[BioC] RNAseq expression threshold using DESeq2 normalised counts

[QAMRA Aditi (GIS)]

Sorry I posted the wrong link while referring to the paper using zFPKM transformation. here it is -

http://www.biomedcentral.com/1471-2164/14/778
________________________________________
From: Aditi [guest] [guest at bioconductor.org]
Sent: Saturday, July 19, 2014 11:51 PM
To: bioconductor at r-project.org; QAMRA Aditi (GIS)
Cc: DESeq2 Maintainer
Subject: RNAseq expression threshold using DESeq2 normalised counts

Hi Mike,

This is a question similar to posted on biostars a few months ago (https://www.biostars.org/p/94680/) that you came across.

I want to determine if a gene is expressed or not using RNAseq data. Though there is quite a discussion on it with papers defining range of FPKM values (generally generated using cufflinks ) as a cutoff to say that a gene is expressed.

Can we rather use normalised counts from DESeq2- look at the distribution and determine a suitable cutoff. Better still if one has negative controls like spike ins in the RNA protocol use that a cutoff ? ( I unfortunately dont have spike in control data)

Or do you think one should extract FPKM values and then use maybe a zFPKM transformation (http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000598) like most people are suggesting

I look forward to your opinion and suggestion,

Thanks !
Aditi





 -- output of sessionInfo():

 -- output of sessionInfo():

R version 3.1.0 (2014-04-10)
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] parallel  stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
 [1] DESeq2_1.4.5            RcppArmadillo_0.4.300.0 Rcpp_0.11.1
 [4] EDASeq_1.10.0           aroma.light_2.0.0       matrixStats_0.8.14
 [7] ShortRead_1.22.0        GenomicAlignments_1.0.1 BSgenome_1.32.0
[10] Rsamtools_1.16.0        GenomicRanges_1.16.3    GenomeInfoDb_1.0.2
[13] Biostrings_2.32.0       XVector_0.4.0           IRanges_1.22.7
[16] BiocParallel_0.6.1      Biobase_2.24.0          BiocGenerics_0.10.0

loaded via a namespace (and not attached):
 [1] annotate_1.42.0      AnnotationDbi_1.26.0 BatchJobs_1.2
 [4] BBmisc_1.6           bitops_1.0-6         brew_1.0-6
 [7] codetools_0.2-8      DBI_0.2-7            DESeq_1.16.0
[10] digest_0.6.4         fail_1.2             foreach_1.4.2
[13] genefilter_1.46.1    geneplotter_1.42.0   grid_3.1.0
[16] hwriter_1.3          iterators_1.0.7      lattice_0.20-29
[19] latticeExtra_0.6-26  locfit_1.5-9.1       plyr_1.8.1
[22] RColorBrewer_1.0-5   R.methodsS3_1.6.1    R.oo_1.18.0
[25] RSQLite_0.11.4       sendmailR_1.1-2      splines_3.1.0
[28] stats4_3.1.0         stringr_0.6.2        survival_2.37-7
[31] tools_3.1.0          XML_3.98-1.1         xtable_1.7-3
[34] zlibbioc_1.10.0

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