Provided by: rsem_1.3.3+dfsg-3build1_amd64 bug

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.

perl v5.38.2                                       2024-04-14                                  RSEM-RUN-EBSEQ(1)