DESeq2: error while ploting figure
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@f68bdf4c
Last seen 3 months ago
Norway

I am trying to plot PCA, but getting error:

>dds <- DESeqDataSetFromMatrix(countData=rawCounts_total, 
                              colData=mapping_file, 
                              design=~Sample_Type, tidy = TRUE)

>dds <- DESeq(dds, parallel = T)
>rld <- vst(dds, fitType='mean', blind=TRUE)
>DESeq2::plotPCA(rld, intgroup=c("Sample_Type"))
Error in .local(object, ...) : unused argument (intgroup = "Sample_Type")


> plotPCA(rld, intgroup=colnames(colData(rld))[4])
Error in .local(object, ...) : unused argument (intgroup = "Sample_Type")

Any suggestions?

R DESeq2 • 725 views
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@mikelove
Last seen 4 days ago
United States

Try running this code with no extra packages loaded (e.g. including ones that may be auto-loading from customized Rprofile...), and just using the code above. Also report your sessionInfo and any other messages that may give a clue as to what may be happening.

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If I work on a new R, then above commands worked, but I still donĀ“t understand, why it was not working on my previous Rprofile. Here is the sessionInfo:

> sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 11.5.2

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] BiocParallel_1.26.2         DESeq2_1.32.0               SummarizedExperiment_1.22.0 Biobase_2.52.0             
 [5] MatrixGenerics_1.4.3        matrixStats_0.61.0          GenomicRanges_1.44.0        GenomeInfoDb_1.28.4        
 [9] IRanges_2.26.0              S4Vectors_0.30.2            BiocGenerics_0.38.0        

loaded via a namespace (and not attached):
 [1] bitops_1.0-7                bit64_4.0.5                 RColorBrewer_1.1-2          httr_1.4.2                 
 [5] syntactic_0.5.0             tools_4.1.1                 utf8_1.2.2                  R6_2.5.1                   
 [9] AcidGenerics_0.5.20         DBI_1.1.1                   colorspace_2.0-2            withr_2.4.2                
[13] tidyselect_1.1.1            processx_3.5.2              bit_4.0.4                   compiler_4.1.1             
[17] AcidExperiment_0.2.2        AcidSingleCell_0.1.9        basejump_0.14.23            cli_3.0.1                  
[21] DelayedArray_0.18.0         scales_1.1.1                genefilter_1.74.1           stringr_1.4.0              
[25] AcidMarkdown_0.1.4          XVector_0.32.0              pkgconfig_2.0.3             sessioninfo_1.1.1          
[29] AcidPlyr_0.1.22             fastmap_1.1.0               DESeqAnalysis_0.4.4         rlang_0.4.12               
[33] AcidPlots_0.3.9             rstudioapi_0.13             RSQLite_2.2.8               generics_0.1.0             
[37] dplyr_1.0.7                 RCurl_1.98-1.5              magrittr_2.0.1              GenomeInfoDbData_1.2.6     
[41] Matrix_1.3-4                Rcpp_1.0.7                  munsell_0.5.0               fansi_0.5.0                
[45] lifecycle_1.0.1             stringi_1.7.5               MASS_7.3-54                 zlibbioc_1.38.0            
[49] goalie_0.5.5                grid_4.1.1                  blob_1.2.2                  crayon_1.4.1               
[53] AcidCLI_0.1.7               lattice_0.20-45             Biostrings_2.60.2           cowplot_1.1.1              
[57] splines_4.1.1               annotate_1.70.0             KEGGREST_1.32.0             locfit_1.5-9.4             
[61] pipette_0.7.2               knitr_1.36                  ps_1.6.0                    pillar_1.6.4               
[65] AcidGenomes_0.2.19          geneplotter_1.70.0          AcidBase_0.4.5              XML_3.99-0.8               
[69] glue_1.4.2                  data.table_1.14.2           png_0.1-7                   vctrs_0.3.8                
[73] gtable_0.3.0                purrr_0.3.4                 assertthat_0.2.1            cachem_1.0.6               
[77] ggplot2_3.3.5               xfun_0.27                   xtable_1.8-4                survival_3.2-13            
[81] SingleCellExperiment_1.14.1 tibble_3.1.5                glmpca_0.2.0                AnnotationDbi_1.54.1       
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It sounds like another package may have done something... not sure though.

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For PCA plot, can we do ANOVA on a group, let's say, if we find any cluster, but want to check whether it's significant or not?

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We don't have any tests for differences in the reduced dimensional space after using the data also to perform clustering. Single cell methods may help you here. Daniella Witten has worked on this recently

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