samples did not decently separate via pcaPlot
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jiakang • 0
Last seen 1 day ago
United States

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Code should be placed in three backticks as shown below

# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session 

Hi, I am performing differential expression of pathway using deseq2. However, the pca plots were not decent. I want to find DEGs between Resistant and Susceptible, can I go further? 
Here is my code,

    design_T1_HF12 <- design[grep("T1", design$Treatment),] %>%
        .[-which(.$Treatment %in% c("HG64T1")),]
    pathway_T1_HF12 <- pathway[, rownames(design_T1_HF12)]
    pathway_T1_HF12_dds <- DESeqDataSetFromMatrix(pathway_T1_HF12, design_T1_HF12, design = ~ Treatment)
    keep <- rowSums(counts(pathway_T1_HF12_dds) >= 10) >= 3
    pathway_T1_HF12_dds <- pathway_T1_HF12_dds[keep, ]
    vs_pathway_T1_HF12_dds <- rlog(pathway_T1_HF12_dds, blind = FALSE)
    plotPCA(vs_pathway_T1_HF12_dds, intgroup = "Property") # + geom_label(aes(label = name))

Any suggestions would be appreciated. Thanks.
sessionInfo( )
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

[1] LC_COLLATE=Chinese (Simplified)_China.936  LC_CTYPE=Chinese (Simplified)_China.936   
[3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C                              
[5] LC_TIME=Chinese (Simplified)_China.936    

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

other attached packages:
 [1] DESeq2_1.28.1               SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
 [4] matrixStats_0.58.0          Biobase_2.48.0              GenomicRanges_1.40.0       
 [7] GenomeInfoDb_1.24.2         IRanges_2.22.2              S4Vectors_0.26.1           
[10] BiocGenerics_0.34.0         RColorBrewer_1.1-2          ComplexHeatmap_2.6.2       
[13] tibble_3.0.6                magrittr_2.0.1              ggplot2_3.3.3              
[16] MVN_5.8                     reshape2_1.4.4              dplyr_1.0.7                
[19] treemap_2.4-2               devtools_2.3.2              usethis_2.0.0              

loaded via a namespace (and not attached):
  [1] utf8_1.2.1             ks_1.13.1              tidyselect_1.1.1       RSQLite_2.2.3         
  [5] AnnotationDbi_1.50.3   BiocParallel_1.22.0    ranger_0.12.1          rainbow_3.6           
  [9] munsell_0.5.0          sROC_0.1-2             withr_2.4.2            colorspace_2.0-0      
 [13] energy_1.7-8           knitr_1.33             rstudioapi_0.13        robustbase_0.93-8     
 [17] vcd_1.4-8              VIM_6.1.0              labeling_0.4.2         GenomeInfoDbData_1.2.3
 [21] cvTools_0.3.2          mnormt_2.0.2           bit64_4.0.5            farver_2.1.0          
 [25] rprojroot_2.0.2        vctrs_0.3.8            generics_0.1.0         xfun_0.23             
 [29] diptest_0.76-0         R6_2.5.0               robCompositions_2.3.0  clue_0.3-58           
 [33] locfit_1.5-9.4         mvoutlier_2.0.9        flexmix_2.3-17         bitops_1.0-6          
 [37] cachem_1.0.1           reshape_0.8.8          assertthat_0.2.1       promises_1.2.0.1      
 [41] scales_1.1.1           nnet_7.3-14            gtable_0.3.0           Cairo_1.5-12.2        
 [45] processx_3.5.2         rlang_0.4.10           genefilter_1.70.0      systemfonts_1.0.2     
 [49] GlobalOptions_0.1.2    splines_4.0.3          abind_1.4-5            httpuv_1.6.1          
 [53] tools_4.0.3            psych_2.1.3            zCompositions_1.3.4    gridBase_0.4-7        
 [57] ellipsis_0.3.2         kableExtra_1.3.4       proxy_0.4-26           sessioninfo_1.1.1     
 [61] Rcpp_1.0.6             plyr_1.8.6             zlibbioc_1.34.0        purrr_0.3.4           
 [65] RCurl_1.98-1.2         ps_1.5.0               prettyunits_1.1.1      GetoptLong_1.0.5      
 [69] zoo_1.8-9              haven_2.4.1            cluster_2.1.0          fs_1.5.0              
 [73] fda_5.1.9              tinytex_0.32           magick_2.7.1           data.table_1.14.0     
 [77] hdrcde_3.4             openxlsx_4.2.3         circlize_0.4.13        lmtest_0.9-38         
 [81] truncnorm_1.0-8        tmvnsim_1.0-2          mvtnorm_1.1-2          pkgload_1.1.0         
 [85] gsl_2.1-6              hms_1.1.0              mime_0.10              evaluate_0.14         
 [89] xtable_1.8-4           XML_3.99-0.5           rio_0.5.26             mclust_5.4.7          
 [93] readxl_1.3.1           shape_1.4.6            testthat_3.0.1         compiler_4.0.3        
 [97] KernSmooth_2.23-17     crayon_1.4.1           fds_1.8                htmltools_0.5.1.1     
[101] mgcv_1.8-33            pcaPP_1.9-74           later_1.2.0            geneplotter_1.66.0    
[105] tidyr_1.1.3            rrcov_1.5-5            DBI_1.1.1              MASS_7.3-53           
[109] fpc_2.2-9              boot_1.3-25            Matrix_1.2-18          car_3.0-10            
[113] cli_2.5.0              sgeostat_1.0-27        igraph_1.2.6           forcats_0.5.1         
[117] pkgconfig_2.0.3        foreign_0.8-80         laeken_0.5.1           sp_1.4-5              
[121] xml2_1.3.2             annotate_1.66.0        svglite_2.0.0          XVector_0.28.0        
[125] webshot_0.5.2          rvest_1.0.0            NADA_1.6-1.1           stringr_1.4.0         
[129] callr_3.7.0            digest_0.6.27          pracma_2.3.3           pls_2.7-3             
[133] rmarkdown_2.8          cellranger_1.1.0       nortest_1.0-4          curl_4.3              
[137] kernlab_0.9-29         shiny_1.6.0            modeltools_0.2-23      rjson_0.2.20          
[141] lifecycle_1.0.0        nlme_3.1-149           carData_3.0-4          desc_1.3.0            
[145] viridisLite_0.4.0      fansi_0.5.0            pillar_1.6.1           lattice_0.20-41       
[149] GGally_2.1.1           fastmap_1.1.0          httr_1.4.2             DEoptimR_1.0-9        
[153] pkgbuild_1.2.0         survival_3.2-7         glue_1.4.2             remotes_2.4.0         
[157] zip_2.2.0              png_0.1-7              prabclus_2.3-2         bit_4.0.4             
[161] class_7.3-17           stringi_1.5.3          blob_1.2.1             moments_0.14          
[165] memoise_2.0.0          e1071_1.7-7

Here is the pca plots, enter image description here enter image description here

pcaplot deseq2 • 89 views
Entering edit mode
ATpoint ▴ 700
Last seen 1 day ago

Hi, I suggest you post this over at as it is not specifically related to DESeq2 but a general question towards your experiment. Be sure to clean up code and formatting and try to add some background about the experiment and the preprocessing. There is generally a larger audience at biostars for these general questions. Try also to add labels to the PCA plot, e.g. batches of library prep to see whether this clustering can be due to batch effects worth correcting.


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