Good afternoon,

The aim of my analysis is to perform Limma in order to generate p values and fold changes for each protein between alive and dead patients. I have created 3 csv files with information on these proteins:

1) ExpressionMatrix: Contains protein abundance values as rows (1552). And each sample (alive and dead patients) as columns (81) 2) FeatureData: Contains each protein as rows (1552). And one other column (Description: This is a description of each protein) 3) PhenotypeData: Contains each sample as rows (81). And two other columns with the headings : Patient ID and State.

I understand that I need to create a Class to hold these different objects in other to make the Limma analysis more straightforward (I know this from using the Limma Tutorial from DataCamp). I did the boxplot function which uses the 3 different objects I'm using in other to confirm that all 3 objects are in the correct data format as the boxplot produced the expected result. But when I try to create a Class of all 3 objects, I keep getting the error: Error in validObject(.Object) : invalid class “ExpressionSet” object: 1: sampleNames differ between assayData and phenoData invalid class “ExpressionSet” object: 2: sampleNames differ between phenoData and protocolData

I figure the issue is with the way I have structured objects "e" and "p"(ExpressionMatrix and PhenotypeData) but I am not sure how to fix it? Below is the code I have used from start to finish. Thank you for you help in advance!

Shimon

>setwd("D:/sa825/Using Limma")
> x<-read.csv("ExpressionMatrix.csv", stringsAsFactors = FALSE)
> f<-read.csv("FeatureData.csv")
> p<-read.csv("PhenotypeData.csv")
> e<-as.matrix(x)
> typeof(e)
[1] "double"
> class(e)
[1] "matrix"
> boxplot(e[1 , ] ~ p$State, main = f[1 , "Description"])
> source("https://bioconductor.org/biocLite.R")
WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of
Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/User/Documents/R/win-library/3.5’
(as ‘lib’ is unspecified)
trying URL 'https://bioconductor.org/packages/3.7/bioc/bin/windows/contrib/3.5/BiocInstaller_1.30.0.zip'
Content type 'application/zip' length 102191 bytes (99 KB)
downloaded 99 KB

package ‘BiocInstaller’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\User\AppData\Local\Temp\RtmpkLuJuJ\downloaded_packages
Bioconductor version 3.7 (BiocInstaller 1.30.0),
  ?biocLite for help
A newer version of Bioconductor is available for this
  version of R, ?BiocUpgrade for help
> biocLite("Biobase")
BioC_mirror: https://bioconductor.org
Using Bioconductor 3.7 (BiocInstaller 1.30.0), R 3.5.3
  (2019-03-11).
Installing package(s) ‘Biobase’
also installing the dependency ‘BiocGenerics’

trying URL 'https://bioconductor.org/packages/3.7/bioc/bin/windows/contrib/3.5/BiocGenerics_0.26.0.zip'
Content type 'application/zip' length 745077 bytes (727 KB)
downloaded 727 KB

trying URL 'https://bioconductor.org/packages/3.7/bioc/bin/windows/contrib/3.5/Biobase_2.40.0.zip'
Content type 'application/zip' length 2413751 bytes (2.3 MB)
downloaded 2.3 MB

package ‘BiocGenerics’ successfully unpacked and MD5 sums checked
package ‘Biobase’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\User\AppData\Local\Temp\RtmpkLuJuJ\downloaded_packages
installation path not writeable, unable to update
  packages: boot, class, cluster, KernSmooth, lattice,
  MASS, Matrix, mgcv, nlme, nnet, rpart, spatial,
  survival
> library(Biobase)
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, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind,
    colMeans, colnames, colSums, dirname, 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, rowMeans, rownames, rowSums, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit,
    which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages
    'citation("pkgname")'.

> eset <- ExpressionSet(assayData = e,
+                       phenoData = AnnotatedDataFrame(p),
+                       featureData = AnnotatedDataFrame(f))
Error in validObject(.Object) : 
  invalid class “ExpressionSet” object: 1: sampleNames differ between assayData and phenoData
invalid class “ExpressionSet” object: 2: sampleNames differ between phenoData and protocolData

> sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

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

other attached packages:
[1] Biobase_2.40.0      BiocGenerics_0.26.0

loaded via a namespace (and not attached):
[1] compiler_3.5.3 tools_3.5.3 

You can make an ExpressionSet object if you want, but it does not make the limma analysis any more or less straightforward than it otherwise would be. limma can just as easily work with your data.frames x, f, and p directly. The limma User's Guide can be viewed by typing

library(limma)
limmaUsersGuide()

or from the limma landing page: https://www.bioconductor.org/packages/release/bioc/html/limma.html

You are reading the reading the data with read.csv, and that seems to be working, but it is important to look at the data.frames that have been read in to check that the dimensions and content are what you expect them to be. It is apparent from the error messages that there is a mismatch somewhere. What does your data show if you type the following?

dim(f)
head(f)
dim(p)
head(p)
dim(f)
head(f)

Also consider using more up-to-date software, although updating won't make any difference to the error messages you've reported so far. The current version of R is 4.0.0 and the current release of Bioconductor is 3.11. Your software is a few Bioc releases out of date.