Hi,

I have a rna-seq data consisting of matched tumor/normal samples from two patients.  For normalization of the counts I am following the steps in the TCC vignette section "3.3 Normalization of two-group count data without replicates (paired)".  The output from the commands are as follows:

>  data=read.delim("count_bt2_iGenomes_Ensembl.tsv")

> head(data)
                A.sorted.bam B.sorted.bam
ENSG00000000003                               2400                      1130
ENSG00000000005                                  2                         3
ENSG00000000419                               1819                       575
ENSG00000000457                               1317                      1262
ENSG00000000460                                799                      1743
ENSG00000000938                                203                        41
                C.sorted.bam D.sorted.bam
ENSG00000000003                          12                          72
ENSG00000000005                           0                           0
ENSG00000000419                         938                        1608
ENSG00000000457                         821                        1469
ENSG00000000460                         367                         800
ENSG00000000938                       33303                       16355

> group <- c(1,1,2,2)
> pair <- c(1,2,1,2)
>  c1 <- data.frame(group=group, pair=pair)
> colnames(data) <- c("T_BRPC13.1118", "T_BRPC_13.764", "N_DU04_PBMC", "N_DU06_PBMC")
>  tcc <- new("TCC", data, c1)
> tcc <- calcNormFactors(tcc, norm.method="tmm", test.method="edger", iteration=1, FDR=0.1, floorPDEG=0.05, paired=TRUE)
TCC::INFO: Calculating normalization factors using DEGES
TCC::INFO: (iDEGES pipeline : tmm - [ edger - tmm ] X 1 )
Error in .testByEdger.3(design = design, coef = coef, contrast = contrast) :
  TCC::ERROR: Need the design matrix for GLM.

Reading further for steps needed for edgeR without TCC I saw something related to design and tried it, but got the same error:

> design <- model.matrix(~ group + pair)
>  tcc <- new("TCC", data, c1)
> tcc <- calcNormFactors(tcc, norm.method="tmm", test.method="edger", iteration=1, FDR=0.1, floorPDEG=0.05, paired=TRUE)
TCC::INFO: Calculating normalization factors using DEGES
TCC::INFO: (iDEGES pipeline : tmm - [ edger - tmm ] X 1 )
Error in .testByEdger.3(design = design, coef = coef, contrast = contrast) :
  TCC::ERROR: Need the design matrix for GLM.

I would appreciate help with understanding the cause of the error.

The output from sessionInfo() and package description is as follows:

> sessionInfo()
R version 3.0.3 (2014-03-06)
Platform: x86_64-unknown-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base
>
> packageDescription("TCC")
Package: TCC
Type: Package
Title: TCC: Differential expression analysis for tag count data with
        robust normalization strategies
Version: 1.2.0
Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji
        Kadota
Maintainer: Jianqiang Sun <wukong@bi.a.u-tokyo.ac.jp>, Tomoaki
        Nishiyama <tomoakin@staff.kanazawa-u.ac.jp>
Description: This package provides a series of functions for performing
        differential expression analysis from RNA-seq count data using
        robust normalization strategy (called DEGES). The basic idea of
        DEGES is that potential differentially expressed genes or
        transcripts (DEGs) among compared samples should be removed
        before data normalization to obtain a well-ranked gene list
        where true DEGs are top-ranked and non-DEGs are bottom ranked.
        This can be done by performing a multi-step normalization
        strategy (called DEGES for DEG elimination strategy). A major
        characteristic of TCC is to provide the robust normalization
        methods for several kinds of count data (two-group with or
        without replicates, multi-group/multi-factor, and so on) by
        virtue of the use of combinations of functions in other
        sophisticated packages (especially edgeR, DESeq, and baySeq).
Depends: R (>= 2.15), methods, DESeq, edgeR, baySeq, ROC
Imports: EBSeq, samr
Suggests: RUnit, BiocGenerics
Enhances: snow
biocViews: HighThroughputSequencing, DifferentialExpression, RNAseq
License: GPL-2
Copyright: Authors listed above
Packaged: 2013-10-15 05:31:33 UTC; biocbuild
Built: R 3.0.3; ; 2014-03-31 20:00:49 UTC; unix

-- File: /general/installs/R/R-3.0.3/lib64/R/library/TCC/Meta/package.rds

Thank you,

- Pankaj
--------------------------------------
Pankaj Agarwal, M.S
Bioinformatician
Bioinformatics Shared Resource
Duke Cancer Institute
Duke University
919-681-6573
p.agarwal@duke.edu<mailto:p.agarwal@duke.edu>

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