tx2gene object trouble when working with kallisto and A.thaliana
1
0
Entering edit mode
@864d48ac
Last seen 6 days ago
Russia

Hello everyone! I'm new in RNAseq data analysis and trying to apply a pipeline from an online course to A.thaliana data in my university course project. The pipeline includes kallisto quantification with subsequent analysis in R. I performed kallisto quantification with A.thaliana transcriptome, which I downloaded from plants.ensembl.org, got all the esential files. Firstly, I imported studydesign and set paths to abundance.tsv files:

targets <- read_tsv("ATstudydesign.txt")
path <- file.path(targets$Sample[1:12], "abundance.tsv")

Now, the main trouble is how to import data from abundance.tsv files to R project and to replace transcript names with gene names.

I need a tx2gene object for tximport() function and, as I understand, there are several ways to create it. I tried to create it by downloading a table from https://plants.ensembl.org/biomart/martview/18f83aa36cd9df8402183dba5ee6d603 Ensembl Plants Biomart

Then I imported it into R project:

Tx <- read_tsv("mart_export.txt")
head(Tx)

The Tx file looks good since it has transcripts names in the first column and gene names in the second:

> head(Tx)
# A tibble: 6 × 2
  `Transcript stable ID` `Gene name`
  <chr>                  <chr>      
1 AT3G11415.1            AT3G11415  
2 AT1G31258.1            AT1G31258  
3 AT5G24735.1            AT5G24735  
4 AT2G45780.1            AT2G45780  
5 AT2G42425.1            AT2G42425  
6 AT4G01533.1            AT4G01533

I ran tximport() function but got an error:

> Txi_gene <- tximport(path, 
+                      type = "kallisto", 
+                      tx2gene = Tx, 
+                      txOut = FALSE,
+                      countsFromAbundance = "lengthScaledTPM",
+                      ignoreTxVersion = TRUE)
Note: importing `abundance.h5` is typically faster than `abundance.tsv`
reading in files with read_tsv
1 2 3 4 5 6 7 8 9 10 11 12 
Error in .local(object, ...) : 
  None of the transcripts in the quantification files are present
  in the first column of tx2gene. Check to see that you are using
  the same annotation for both.

Example IDs (file): [AT1G76820, ATMG00060, AT4G16360, ...]

Example IDs (tx2gene): [AT3G11415.1, AT1G31258.1, AT5G24735.1, ...]

  This can sometimes (not always) be fixed using 'ignoreTxVersion' or 'ignoreAfterBar'.

I tried to look at abundance.tsv file by importing the data from it:

Sample <- read_tsv("./RootCtrl1/abundance.tsv")
> head(Sample)
# A tibble: 6 × 5
  target_id   length eff_length est_counts    tpm
  <chr>        <dbl>      <dbl>      <dbl>  <dbl>
1 AT1G76820.1   3857      3645.       21.8  0.934
2 ATMG00060.1    542       333.        2    0.939
3 AT4G16360.1   1685      1473.      465   49.3  
4 AT5G26800.1    637       426.      166   60.9  
5 AT4G16110.1   2666      2454.      293   18.7  
6 AT5G39100.1    808       597.        0    0

I also tried to search several transcript names in abundance.tsv and mart_export.txt and I was able to find them in both files through simple Find in Sublime text editor.

Can't understand what's wrong with these commands and/or files. Maybe someone will share thoughts or suuggestions? Maybe there are alternative ways to create this tx2gene objects? Thanks in advance!

sessionInfo( )

> sessionInfo()
R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=Russian_Russia.utf8  LC_CTYPE=Russian_Russia.utf8    LC_MONETARY=Russian_Russia.utf8
[4] LC_NUMERIC=C                    LC_TIME=Russian_Russia.utf8    

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

other attached packages:
 [1] tximport_1.26.0 forcats_0.5.2   stringr_1.4.1   dplyr_1.0.10    purrr_0.3.5     readr_2.1.3     tidyr_1.2.1    
 [8] tibble_3.1.8    ggplot2_3.4.0   tidyverse_1.3.2

loaded via a namespace (and not attached):
  [1] bitops_1.0-7                matrixStats_0.62.0          fs_1.5.2                    lubridate_1.9.0            
  [5] bit64_4.0.5                 filelock_1.0.2              progress_1.2.2              httr_1.4.4                 
  [9] GenomeInfoDb_1.32.2         tools_4.2.2                 backports_1.4.1             utf8_1.2.2                 
 [13] R6_2.5.1                    DBI_1.1.3                   BiocGenerics_0.42.0         colorspace_2.0-3           
 [17] rhdf5filters_1.8.0          withr_2.5.0                 tidyselect_1.2.0            prettyunits_1.1.1          
 [21] bit_4.0.4                   curl_4.3.3                  compiler_4.2.2              rvest_1.0.3                
 [25] cli_3.3.0                   Biobase_2.56.0              xml2_1.3.3                  DelayedArray_0.22.0        
 [29] rtracklayer_1.56.1          scales_1.2.1                rappdirs_0.3.3              digest_0.6.30              
 [33] Rsamtools_2.12.0            XVector_0.36.0              pkgconfig_2.0.3             MatrixGenerics_1.8.1       
 [37] dbplyr_2.2.1                fastmap_1.1.0               readxl_1.4.1                rlang_1.0.6                
 [41] rstudioapi_0.14             RSQLite_2.2.15              BiocIO_1.6.0                generics_0.1.3             
 [45] jsonlite_1.8.3              vroom_1.6.0                 BiocParallel_1.30.3         googlesheets4_1.0.1        
 [49] RCurl_1.98-1.8              magrittr_2.0.3              GenomeInfoDbData_1.2.8      Matrix_1.5-1               
 [53] Rhdf5lib_1.18.2             Rcpp_1.0.9                  munsell_0.5.0               S4Vectors_0.34.0           
 [57] fansi_1.0.3                 lifecycle_1.0.3             stringi_1.7.8               yaml_2.3.6                 
 [61] SummarizedExperiment_1.26.1 zlibbioc_1.42.0             rhdf5_2.40.0                BiocFileCache_2.4.0        
 [65] grid_4.2.2                  blob_1.2.3                  parallel_4.2.2              crayon_1.5.2               
 [69] lattice_0.20-45             Biostrings_2.64.0           haven_2.5.1                 GenomicFeatures_1.48.3     
 [73] hms_1.1.2                   KEGGREST_1.36.3             pillar_1.8.1                GenomicRanges_1.48.0       
 [77] rjson_0.2.21                codetools_0.2-18            biomaRt_2.52.0              stats4_4.2.2               
 [81] reprex_2.0.2                XML_3.99-0.10               glue_1.6.2                  modelr_0.1.10              
 [85] tzdb_0.3.0                  png_0.1-7                   vctrs_0.5.0                 cellranger_1.1.0           
 [89] gtable_0.3.1                assertthat_0.2.1            cachem_1.0.6                broom_1.0.1                
 [93] restfulr_0.0.15             googledrive_2.0.0           gargle_1.2.1                GenomicAlignments_1.32.1   
 [97] AnnotationDbi_1.58.0        memoise_2.0.1               IRanges_2.30.0              timechange_0.1.1           
[101] ellipsis_0.3.2
tx2gene tximport Arabidopsis_thaliana kallisto • 122 views
ADD COMMENT
2
Entering edit mode
@james-w-macdonald-5106
Last seen 36 minutes ago
United States

One of the things you have to learn when starting out with R (and any software for that matter) is that the error messages mean something. Here is the error message you got.

Error in .local(object, ...) : 
  None of the transcripts in the quantification files are present
  in the first column of tx2gene. Check to see that you are using
  the same annotation for both.

Example IDs (file): [AT1G76820, ATMG00060, AT4G16360, ...]

Example IDs (tx2gene): [AT3G11415.1, AT1G31258.1, AT5G24735.1, ...]

  This can sometimes (not always) be fixed using 'ignoreTxVersion' or 'ignoreAfterBar'.

Which clearly shows you what IDs you are using, and the IDs that are expected, and even recommends the solution! Upon reading that message, you can then do ?tximport, which will provide this, in the arguments section

ignoreTxVersion: logical, whether to split the tx id on the '.'
          character to remove version information to facilitate
          matching with the tx id in 'tx2gene' (default FALSE)
1
Entering edit mode

Thank you for your answer! Of course I read the error message before posting and tried to switch ignoreTxVersion back and forth. It seems to me that when TRUE it ignores versions of transcripts only in abundance.tsv files, not in tx2gene. I tried another way to create tx2gene table with GenomicFeatures package:

txdb <- makeTxDbFromBiomart(biomart = "plants_mart", host="https://plants.ensembl.org", dataset = "athaliana_eg_gene") Then I got the same error and deleted versions from transcripts. The final version is

txdb <- makeTxDbFromBiomart(biomart = "plants_mart", host="https://plants.ensembl.org", dataset = "athaliana_eg_gene")
k <- keys(txdb, keytype="TXNAME")
tx_map <- select(txdb, keys = k, columns="GENEID", keytype = "TXNAME")
tx2gene <- tx_map
tx2gene <- separate(tx2gene, col = TXNAME, into = c("TXNAME", "version"), remove = FALSE)
head(tx2gene)
tx2gene <- dplyr::select(tx2gene, "TXNAME", "GENEID")

Txi_gene <- tximport(path, 
                     type = "kallisto", 
                     tx2gene = tx2gene, 
                     txOut = FALSE,
                     countsFromAbundance = "lengthScaledTPM",
                     ignoreTxVersion = TRUE)

I used advices from https://sbc.shef.ac.uk/workshops/2020-02-13-rnaseq-r/rna-seq-preprocessing.nb.html

I think the code can be optimised but it works:)

ADD REPLY
1
Entering edit mode

Ah, yes. The exact opposite issue for which ignoreTxVersion is intended.

Your solution obviously works, but as you note could be optimized. The tidyverse IMO mostly re-invents existing wheels while requiring more typing to do so. You can do the exact same thing with just three lines of code, which is optimized in terms of typing, and likely in terms of speed as well.

> txdb <- makeTxDbFromBiomart(biomart = "plants_mart", host="https://plants.ensembl.org", dataset = "athaliana_eg_gene")
> tx2gene <- select(txdb, keys(txdb, "TXNAME"), "GENEID","TXNAME")
'select()' returned 1:1 mapping between keys and columns
> head(tx2gene)
       TXNAME    GENEID
1 AT1G01010.1 AT1G01010
2 AT1G03987.1 AT1G03987
3 AT1G01040.1 AT1G01040
4 AT1G01040.2 AT1G01040
5   at1g01046 AT1G01046
6 AT1G03997.1 AT1G03997
> tx2gene$TXNAME <- gsub("\\.[0-9]+$", "", tx2gene$TXNAME)
> head(tx2gene)
     TXNAME    GENEID
1 AT1G01010 AT1G01010
2 AT1G03987 AT1G03987
3 AT1G01040 AT1G01040
4 AT1G01040 AT1G01040
5 at1g01046 AT1G01046
6 AT1G03997 AT1G03997

Login before adding your answer.

Traffic: 564 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6