Hello All Recently i got clariom D samples As of now i am using TAC (Affymetrix tool)and bioconductor package and comparing results of both My bioconductor code is :
setwd("Path to my cel files")
library(oligo)
library(affycoretools)
library(limma)
library(clariomdhumantranscriptcluster.db)
library(pd.clariom.d.human)
list.celfiles()
T1.cel
T2.cel
T3.cel
C1.cel
C2.cel
C3.cel
dat <- read.celfiles(list.celfiles())
probeset.eset=rma(dat,target="core")
probeset.eset <- annotateEset(probeset.eset,annotation(probeset.eset))
strain <- c("Test","Test","Test","Control","Control","Control")
x <- model.matrix(~factor(strain))
design
colnames(x) <- c("Test vs Control","Control")
>x
Test vs Control Control
1 1 1
2 1 1
3 1 1
4 1 0
5 1 0
6 1 0
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$`factor(strain)`
"contr.treatment"**
fit3 <- lmFit(probeset.eset,design = x)
fit3<- eBayes(fit3)
options(digits=2)
r6<-topTable(fit = fit3,coef=2, n=10000, adjust="BY")
write.csv(r5,"C://Users/Clariom-D/Clariom_7_17/reult.csv")
When i compare DGE expressing of gene say x from limma pipeline i am getting value -1.91 and from TAC software i am getting value -13
Why there is so much difference between the two? Which pipeline should i consider to be better?
Is there any mistake i am doing if so what that mistake is and how to rectify?
sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] pd.clariom.d.human_3.14.1 DBI_0.7
[3] RSQLite_2.0 clariomdhumantranscriptcluster.db_8.5.0
[5] org.Hs.eg.db_3.4.0 AnnotationDbi_1.36.2
[7] limma_3.30.13 affycoretools_1.46.5
[9] oligo_1.38.0 Biostrings_2.42.1
[11] XVector_0.14.1 IRanges_2.8.2
[13] S4Vectors_0.12.2 Biobase_2.34.0
[15] oligoClasses_1.36.0 BiocGenerics_0.20.0
loaded via a namespace (and not attached):
[1] colorspace_1.3-2 hwriter_1.3.2
[3] biovizBase_1.22.0 htmlTable_1.9
[5] GenomicRanges_1.26.4 base64enc_0.1-3
[7] dichromat_2.0-0 affyio_1.44.0
[9] bit64_0.9-7 interactiveDisplayBase_1.12.0
[11] codetools_0.2-15 splines_3.3.3
[13] R.methodsS3_1.7.1 ggbio_1.22.4
[15] geneplotter_1.52.0 knitr_1.16
[17] Formula_1.2-2 Rsamtools_1.26.2
[19] annotate_1.52.1 cluster_2.0.5
[21] GO.db_3.4.0 R.oo_1.21.0
[23] graph_1.52.0 shiny_1.0.3
[25] httr_1.2.1 GOstats_2.40.0
[27] backports_1.1.0 Matrix_1.2-8
[29] lazyeval_0.2.0 acepack_1.4.1
[31] htmltools_0.3.6 tools_3.3.3
[33] affy_1.52.0 gtable_0.2.0
[35] Category_2.40.0 reshape2_1.4.2
[37] affxparser_1.46.0 Rcpp_0.12.12
[39] gdata_2.18.0 preprocessCore_1.36.0
[41] rtracklayer_1.34.2 iterators_1.0.8
[43] stringr_1.2.0 mime_0.5
[45] ensembldb_1.6.2 gtools_3.5.0
[47] XML_3.98-1.9 AnnotationHub_2.6.5
[49] edgeR_3.16.5 zlibbioc_1.20.0
[51] scales_0.4.1 BSgenome_1.42.0
[53] VariantAnnotation_1.20.3 BiocInstaller_1.24.0
[55] SummarizedExperiment_1.4.0 RBGL_1.50.0
[57] RColorBrewer_1.1-2 yaml_2.1.14
[59] memoise_1.1.0 gridExtra_2.2.1
[61] ggplot2_2.2.1 biomaRt_2.30.0
[63] rpart_4.1-10 gcrma_2.46.0
[65] reshape_0.8.6 latticeExtra_0.6-28
[67] stringi_1.1.5 genefilter_1.56.0
[69] foreach_1.4.3 checkmate_1.8.3
[71] caTools_1.17.1 GenomicFeatures_1.26.4
[73] BiocParallel_1.8.2 GenomeInfoDb_1.10.3
[75] ReportingTools_2.14.0 rlang_0.1.1
[77] pkgconfig_2.0.1 bitops_1.0-6
[79] lattice_0.20-34 GenomicAlignments_1.10.1
[81] htmlwidgets_0.9 bit_1.1-12
[83] GSEABase_1.36.0 AnnotationForge_1.16.1
[85] GGally_1.3.1 plyr_1.8.4
[87] magrittr_1.5 DESeq2_1.14.1
[89] R6_2.2.2 gplots_3.0.1
[91] Hmisc_4.0-3 foreign_0.8-67
[93] survival_2.41-3 RCurl_1.95-4.8
[95] nnet_7.3-12 tibble_1.3.3
[97] KernSmooth_2.23-15 OrganismDbi_1.16.0
[99] PFAM.db_3.4.0 locfit_1.5-9.1
[101] grid_3.3.3 data.table_1.10.4
[103] blob_1.1.0 digest_0.6.12
[105] xtable_1.8-2 ff_2.2-13
[107] httpuv_1.3.5 R.utils_2.5.0
[109] munsell_0.4.3