Firstly i downloaded data from GEO then i compress and unzip it (for eg:GSE14325) i have 9 GSE ids fro my work.
Data preprocessing and Identification of DEGs :(i am following this step)
The raw expression datasets were downloaded and preprocessed by log2 transformation in R language . The Linear Models ``limma'' package in R language wasused to analyze the microarray datasets . Differentially expressed genes wereidentified in patients with RA compared to healthy individuals. The false discovery rate(FDR) was utilized for multiple testing corrections by using theBenjamini and Hochberg method. FDR < 0.05 was set as the threshold of DEGs.
log2 transformation(R result)(GSE14325=data)
> cels = list.files("data/", pattern = "cel")
> library(affy)
Loading required package: BiocGenerics Loading required package: parallel
Attaching package: ‘BiocGenerics’ The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, cbind, colnames, do.call,
duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,
is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
Reduce, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit, which, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
> library(hgu133a.db)
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
The following objects are masked from ‘package:base’:
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: org.Hs.eg.db
> library(hgu133acdf)
Warning messages:
1: replacing previous import ‘AnnotationDbi::tail’ by ‘utils::tail’ when loading ‘hgu133acdf’
2: replacing previous import ‘AnnotationDbi::head’ by ‘utils::head’ when loading ‘hgu133acdf’
> raw.data = ReadAffy(verbose = FALSE, filenames = cels, cdfname = "hgu133acdf")
> data.rma.norm = rma(raw.data)
Background correcting
Normalizing
Calculating Expression
> rma = exprs(data.rma.norm)
> rma[1:5, 1:5]
GSM338681.CEL GSM338691.CEL GSM338737.CEL GSM338781.CEL GSM338795.CEL
1007_s_at 4.339073 3.958062 4.109060 4.409588 5.450845
1053_at 2.861533 2.903839 2.896392 2.882178 2.845537
117_at 2.306659 3.511815 2.233903 2.413740 2.808514
121_at 6.042489 6.029588 5.901305 6.155920 6.102781
1255_g_at 1.997067 2.140122 1.981548 2.046443 2.035436
> write.table(rma, file = "rma.txt", quote = FALSE, sep = "\t")
OUTPUT of log2 transformation:(showing just top 3 lines of output )
GSM338681.CEL GSM338691.CEL GSM338737.CEL GSM338781.CEL GSM338795.CEL
1007_s_at 4.33907299500177 3.95806192721582 4.10905978028352
1053_at 2.86153271830982 2.90383946273172 2.89639190676943
LIMMA
i have searched tutorials for limma but the basic confusion i have which file i have to used as input either cel file which was retrieved from geo db by downloading and compressing GSE files or the file which was retrieved from log2 trans. and after performing limma how to use FDR for * testing corrections.
while using LIMMA this error is generated
> names(RG) Error: object 'RG' not found
thankyou for your response i considered that tutorial nd i got an error of RG not found,
kindly consider other queries also.
Kindly follow my answer and read Section 17.2. There is no mention of "RG" in that section.
If you read the limma User's Guide a little more carefully, you will see that RG objects are only used for two color microarrays.
BTW, when I wrote my original answer, you hadn't mentioned anything about trying to access 'RG'.