Hi everyone!

I'm trying to apply pathway analysis to Array Express experiments. For that I downloaded the processed data, along with the sample and data relationship files (e.g. https://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-15641/). I made every value an integer (I know this isn't how the proper analysis is made, but this is just a fast way to corraborate suspicions on something) and prepared bouth countData and colData files (both .csv).

colData:

countData:

Now in R Studio I run the following commands:
library(tidyverse)
files <- list.files()
metadata <-  read_csv(files[1])
counts <- read_csv(files[2])

library(DESeq2)
mycounts <- as.data.frame(counts)
metadata <- as.data.frame(metadata)

dds <- DESeqDataSetFromMatrix(countData=mycounts,colData=metadata,design=~type,tidy=TRUE)
dds <- DESeq(dds)
res <- results(dds, tidy=TRUE)

res <- tbl_df(res)

res %>% 

filter(padj<0.05) %>% 

write_csv("DGE.csv")

DGE.csv:

So now I want to run pathway analysis according to this guide: http://www.gettinggeneticsdone.com/2015/12/tutorial-rna-seq-differential.html

The experiment was made with Affymetrix Gene Chip HGU133A, so I:

library("AnnotationDbi") 
library("hgu133a.db")

And when I'm trying to merge annotation data to my matrix with:

res$entrez = mapIds(hgu133a.db, keys=row.names(res), column="ENTREZID", keytype="PROBEID", multiVals="first")

Error in .testForValidKeys(x, keys, keytype, fks) : 
  None of the keys entered are valid keys for 'PROBEID'. Please use the keys method to see a listing of valid arguments.

If I just type: res

# A tibble: 22,283 x 7
   row          baseMean log2FoldChange  lfcSE     stat   pvalue     padj
   <chr>           <dbl>          <dbl>  <dbl>    <dbl>    <dbl>    <dbl>
 1 AFFX-TrpnX-…     25.7        0.134   0.0882   1.52   1.28e- 1 1.84e- 1

> sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.1 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_AR.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=es_AR.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=es_AR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_AR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] hugene10sttranscriptcluster.db_8.7.0
 [2] annotate_1.56.2                     
 [3] XML_3.98-1.16                       
 [4] hgu133a.db_3.2.3                    
 [5] hgu95av2.db_3.2.3                   
 [6] org.Hs.eg.db_3.5.0                  
 [7] AnnotationDbi_1.40.0                
 [8] BiocInstaller_1.28.0                
 [9] DESeq2_1.18.1                       
[10] SummarizedExperiment_1.8.1          
[11] DelayedArray_0.4.1                  
[12] matrixStats_0.54.0                  
[13] Biobase_2.38.0                      
[14] GenomicRanges_1.30.3                
[15] GenomeInfoDb_1.14.0                 
[16] IRanges_2.12.0                      
[17] S4Vectors_0.16.0                    
[18] BiocGenerics_0.24.0                 
[19] forcats_0.3.0                       
[20] stringr_1.3.1                       
[21] dplyr_0.7.6                         
[22] purrr_0.2.5                         
[23] readr_1.1.1                         
[24] tidyr_0.8.1                         
[25] tibble_1.4.2                        
[26] ggplot2_3.0.0                       
[27] tidyverse_1.2.1                     

loaded via a namespace (and not attached):
 [1] nlme_3.1-131           bitops_1.0-6           lubridate_1.7.4       
 [4] bit64_0.9-7            RColorBrewer_1.1-2     httr_1.3.1            
 [7] tools_3.4.4            backports_1.1.2        utf8_1.1.4            
[10] R6_2.2.2               rpart_4.1-13           Hmisc_4.1-1           
[13] DBI_1.0.0              lazyeval_0.2.1         colorspace_1.3-2      
[16] nnet_7.3-12            withr_2.1.2            tidyselect_0.2.4      
[19] gridExtra_2.3          bit_1.1-14             compiler_3.4.4        
[22] cli_1.0.1              rvest_0.3.2            htmlTable_1.12        
[25] xml2_1.2.0             scales_1.0.0           checkmate_1.8.5       
[28] genefilter_1.60.0      digest_0.6.17          foreign_0.8-69        
[31] XVector_0.18.0         base64enc_0.1-3        pkgconfig_2.0.2       
[34] htmltools_0.3.6        htmlwidgets_1.3        rlang_0.2.2           
[37] readxl_1.1.0           rstudioapi_0.7         RSQLite_2.1.1         
[40] bindr_0.1.1            jsonlite_1.5           BiocParallel_1.12.0   
[43] acepack_1.4.1          RCurl_1.95-4.11        magrittr_1.5          
[46] GenomeInfoDbData_1.0.0 Formula_1.2-3          Matrix_1.2-14         
[49] fansi_0.3.0            Rcpp_0.12.19           munsell_0.5.0         
[52] stringi_1.2.4          yaml_2.2.0             zlibbioc_1.24.0       
[55] plyr_1.8.4             blob_1.1.1             grid_3.4.4            
[58] crayon_1.3.4           lattice_0.20-35        haven_1.1.2           
[61] splines_3.4.4          hms_0.4.2              locfit_1.5-9.1        
[64] knitr_1.20             pillar_1.3.0           geneplotter_1.56.0    
[67] glue_1.3.0             latticeExtra_0.6-28    data.table_1.11.8     
[70] modelr_0.1.2           cellranger_1.1.0       gtable_0.2.0          
[73] assertthat_0.2.0       xtable_1.8-3           broom_0.5.0           
[76] survival_2.41-3        memoise_1.1.0          bindrcpp_0.2.2        
[79] cluster_2.0.7-1  

I've been trying lots of guides and I can't get past through the annotation. When I type:

select(hgu133a.db, c("74694_s_at"), c("SYMBOL","ENTREZID", "GENENAME"))
'select()' returned 1:1 mapping between keys and columns
     PROBEID SYMBOL ENTREZID
1 74694_s_at RABEP2    79874
                                         GENENAME
1 rabaptin, RAB GTPase binding effector protein 2

I'm new to R, so I'm betting this has something to do with a very basic deal, hope you don't mind telling me.

Thank you for your time!

Maxi

First off, DESeq2 is not the right package to use for Affymetrix arrays! Did you get that idea somewhere? It's a bad idea, so you should stop doing that and use limma.

Secondly, you are using an old version of R/Bioconductor so you should update.

Third, if I am not mistaken, using row.names on a tibble returns the row names rather than a column called 'row'. I could be wrong, as I avoid as much of the tidyverse as possible; my go-to move when confronted with a tibble is to wrap it in as.data.frame, so I can interact with it like a civilized person. But to my jaundiced eye, it looks like the row names for that tibble are just 1:22283.

Anyway, if you get an error from mapIds that says the keys you are using aren't valid keys, that means that the keys you are using aren't the things you think they are. Your next move should be to look at the keys and try to figure out what the problem is. My guess is you are extracting 1:22282, which are not Affy IDs, and which is causing the error.