Hi Everyone, I would like to use the CQN package to normalize my RNAseq data prior to network analysis. I've used edgeR and Deseq2 for differential expression analysis. I am following the (CQN vignette), and can run the analysis using their toy data set, but I am getting an error when I try to run it on my own.

Here are the components I need for the analysis: 1. gene by sample count table 2. vector of size factors for each sample 3. a matrix containing length and GC-content for each gene.

I use the DGEList function from EdgeR to make my count table (countsDGE) and calculate the normalization factor for the samples.

countsDGE_cqn <- countsDGE$counts
> str(countsDGE_cqn)
 int [1:22053, 1:24] 394 1 63 946 1003 1142 1072 3444 77 13 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:22053] "497097" "19888" "20671" "27395" ...
  ..$ : chr [1:24] "MSC_21d_IP_1" "MSC_21d_IP_2" "MSC_21d_IP_3" "E12_14d_IP_1" ...
> str(countsDGE$samples$norm.factors)
 num [1:24] 0.8 0.704 0.783 0.785 0.778 ...

I use the getGeneLengthAndGCContent function from the EDASeq package to get the length and GC content matrix.

GC_content <- getGeneLengthAndGCContent(rownames(countsDGE_cqn), "mm10", mode="org.db")
str(GC_content)
 num [1:22053, 1:2] 3634 9747 4340 4203 4128 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:22053] "497097" "19888" "20671" "27395" ...
  ..$ : chr [1:2] "length" "gc"

Then I call the CQN function :

cqn.countsDGE <- cqn(counts = countsDGE_cqn, lengths = GC_content[,1],
                  x = GC_content[,2], sizeFactors = countsDGE$samples$norm.factors,
                  verbose = TRUE)

I get the following error, which makes no sense to me:

Error in if (any(lengths <= 0)) stop("argument 'lengths' need to be greater than zero") : missing value where TRUE/FALSE needed

All my arguments have length greater than zero. Any help would be greatly appreciated!

> sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.3

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] splines   parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] BSgenome.Mmusculus.UCSC.mm10_1.4.0       BSgenome_1.50.0                         
 [3] rtracklayer_1.42.1                       TxDb.Mmusculus.UCSC.mm10.knownGene_3.4.4
 [5] GenomicFeatures_1.34.3                   org.Mm.eg.db_3.7.0                      
 [7] AnnotationDbi_1.44.0                     EDASeq_2.16.3                           
 [9] ShortRead_1.40.0                         GenomicAlignments_1.18.1                
[11] Rsamtools_1.34.1                         Biostrings_2.50.2                       
[13] XVector_0.22.0                           scales_1.0.0                            
[15] cqn_1.28.1                               quantreg_5.38                           
[17] SparseM_1.77                             preprocessCore_1.44.0                   
[19] nor1mix_1.2-3                            mclust_5.4.2                            
[21] edgeR_3.24.3                             limma_3.38.3                            
[23] DESeq2_1.22.2                            SummarizedExperiment_1.12.0             
[25] DelayedArray_0.8.0                       BiocParallel_1.16.6                     
[27] matrixStats_0.54.0                       Biobase_2.42.0                          
[29] GenomicRanges_1.34.0                     GenomeInfoDb_1.18.2                     
[31] IRanges_2.16.0                           S4Vectors_0.20.1                        
[33] BiocGenerics_0.28.0                      WGCNA_1.66                              
[35] fastcluster_1.1.25                       dynamicTreeCut_1.63-1                   

loaded via a namespace (and not attached):
 [1] colorspace_1.4-0       hwriter_1.3.2          htmlTable_1.13.1       base64enc_0.1-3       
 [5] rstudioapi_0.9.0       MatrixModels_0.4-1     bit64_0.9-7            mvtnorm_1.0-8         
 [9] codetools_0.2-16       R.methodsS3_1.7.1      doParallel_1.0.14      impute_1.56.0         
[13] DESeq_1.34.1           robustbase_0.93-3      geneplotter_1.60.0     knitr_1.21            
[17] Formula_1.2-3          annotate_1.60.0        cluster_2.0.7-1        GO.db_3.7.0           
[21] R.oo_1.22.0            BiocManager_1.30.4     httr_1.4.0             rrcov_1.4-7           
[25] compiler_3.5.2         backports_1.1.3        assertthat_0.2.0       Matrix_1.2-15         
[29] lazyeval_0.2.1         prettyunits_1.0.2      acepack_1.4.1          htmltools_0.3.6       
[33] tools_3.5.2            gtable_0.2.0           glue_1.3.0             GenomeInfoDbData_1.2.0
[37] dplyr_0.8.0.1          Rcpp_1.0.0             iterators_1.0.10       xfun_0.5              
[41] stringr_1.4.0          XML_3.98-1.17          DEoptimR_1.0-8         zlibbioc_1.28.0       
[45] MASS_7.3-51.1          aroma.light_3.12.0     hms_0.4.2              RColorBrewer_1.1-2    
[49] memoise_1.1.0          gridExtra_2.3          ggplot2_3.1.0          biomaRt_2.38.0        
[53] rpart_4.1-13           latticeExtra_0.6-28    stringi_1.3.1          RSQLite_2.1.1         
[57] genefilter_1.64.0      pcaPP_1.9-73           foreach_1.4.4          checkmate_1.9.1       
[61] rlang_0.3.1            pkgconfig_2.0.2        bitops_1.0-6           lattice_0.20-38       
[65] purrr_0.3.0            htmlwidgets_1.3        bit_1.1-14             tidyselect_0.2.5      
[69] robust_0.4-18          plyr_1.8.4             magrittr_1.5           R6_2.4.0              
[73] Hmisc_4.2-0            fit.models_0.5-14      DBI_1.0.0              pillar_1.3.1          
[77] foreign_0.8-71         survival_2.43-3        RCurl_1.95-4.11        nnet_7.3-12           
[81] tibble_2.0.1           crayon_1.3.4           progress_1.2.0         locfit_1.5-9.1        
[85] grid_3.5.2             data.table_1.12.0      blob_1.1.1             digest_0.6.18         
[89] xtable_1.8-3           R.utils_2.8.0          munsell_0.5.0

What is the output of summary(GC_content[,1])

Best, Kasper