how to use log2 transformation and limma
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Entering edit mode
nia ▴ 30
@nia-12707
Last seen 4.1 years ago

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

 

RMA normalization • 3.0k views
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Entering edit mode
@gordon-smyth
Last seen 9 hours ago
WEHI, Melbourne, Australia

You give limma the normalized data, for example:

fit <- lmfit(rma, design)

and so on.

Have a look at the case study in Section 17.2 of the limma User's Guide for an example analysis.

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0
Entering edit mode

thankyou for your response i considered that tutorial nd i got an error of RG not found,

kindly consider other queries also.

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0
Entering edit mode

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'.

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