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.