DTU analysis with tximport-DRIM-Seq-DEXSeq-stageR workflow starting from StrinTie output
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Entering edit mode
greta • 0
@f3036fa1
Last seen 5 weeks ago
Finland

Hello,

I am performing DTU analysis following "Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification" publication workflow with own data (Link). My data include patient sample data, 21 samples on each 3 groups. I have used StringTie produced .ctab files from each sample as input for the workflow. First rows showed below:

>t_id   chr strand  start   end t_name  num_exons   length  gene_id gene_name   cov FPKM    
1   chr1    +   11874   14409   rna0    3   1652    DDX11L1 DDX11L1 0.286368    0.090211  
2   chr1    -   14362   29370   rna1    11  1769    WASH7P  WASH7P  5.673654`

I used tximport to download the data into R and to create txi object with transcript-to-gene-mapping object. Then I followed the workflow where Tx2gene was used as transcript-to-gene-mapping object. In this modification, t_name correspond to TXNAME and gene_name to GENEID. Tx2gene:

>A tibble: 76,103 × 3
   t_name         gene_name    ntx    
   <chr>          <chr>        <table>
 1 rna0           DDX11L1      1      
 2 rna1           WASH7P       1      
 3 rna3           MIR6859-1    3      
 4 rna2           MIR6859-1    3      
 5 rna4           MIR6859-1    3      
 6 rna5           MIR1302-2    2

Now, my problem is that for some reason DRIMSeq object inform me to have only 1 gene and 63 samples. The used code is below:

#DEXSeq_stringtie.csv file include the path to the files 

DEXSeq_stringtie <- read_csv("DEXSeq_stringtie.csv")

path<-DEXSeq_stringtie$Path

tx2gene <- tmp[, c("t_name", "gene_name")]

txi <- tximport(path, type = "stringtie", tx2gene = tx2gene, txOut=TRUE, countsFromAbundance="scaledTPM")

#This was modified from both workflows

*reading in files with read_tsv
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 *

cts <- txi$counts

cts <- cts[rowSums(cts) > 0,]

#Checking data

 all(rownames(cts) %in% tx2gene$t_name)

[1] TRUE

tx2gene <- tx2gene[match(rownames(cts),tx2gene$t_name),]

 all(rownames(cts) == tx2gene$t_name)

[1] TRUE

#Adding metadata

 samps<-Rmetadata_DRIMSeq

#Variables to factors

samps$group <- factor(samps$group)

samps$patient<- factor(samps$patient)

samps$diabetes<-factor(samps$diabetes)

samps<-as.data.frame(samps)

#DRIMSeq object
colnames(cts)<-samps$sample_id

counts <- data.frame(gene_id=tx2gene$gene_name, feature_id=tx2gene$t_name, cts)

d <- dmDSdata(counts=counts, samples=samps)

d

>An object of class dmDSdata 
**with 1 genes and 63 samples** * data accessors: counts(), samples()

Would you think any reason why the object is giving me only 1 gene with the object? I appreciate your help!

sessionInfo(
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /usr/local/apps/linux-centos7-x86_64/gcc-4.8.5/r/4.0.0-rmu2ifmqdxpwpvbr62zkux2ex77isony/rlib/R/lib/libRblas.so
LAPACK: /usr/local/apps/linux-centos7-x86_64/gcc-4.8.5/r/4.0.0-rmu2ifmqdxpwpvbr62zkux2ex77isony/rlib/R/lib/libRlapack.so

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

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

other attached packages:
 [1] tximport_1.18.0            
 [2] stageR_1.12.0              
 [3] DEXSeq_1.36.0              
 [4] RColorBrewer_1.1-2         
 [5] AnnotationDbi_1.52.0       
 [6] DESeq2_1.30.1              
 [7] SummarizedExperiment_1.20.0
 [8] GenomicRanges_1.42.0       
 [9] GenomeInfoDb_1.26.7        
[10] IRanges_2.24.1             
[11] S4Vectors_0.28.1           
[12] MatrixGenerics_1.2.1       
[13] matrixStats_0.60.0         
[14] Biobase_2.50.0             
[15] BiocGenerics_0.36.1        
[16] BiocParallel_1.24.1        
[17] DRIMSeq_1.18.0             
[18] edgeR_3.32.1               
[19] limma_3.46.0               

loaded via a namespace (and not attached):
 [1] bitops_1.0-7            
 [2] bit64_4.0.5             
 [3] progress_1.2.2          
 [4] httr_1.4.2              
 [5] tools_4.0.0             
 [6] utf8_1.2.2              
 [7] R6_2.5.0                
 [8] DBI_1.1.1               
 [9] colorspace_2.0-2        
[10] tidyselect_1.1.1        
[11] gridExtra_2.3           
[12] prettyunits_1.1.1       
[13] bit_4.0.4               
[14] curl_4.3.2              
[15] compiler_4.0.0          
[16] cli_3.0.1               
[17] xml2_1.3.2              
[18] DelayedArray_0.16.3     
[19] rtracklayer_1.50.0      
[20] scales_1.1.1            
[21] genefilter_1.72.1       
[22] askpass_1.1             
[23] rappdirs_0.3.3          
[24] Rsamtools_2.6.0         
[25] stringr_1.4.0           
[26] DOSE_3.16.0             
[27] XVector_0.30.0          
[28] pkgconfig_2.0.3         
[29] dbplyr_2.1.1            
[30] fastmap_1.1.0           
[31] rlang_0.4.11            
[32] rstudioapi_0.13         
[33] RSQLite_2.2.7           
[34] generics_0.1.0          
[35] hwriter_1.3.2           
[36] GOSemSim_2.16.1         
[37] dplyr_1.0.7             
[38] RCurl_1.98-1.3          
[39] magrittr_2.0.1          
[40] GO.db_3.12.1            
[41] GenomeInfoDbData_1.2.4  
[42] Matrix_1.3-4            
[43] Rcpp_1.0.7              
[44] munsell_0.5.0           
[45] fansi_0.5.0             
[46] lifecycle_1.0.0         
[47] stringi_1.7.3           
[48] yaml_2.2.1              
[49] zlibbioc_1.36.0         
[50] plyr_1.8.6              
[51] qvalue_2.22.0           
[52] BiocFileCache_1.14.0    
[53] grid_4.0.0              
[54] blob_1.2.2              
[55] DO.db_2.9               
[56] crayon_1.4.1            
[57] lattice_0.20-44         
[58] Biostrings_2.58.0       
[59] splines_4.0.0           
[60] GenomicFeatures_1.42.3  
[61] annotate_1.68.0         
[62] hms_1.1.0               
[63] locfit_1.5-9.4          
[64] pillar_1.6.2            
[65] fgsea_1.16.0            
[66] geneplotter_1.68.0      
[67] reshape2_1.4.4          
[68] biomaRt_2.46.3          
[69] fastmatch_1.1-3         
[70] XML_3.99-0.6            
[71] glue_1.4.2              
[72] data.table_1.14.0       
[73] vctrs_0.3.8             
[74] gtable_0.3.0            
[75] openssl_1.4.4           
[76] purrr_0.3.4             
[77] assertthat_0.2.1        
[78] cachem_1.0.5            
[79] ggplot2_3.3.5           
[80] xtable_1.8-4            
[81] survival_3.2-11         
[82] tibble_3.1.3            
[83] GenomicAlignments_1.26.0
[84] memoise_2.0.0           
[85] statmod_1.4.36          
[86] ellipsis_0.3.2)
RNA-seq DRIMSeq DEXSeq rnaseqDTU • 145 views
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Entering edit mode

Ok, I reconstructed my initial input to tximport and I think dmDSdata object is as it should.

Prepping sample for tximport

files <- file.path(path)

names(files) <- samps$sample_id

head(files)

txi <- tximport(files, type = "stringtie", tx2gene = tx2gene, txOut=TRUE, countsFromAbundance="scaledTPM")

..................

d

An object of class dmDSdata with 26895 genes and 63 samples

  • data accessors: counts(), samples()
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Entering edit mode
@mikelove
Last seen 9 hours ago
United States

What do you get for:

head(table(counts$gene_id), 40)

And what about dim(d) at the end?

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0
Entering edit mode

head(table(counts$gene_id), 40)

dim(d)

NULL

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Entering edit mode

To be honest I'm totally confused why dmDSdata is giving you only one gene. Maybe the DRIMSeq authors can weigh in.

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Entering edit mode

Ok, thank you anyway for your time!

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