NAME

       rsem-run-ebseq - Wrapper for EBSeq to perform differential expression analysis.

SYNOPSIS

       rsem-run-ebseq [options] data_matrix_file conditions output_file

ARGUMENTS

       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.

       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.

       output_file
           Output file name.

OPTIONS

       --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)

       -h/--help
           Show help information.

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.

OUTPUT

       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.

       output_file.normalized_data_matrix
           This file contains the median normalized version of the input data matrix.

       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.

       output_file.condmeans
           This file is only generated when there are more than 2 conditions. It gives the normalized mean count
           value for each gene/transcript at each condition. It is formatted as a matrix with names. Each row
           represents a gene/transcript and each column represent a condition. The order of genes/transcripts is
           the same as 'output_file'. This file can be used to calculate fold changes between conditions which
           users are interested in.

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.txt' and generated
       ngvector as 'ngvector.ngvec':

        rsem-run-ebseq --ngvector ngvector.ngvec IsoMat.txt 5,5 IsoMat.results

       The results will be in 'IsoMat.results' and 'IsoMat.results.normalized_data_matrix' contains the
       normalized data matrix.

       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.txt':

        rsem-run-ebseq GeneMat.txt 3,4,4 GeneMat.results

       Four files, 'GeneMat.results', 'GeneMat.results.normalized_data_matrix', 'GeneMat.results.pattern', and
       'GeneMat.results.condmeans', will be generated.