--- /dev/null
+#!/usr/bin/env Rscript
+
+result <- suppressWarnings(tryCatch({
+ library("EBSeq", lib.loc = ".")
+ }, error = function(err) {
+ tryCatch({
+ source("http://www.bioconductor.org/biocLite.R")
+ biocLite("EBSeq", lib = ".")
+ library("EBSeq", lib.loc = ".")
+ }, error = function(err) {
+ tryCatch({
+ cat("Failed to install the latest version of EBSeq from Bioconductor! Try to install EBSeq v1.1.5 locally instead.\n")
+ install.packages(c("blockmodeling_0.1.8.tar.gz", "EBSeq_1.1.5.tar.gz"), lib = ".", repos = NULL)
+ library("EBSeq", lib.loc = ".")
+ }, error = function(err) { cat("Failed to install EBSeq v1.1.5 locally!\n") })
+ })
+ }))
+
+
+++ /dev/null
-#!/usr/bin/env Rscript
-
-result <- suppressWarnings(tryCatch({
- library("EBSeq", lib.loc = ".")
- }, error = function(err) {
- tryCatch({
- source("http://www.bioconductor.org/biocLite.R")
- biocLite("EBSeq", lib = ".")
- library("EBSeq", lib.loc = ".")
- }, error = function(err) {
- tryCatch({
- cat("Failed to install the latest version of EBSeq from Bioconductor! Try to install EBSeq v1.1.5 locally instead.\n")
- install.packages(c("blockmodeling_0.1.8.tar.gz", "EBSeq_1.1.5.tar.gz"), lib = ".", repos = NULL)
- library("EBSeq", lib.loc = ".")
- }, error = function(err) { cat("Failed to install EBSeq v1.1.5 locally!\n") })
- })
- }))
-
-
all : $(PROGRAMS)
EBSeq :
- ./install.R
+ ./install
rsem-for-ebseq-calculate-clustering-info : calcClusteringInfo.cpp
$(CC) -O3 -Wall calcClusteringInfo.cpp -o $@
--- /dev/null
+#!/usr/bin/env Rscript
+
+argv <- commandArgs(TRUE)
+if (length(argv) < 6) {
+ cat("Usage: rsem-for-ebseq-find-DE path ngvector_file data_matrix_file output_file number_of_replicate_for_condition_1 number_of_replicate_for_condition_2 ...\n")
+ q(status = 1)
+}
+
+path <- argv[1]
+ngvector_file <- argv[2]
+data_matrix_file <- argv[3]
+output_file <- argv[4]
+
+nc <- length(argv) - 4;
+num_reps <- as.numeric(argv[5:(5+nc-1)])
+
+library(EBSeq, lib.loc = path)
+
+DataMat <- data.matrix(read.table(data_matrix_file))
+n <- dim(DataMat)[2]
+if (sum(num_reps) != n) stop("Total number of replicates given does not match the number of columns from the data matrix!")
+
+conditions <- as.factor(rep(paste("C", 1:nc, sep=""), times = num_reps))
+Sizes <- MedianNorm(DataMat)
+ngvector <- NULL
+if (ngvector_file != "#") {
+ ngvector <- as.vector(data.matrix(read.table(ngvector_file)))
+ stopifnot(!is.null(ngvector))
+}
+
+if (nc == 2) {
+ EBOut <- NULL
+ EBOut <- EBTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, sizeFactors = Sizes, maxround = 5)
+ stopifnot(!is.null(EBOut))
+
+ PP <- as.data.frame(GetPPMat(EBOut))
+ fc_res <- PostFC(EBOut)
+
+ results <- cbind(PP, fc_res$PostFC, fc_res$RealFC)
+ colnames(results) <- c("PPEE", "PPDE", "PostFC", "RealFC")
+ results <- results[order(results[,"PPDE"], decreasing = TRUE),]
+ write.table(results, file = output_file, sep = "\t")
+
+} else {
+ patterns <- GetPatterns(conditions)
+ eename <- rownames(patterns)[which(rowSums(patterns) == nc)]
+ stopifnot(length(eename) == 1)
+
+ MultiOut <- NULL
+ MultiOut <- EBMultiTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, AllParti = patterns, sizeFactors = Sizes, maxround = 5)
+ stopifnot(!is.null(MultiOut))
+
+ MultiPP <- GetMultiPP(MultiOut)
+
+ PP <- as.data.frame(MultiPP$PP)
+ pos <- which(names(PP) == eename)
+ probs <- rowSums(PP[,-pos])
+
+ results <- cbind(PP, MultiPP$MAP[rownames(PP)], probs)
+ colnames(results) <- c(colnames(PP), "MAP", "PPDE")
+ results <- results[order(results[,"PPDE"], decreasing = TRUE),]
+ write.table(results, file = output_file, sep = "\t")
+
+ write.table(MultiPP$Patterns, file = paste(output_file, ".pattern", sep = ""), sep = "\t")
+}
DESeq do not take variance due to read mapping uncertainty into
consideration. Because read mapping ambiguity is prevalent among
isoforms and de novo assembled transcripts, these tools are not ideal
-for DE detection in such conditions.
-
-**EBSeq**, an empirical Bayesian DE analysis tool developed in
-UW-Madison, can take variance due to read mapping ambiguity into
-consideration by grouping isoforms with parent gene's number of
-isoforms. In addition, it is more robust to outliers. For more
-information about EBSeq (including the paper describing their method),
-please visit <a
+for DE detection in such conditions.
+
+EBSeq, an empirical Bayesian DE analysis tool developed in UW-Madison,
+can take variance due to read mapping ambiguity into consideration by
+grouping isoforms with parent gene's number of isoforms. In addition,
+it is more robust to outliers. For more information about EBSeq
+(including the paper describing their method), please visit <a
href="http://www.biostat.wisc.edu/~ningleng/EBSeq_Package">EBSeq
website</a>.
-RSEM includes the newest version of EBSeq in its folder
-named 'EBSeq'. To use it, first type
+RSEM includes EBSeq in its folder named 'EBSeq'. To use it, first type
make ebseq
NgVec <- scan(file="output_name.ngvec", what=0, sep="\n")
-. After that, replace 'IsoNgTrun' with 'NgVec' in the second line of
-section 3.2.5 (Page 10) of EBSeq's vignette:
+. After that, set "NgVector = NgVec" for your differential expression
+test (either 'EBTest' or 'EBMultiTest').
- IsoEBres=EBTest(Data=IsoMat, NgVector=NgVec, ...)
For users' convenience, RSEM also provides a script
'rsem-generate-data-matrix' to extract input matrix from expression
IsoMat <- data.matrix(read.table(file="output_name.counts.matrix"))
-before running function 'EBTest'.
-
-At last, RSEM provides a R script, 'rsem-find-DE', which run EBSeq for
-you.
-
-Usage:
-
- rsem-find-DE data_matrix_file [--ngvector ngvector_file] number_of_samples_in_condition_1 FDR_rate output_file
-
-This script calls EBSeq to find differentially expressed genes/transcripts in two conditions.
-
-data_matrix_file: m by n matrix containing expected counts, m is the number of transcripts/genes, n is the number of total samples.
-[--ngvector ngvector_file]: optional field. 'ngvector_file' is calculated by 'rsem-generate-ngvector'. Having this field is recommended for transcript data.
-number_of_samples_in_condition_1: the number of samples in condition 1. A condition's samples must be adjacent. The left group of samples are defined as condition 1.
-FDR_rate: false discovery rate.
-output_file: the output file. Three files will be generated: 'output_file', 'output_file.hard_threshold' and 'output_file.all'. The first file reports all DE genes/transcripts using a soft threshold (calculated by crit_func in EBSeq). The second file reports all DE genes/transcripts using a hard threshold (only report if PPEE <= fdr). The third file reports all genes/transcripts. The first file is recommended to be used as DE results because it generally contains more called genes/transcripts.
+before running either 'EBTest' or 'EBMultiTest'.
-The results are written as a matrix with row and column names. The row names are the differentially expressed transcripts'/genes' ids. The column names are 'PPEE', 'PPDE', 'PostFC' and 'RealFC'.
+Lastly, RSEM provides two scripts, 'rsem-run-ebseq' and
+'rsem-control-fdr', to help users find differential expressed
+genes. First, 'rsem-run-ebseq' calls EBSeq to calculate related statistics
+for all genes/transcripts. Run
-PPEE: posterior probability of being equally expressed.
-PPDE: posterior probability of being differentially expressed.
-PostFC: posterior fold change (condition 1 over condition2).
-RealFC: real fold change (condition 1 over condition2).
+ rsem-run-ebseq --help
-To get the above usage information, type
+to get usage information or visit the [rsem-run-ebseq documentation
+page](http://deweylab.biostat.wisc.edu/rsem/rsem-run-ebseq.html). Second,
+'rsem-control-fdr' takes 'rsem-run-ebseq' 's result and reports called
+differentially expressed genes/transcripts by controlling the false
+discovery rate. Run
- rsem-find-DE
+ rsem-control-fdr --help
-Note: any wrong parameter setting will lead 'rsem-find-DE' to output
-usage information and halt.
+to get usage information or visit the [rsem-control-fdr documentation
+page](http://deweylab.biostat.wisc.edu/rsem/rsem-control-fdr.html). These
+two scripts can perform DE analysis on either 2 conditions or multiple
+conditions.
Questions related to EBSeq should
be sent to <a href="mailto:nleng@wisc.edu">Ning Leng</a>.
=head1 SYNOPSIS
-=over
-
- convert-sam-for-rsem [options] <input.sam/input.bam> output_file_name
-
-=back
+convert-sam-for-rsem [options] <input.sam/input.bam> output_file_name
=head1 ARGUMENTS
=head1 SYNOPSIS
-=over
-
- rsem-calculate-expression [options] upstream_read_file(s) reference_name sample_name
- rsem-calculate-expression [options] --paired-end upstream_read_file(s) downstream_read_file(s) reference_name sample_name
+ rsem-calculate-expression [options] upstream_read_file(s) reference_name sample_name
+ rsem-calculate-expression [options] --paired-end upstream_read_file(s) downstream_read_file(s) reference_name sample_name
rsem-calculate-expression [options] --sam/--bam [--paired-end] input reference_name sample_name
-=back
-
=head1 ARGUMENTS
=over
--- /dev/null
+#!/usr/bin/perl
+
+use Getopt::Long;
+use Pod::Usage;
+use strict;
+
+my $hard = 0;
+my $soft = 0;
+my $help = 0;
+
+GetOptions("hard-threshold" => \$hard,
+ "soft-threshold" => \$soft,
+ "h|help" => \$help) or pod2usage(-exitval => 2, -verbose => 2);
+
+pod2usage(-verbose => 2) if ($help == 1);
+pod2usage(-msg => "Invalid number of arguments!", -exitval => 2, -verbose => 2) if (scalar(@ARGV) != 3);
+pod2usage(-msg => "--hard-threshold and --soft-threshold cannot be set at the same time!", -exitval => 2, -verbose => 2) if ($hard && $soft);
+
+if ($hard == 0 && $soft == 0) { $hard = 1; }
+
+my $fdr = $ARGV[1];
+
+open(INPUT, "$ARGV[0]");
+open(OUTPUT, ">$ARGV[2]");
+
+my $header = <INPUT>;
+chomp($header);
+my @columns = split(/\t/, $header);
+
+my $pos = 0;
+while ($pos <= $#columns && $columns[$pos] ne "\"PPDE\"") { ++$pos; }
+if ($pos > $#columns) { print "Error: Cannot find column PPDE!\n"; exit(-1); }
+++$pos;
+
+print OUTPUT "$header\n";
+
+my ($n, $sum) = (0, 0);
+my $line = "";
+while($line = <INPUT>) {
+ chomp($line);
+ my @fields = split(/\t/, $line);
+ my $ppee = 1.0 - $fields[$pos];
+ if ($hard) {
+ if ($ppee > $fdr) { last; }
+ ++$n;
+ print OUTPUT "$line\n";
+ }
+ else {
+ if ($sum + $ppee > $fdr * ($n + 1)) { last; }
+ ++$n;
+ $sum += $ppee;
+ print OUTPUT "$line\n";
+ }
+}
+
+print "There are $n genes/transcripts reported at FDR = $fdr.\n";
+
+close(INPUT);
+close(OUTPUT);
+
+__END__
+
+=head1 NAME
+
+rsem-control-fdr
+
+=head1 SYNOPSIS
+
+rsem-control-fdr [options] input_file fdr_rate output_file
+
+=head1 ARGUMENTS
+
+=over
+
+=item B<input_file>
+
+This should be the result file generated by 'rsem-run-ebseq', which contains all genes/transcripts and their associated statistics.
+
+=item B<fdr_rate>
+
+The desire false discovery rate (FDR).
+
+=item B<output_file>
+
+This file is a subset of the 'input_file'. It only contains the genes/transcripts called as differentially expressed (DE). When more than 2 conditions exist, DE is defined as not all conditions are equally expressed.
+
+=back
+
+=head1 OPTIONS
+
+=over
+
+=item B<--hard-threshold>
+
+Use hard threshold method to control FDR. If this option is set, only those genes/transcripts with their PPDE >= 1 - fdr_rate are called as DE. (Default: on)
+
+=item B<--soft-threshold>
+
+Use soft threshold method to control FDR. If this option is set, this program will try to report as many genes/transcripts as possible, as long as their average PPDE >= 1 - fdr_rate. (Default: off)
+
+=item B<-h/--help>
+
+Show help information.
+
+=back
+
+=head1 DESCRIPTION
+
+This program controls the false discovery rate and reports differentially expressed genes/transcripts.
+
+=head1 EXAMPLES
+
+We assume that we have 'GeneMat.results' as input. We want to control FDR at 0.05 using hard threshold method and name the output file as 'GeneMat.de.txt':
+
+ rsem-control-fdr GeneMat.results 0.05 GeneMat.de.txt
+
+If we instead want to use soft threshold method to obtain more called genes/transcripts, we can use:
+
+ rsem-control-fdr --soft-threshold GeneMat.results 0.05 GeneMat.de.txt
+
+=cut
+++ /dev/null
-#!/usr/bin/env Rscript
-
-printUsage <- function() {
- cat("Usage: rsem-find-DE data_matrix_file [--ngvector ngvector_file] number_of_samples_in_condition_1 FDR_rate output_file\n\n")
- cat("This script calls EBSeq to find differentially expressed genes/transcripts in two conditions.\n\n")
- cat("data_matrix_file: m by n matrix containing expected counts, m is the number of transcripts/genes, n is the number of total samples.\n")
- cat("[--ngvector ngvector_file]: optional field. 'ngvector_file' is calculated by 'rsem-generate-ngvector'. Having this field is recommended for transcript data.\n")
- cat("number_of_samples_in_condition_1: the number of samples in condition 1. A condition's samples must be adjacent. The left group of samples are defined as condition 1.\n")
- cat("FDR_rate: false discovery rate.\n")
- cat("output_file: the output file. Three files will be generated: 'output_file', 'output_file.hard_threshold' and 'output_file.all'. The first file reports all DE genes/transcripts using a soft threshold (calculated by crit_func in EBSeq). The second file reports all DE genes/transcripts using a hard threshold (only report if PPEE <= fdr). The third file reports all genes/transcripts. The first file is recommended to be used as DE results because it generally contains more called genes/transcripts.\n\n")
- cat("The results are written as a matrix with row and column names. The row names are the genes'/transcripts' ids. The column names are 'PPEE', 'PPDE', 'PostFC' and 'RealFC'.\n\n")
- cat("PPEE: posterior probability of being equally expressed.\n")
- cat("PPDE: posterior probability of being differentially expressed.\n")
- cat("PostFC: posterior fold change (condition 1 over condition2).\n")
- cat("RealFC: real fold change (condition 1 over condition2).\n")
- q(status = 1)
-}
-
-argv <- commandArgs(FALSE)
-path <- dirname(sub("--file=", "", argv[grep("--file", argv)]))
-path <- paste(path, "EBSeq", sep = "/")
-start_pos <- grep("--args", argv) + 1
-end_pos <- length(argv)
-if (start_pos <= end_pos) argv <- argv[start_pos : end_pos] else argv <- NULL
-
-ng_pos <- grep("--ngvector", argv)
-if (length(ng_pos) > 1 || length(ng_pos) == 0 && length(argv) != 4 || length(ng_pos) == 1 && length(argv) != 6) printUsage()
-ngvector_file <- NULL
-if (length(ng_pos) == 1) {
- if (ng_pos == length(argv)) printUsage()
- ngvector_file <- argv[ng_pos + 1]
- argv <- argv[-c(ng_pos, ng_pos + 1)]
-}
-
-data_matrix_file <- argv[1]
-num_con1 <- as.numeric(argv[2])
-fdr <- as.numeric(argv[3])
-output_file <- argv[4]
-
-library(blockmodeling, lib.loc = path)
-library(EBSeq, lib.loc = path)
-
-DataMat <- data.matrix(read.table(data_matrix_file))
-n <- dim(DataMat)[2]
-conditions <- as.factor(rep(c("C1", "C2"), times = c(num_con1, n - num_con1)))
-Sizes <- MedianNorm(DataMat)
-ngvector <- NULL
-if (!is.null(ngvector_file)) {
- ngvector <- as.vector(data.matrix(read.table(ngvector_file)))
-}
-
-
-EBOut <- NULL
-if (is.null(ngvector)) {
- EBOut <- EBTest(Data = DataMat, Conditions = conditions, sizeFactors = Sizes, maxround = 5)
-} else {
- EBOut <- EBTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, sizeFactors = Sizes, maxround = 5)
-}
-stopifnot(!is.null(EBOut))
-
-PP <- as.data.frame(GetPPMat(EBOut))
-fc_res <- PostFC(EBOut)
-
-# soft threshold, default output
-thre <- crit_fun(PP[, "PPEE"], fdr)
-DEfound <- rownames(PP)[which(PP[, "PPDE"] >= thre)]
-
-results <- cbind(PP[DEfound, ], fc_res$PostFC[DEfound], fc_res$RealFC[DEfound])
-colnames(results) <- c("PPEE", "PPDE", "PostFC", "RealFC")
-write.table(results, file = output_file)
-
-# hard threshold
-thre <- 1.0 - fdr
-DEfound <- rownames(PP)[which(PP[, "PPDE"] >= thre)]
-
-results <- cbind(PP[DEfound, ], fc_res$PostFC[DEfound], fc_res$RealFC[DEfound])
-colnames(results) <- c("PPEE", "PPDE", "PostFC", "RealFC")
-write.table(results, file = paste(output_file, ".hard_threshold", sep = ""))
-
-# all
-results <- cbind(PP, fc_res$PostFC, fc_res$RealFC)
-colnames(results) <- c("PPEE", "PPDE", "PostFC", "RealFC")
-write.table(results, file = paste(output_file, ".all", sep = ""))
=head1 SYNOPSIS
-=over
-
- rsem-generate-ngvector [options] input_fasta_file output_name
-
-=back
+rsem-generate-ngvector [options] input_fasta_file output_name
=head1 ARGUMENTS
=head1 SYNOPSIS
-=over
-
- rsem-plot-transcript-wiggles [options] sample_name input_list output_plot_file
-
-=back
+rsem-plot-transcript-wiggles [options] sample_name input_list output_plot_file
=head1 ARGUMENTS
=head1 SYNOPSIS
-=over
-
- rsem-prepare-reference [options] reference_fasta_file(s) reference_name
-
-=back
+rsem-prepare-reference [options] reference_fasta_file(s) reference_name
=head1 ARGUMENTS
--- /dev/null
+#!/usr/bin/perl
+
+use Getopt::Long;
+use Pod::Usage;
+use FindBin;
+use lib $FindBin::Bin;
+use strict;
+
+use rsem_perl_utils;
+
+my $ngvF = "";
+my $help = 0;
+
+GetOptions("ngvector=s" => \$ngvF,
+ "h|help" => \$help) or pod2usage(-exitval => 2, -verbose => 2);
+
+pod2usage(-verbose => 2) if ($help == 1);
+pod2usage(-msg => "Invalid number of arguments!", -exitval => 2, -verbose => 2) if (scalar(@ARGV) != 3);
+pod2usage(-msg => "ngvector file cannot be named as #! # is reserved for other purpose!", -exitval => 2, -verbose => 2) if ($ngvF eq "#");
+
+my $dir = "$FindBin::Bin/";
+my $command = "";
+
+my @conditions = split(/,/, $ARGV[1]);
+
+pod2usage(-msg => "At least 2 conditions are required for differential expression analysis!", -exitval => 2, -verbose => 2) if (scalar(@conditions) < 2);
+
+if ($ngvF eq "") { $ngvF = "#"; }
+
+$" = " ";
+$command = $dir."EBSeq/rsem-for-ebseq-find-DE ".$dir."EBSeq $ngvF $ARGV[0] $ARGV[2] @conditions";
+&runCommand($command)
+
+__END__
+
+=head1 NAME
+
+rsem-run-ebseq
+
+=head1 SYNOPSIS
+
+rsem-run-ebseq [options] data_matrix_file conditions output_file
+
+=head1 ARGUMENTS
+
+=over
+
+=item B<data_matrix_file>
+
+This file is a m by n matrix. m is the number of genes/transcripts and n is the number of total samples. Each element in the matrix represents the expected count for a particular gene/transcript in a particular sample. Users can use 'rsem-generate-data-matrix' to generate this file from expression result files.
+
+=item B<conditions>
+
+Comma-separated list of values representing the number of replicates for each condition. For example, "3,3" means the data set contains 2 conditions and each condition has 3 replicates. "2,3,3" means the data set contains 3 conditions, with 2, 3, and 3 replicates for each condition respectively.
+
+=item B<output_file>
+
+Output file name.
+
+=back
+
+=head1 OPTIONS
+
+=over
+
+=item B<--ngvector> <file>
+
+This option provides the grouping information required by EBSeq for isoform-level differential expression analysis. The file can be generated by 'rsem-generate-ngvector'. Turning this option on is highly recommended for isoform-level differential expression analysis. (Default: off)
+
+=item B<-h/--help>
+
+Show help information.
+
+=back
+
+=head1 DESCRIPTION
+
+This program is a wrapper over EBSeq. It performs differential expression analysis and can work on two or more conditions. All genes/transcripts and their associated statistcs are reported in one output file. This program does not control false discovery rate and call differential expressed genes/transcripts. Please use 'rsem-control-fdr' to control false discovery rate after this program is finished.
+
+=head1 OUTPUT
+
+=over
+
+=item B<output_file>
+
+This file reports the calculated statistics for all genes/transcripts. It is written as a matrix with row and column names. The row names are the genes'/transcripts' names. The column names are for the reported statistics.
+
+If there are only 2 different conditions among the samples, four statistics (columns) will be reported for each gene/transcript. They are "PPEE", "PPDE", "PostFC" and "RealFC". "PPEE" is the posterior probability (estimated by EBSeq) that a gene/transcript is equally expressed. "PPDE" is the posterior probability that a gene/transcript is differentially expressed. "PostFC" is the posterior fold change (condition 1 over condition2) for a gene/transcript. It is defined as the ratio between posterior mean expression estimates of the gene/transcript for each condition. "RealFC" is the real fold change (condition 1 over condition2) for a gene/transcript. It is the ratio of the normalized within condition 1 mean count over normalized within condition 2 mean count for the gene/transcript. Fold changes are calculated using EBSeq's 'PostFC' function. The genes/transcripts are reported in descending order of their "PPDE" values.
+
+If there are more than 2 different conditions among the samples, the output format is different. For differential expression analysis with more than 2 conditions, EBSeq will enumerate all possible expression patterns (on which conditions are equally expressed and which conditions are not). Suppose there are k different patterns, the first k columns of the output file give the posterior probability of each expression pattern is true. Patterns are defined in a separate file, 'output_file.pattern'. The k+1 column gives the maximum a posteriori (MAP) expression pattern for each gene/transcript. The k+2 column gives the posterior probability that not all conditions are equally expressed (column name "PPDE"). The genes/transcripts are reported in descending order of their "PPDE" column values. For details on how EBSeq works for more than 2 conditions, please refer to EBSeq's manual.
+
+=item B<output_file.pattern>
+
+This file is only generated when there are more than 2 conditions. It defines all possible expression patterns over the conditions using a matrix with names. Each row of the matrix refers to a different expression pattern and each column gives the expression status of a different condition. Two conditions are equally expressed if and only if their statuses are the same.
+
+=back
+
+=head1 EXAMPLES
+
+1) We're interested in isoform-level differential expression analysis and there are two conditions. Each condition has 5 replicates. We have already collected the data matrix as 'IsoMat' and generated ngvector as 'ngvector.ngvec':
+
+ rsem-run-ebseq --ngvector ngvector.ngvec IsoMat 5,5 IsoMat.results
+
+The results will be in 'IsoMat.results'.
+
+2) We're interested in gene-level analysis and there are 3 conditions. The first condition has 3 replicates and the other two has 4 replicates each. The data matrix is named as 'GeneMat':
+
+ rsem-run-ebseq GeneMat 3,4,4 GeneMat.results
+
+Two files, 'GeneMat.results' and 'GeneMat.results.pattern', will be generated.
+
+=cut
+
+
+
+
+
projects / rsem.git / commitdiff