prior knowledge for one category (cell type for example), containing genes or protein symbols with high degree of detection.} 14 | 15 | \item{max_iter}{An integer > 0. Default is 100. Max iterations allowed for the EM algorithm.} 16 | 17 | \item{convergence_rate}{A float between 0 and 1. Default is 0.99. Percentage of cells for which the type assignment remains stable for the last n rounds.} 18 | 19 | \item{sensitivity_cutoff}{A float between 0 and 1. Default is 1. The cutoff to remove signatures whose cells types are deemed as non-existent at all in the data by the SCINA algorithm.} 20 | 21 | \item{rm_overlap}{A binary value, default 1 (TRUE), denotes that shared symbols between signature lists will be removed. If 0 (FALSE) then allows different cell types to share the same identifiers.} 22 | 23 | \item{allow_unknown}{A binary value, default 1 (TRUE). If 0 (FALSE) then no cell will be assigned to the 'unknown' category.} 24 | 25 | \item{convergence_n}{An integer > 0. Default is 10. Stop the EM algorithm if during the last n rounds of iterations, cell type assignment keeps steady above the convergence_rate.} 26 | 27 | \item{log_file}{A string names the record of the running status of the SCINA algorithem, default 'SCINA.log'.} 28 | } 29 | \details{ 30 | More detailed information can be found from our web server: \url{ 31 | http://lce.biohpc.swmed.edu/scina/. 32 | } 33 | 34 | For any symbols in signature lists, if the category is identified with symbol X's 35 | low detection level, please specify the symbol as 'low_X'. The name for the list is the category. 36 | 37 | Details for 'low_X' (take scRNA-Seqs as an example):\cr 38 | (a) There are 4 cell types, the first one highly express one gene A, and the other three lowly express the same gene. 39 | Then it is better to specify A as the high marker for cell type 1, but it is not a good idea to specify A as the low 40 | expression marker for cell type 2,3,4.\cr 41 | (b) There are 4 cell types, the first one lowly express one gene A, and the other three highly express the same gene. 42 | Then is it better to specify A as the low marker for cell type 1, but it is not a good idea to specify A as the 43 | high expression marker for cell type 2,3,4.\cr 44 | (c) There are 4 cell types, the first one lowly express one gene A, the second and third one moderately express gene A, 45 | and the last one highly express gene A. Then is it better to specify A as the low marker for cell type 1, and as the high 46 | expression marker for cell type 4.\cr 47 | (d) The same specification can be applied to protein markers in CyTOF anlysis.\cr 48 | For immune cell types, an example signature lists can be found in \code{\link[DisHet]{eTME_signatures}.} 49 | 50 | Small sensitivity_cutoff leads to more signatures to be removed, and 1 denotes that no signature is removed. 51 | } 52 | \value{ 53 | cell_labels return a vector contains cell type mapping results for each cell. 54 | 55 | probabilities return a probability matrix indicating the predicted probability for each cell belongs to each cell type respectively. 56 | } 57 | \description{ 58 | An automatic cell type detection and assignment algorithm for single cell RNA-Seq (scRNA-seq) and Cytof/FACS data. 59 | } 60 | \examples{ 61 | load(system.file('extdata','example_expmat.RData', package = "SCINA")) 62 | load(system.file('extdata','example_signatures.RData', package = "SCINA")) 63 | exp = exp_test$exp_data 64 | results = SCINA(exp, signatures, max_iter = 120, convergence_n = 12, 65 | convergence_rate = 0.999, sensitivity_cutoff = 0.9) 66 | table(exp_test$true_label, results$cell_labels) 67 | } 68 | -------------------------------------------------------------------------------- /man/figures/sub1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/man/figures/sub1.png -------------------------------------------------------------------------------- /man/figures/sub2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/man/figures/sub2.png -------------------------------------------------------------------------------- /man/figures/sub3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/man/figures/sub3.png -------------------------------------------------------------------------------- /man/figures/sub4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/man/figures/sub4.png -------------------------------------------------------------------------------- /man/plotheat.SCINA.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/EM_functions.R 3 | \name{plotheat.SCINA} 4 | \alias{plotheat.SCINA} 5 | \title{A function to plot SCINA results in a heatmap.} 6 | \usage{ 7 | plotheat.SCINA(exp, results, signatures) 8 | } 9 | \arguments{ 10 | \item{exp}{See more details in \code{\link{SCINA}}} 11 | 12 | \item{results}{An output object returned from SCINA.} 13 | 14 | \item{signatures}{See more details in \code{\link{SCINA}}} 15 | } 16 | \value{ 17 | Plot a heatmap showing signature genes' expression level and SCINA predicted cell types. 18 | } 19 | \description{ 20 | A function to plot SCINA results in a heatmap. 21 | } 22 | -------------------------------------------------------------------------------- /man/preprocess.signatures.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/EM_functions.R 3 | \name{preprocess.signatures} 4 | \alias{preprocess.signatures} 5 | \title{A function to convert signatures uploaded via .csv files to lists used by SCINA.} 6 | \usage{ 7 | preprocess.signatures(file_path) 8 | } 9 | \arguments{ 10 | \item{file_path}{The path of the .csv file. The first row of the file should be cell type names. Each column is occupied by the signature genes/protein markers for the cell type in the first row. Please find more details in \code{\link{SCINA}}.} 11 | } 12 | \value{ 13 | A list of signature gene lists as an input for SCINA. 14 | } 15 | \description{ 16 | A function to convert signatures uploaded via .csv files to lists used by SCINA. 17 | } 18 | -------------------------------------------------------------------------------- /plot_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcao89757/SCINA/a78956355f68bbb7eb5110df5456a585ca5320b8/plot_example.jpg --------------------------------------------------------------------------------