FragPipeAnalystR DIA Global Proteome Tutorial
Introduction
FragPipeAnalystR is a R package intended for downstream analysis of data generated from FragPipe. Here we demonstrated the utility of FragPipeAnalyst by reanalyzing a clear cell renal cell carcinoma (ccRCC) data-independent acquisition (DIA) data collected by CPTAC.
CCRCC DIA data
As described in the manuscript, DIA ccRCC data were fetched from Clark et al. (2019) and processed via FragPipe. As you will see in the following sections. The result is quite similar to corresponding TMT data.
library(FragPipeAnalystR)
ccrcc <- make_se_from_files("/Users/hsiaoyi/Documents/workspace/FragPipeR_manuscript/data/DIA_4plex/diann-output.pg_matrix.tsv",
"/Users/hsiaoyi/Documents/workspace/FragPipeR_manuscript/data/DIA_4plex/experiment_annotation_clean.tsv",
type = "DIA")print(head(rownames(ccrcc)))## [1] "A0A024RBG1" "A0A075B6H7" "A0A075B6H9" "A0A075B6I0" "A0A075B6I4"
## [6] "A0A075B6I9"plot_pca(ccrcc)
plot_correlation_heatmap(ccrcc)
plot_missval_heatmap(ccrcc)## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 rows. You can control `use_raster` argument by explicitly setting
## TRUE/FALSE to it.
##
## Set `ht_opt$message = FALSE` to turn off this message.## 'magick' package is suggested to install to give better rasterization.
##
## Set `ht_opt$message = FALSE` to turn off this message.
plot_feature_numbers(ccrcc)
plot_feature(ccrcc, c("Q16790", # CA9
"Q8IVF2", # AHNAK2
"P19404", # NDUFV2
"P01833" # PIGR
))
Imputation
imputed <- manual_impute(ccrcc)plot_pca(imputed)
plot_feature(imputed, c("Q16790", # CA9
"Q8IVF2", # AHNAK2
"P19404", # NDUFV2
"P01833" # PIGR
))
DE result without imputation
de_result <- test_limma(ccrcc, type = "all")## Tested contrasts: Tumor_vs_NATde_result_updated <- add_rejections(de_result)
plot_volcano(de_result_updated, "Tumor_vs_NAT")
DE result with imputation
de_result <- test_limma(imputed, type = "all")## Tested contrasts: Tumor_vs_NATde_result_updated <- add_rejections(de_result)
plot_volcano(de_result_updated, "Tumor_vs_NAT")
One of the differences between this two sets of differential expression analysis is CA9 (Carbonic anhydrase 9) which is a known marker of clear cell renal cell carcinoma.
sessionInfo()## R version 4.3.1 Patched (2023-10-12 r85331)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Detroit
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices datasets utils methods base
##
## other attached packages:
## [1] FragPipeAnalystR_0.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 fdrtool_1.2.17
## [3] rlang_1.1.3 magrittr_2.0.3
## [5] clue_0.3-65 GetoptLong_1.0.5
## [7] matrixStats_1.3.0 compiler_4.3.1
## [9] png_0.1-8 vctrs_0.6.5
## [11] stringr_1.5.1 ProtGenerics_1.34.0
## [13] pkgconfig_2.0.3 shape_1.4.6.1
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## [17] XVector_0.42.0 labeling_0.4.3
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## [27] SNFtool_2.3.1 GenomeInfoDb_1.38.8
## [29] jsonlite_1.8.8 ExPosition_2.8.23
## [31] highr_0.10 DelayedArray_0.28.0
## [33] BiocParallel_1.36.0 parallel_4.3.1
## [35] cluster_2.1.4 R6_2.5.1
## [37] stringi_1.8.4 bslib_0.7.0
## [39] RColorBrewer_1.1-3 limma_3.58.1
## [41] GenomicRanges_1.54.1 jquerylib_0.1.4
## [43] assertthat_0.2.1 Rcpp_1.0.12
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## [47] knitr_1.46 readr_2.1.5
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## [51] Matrix_1.6-1.1 tidyselect_1.2.1
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## [57] codetools_0.2-19 affy_1.80.0
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## [61] plyr_1.8.9 withr_3.0.0
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## [69] BiocManager_1.30.23 MatrixGenerics_1.14.0
## [71] renv_0.17.0 foreach_1.5.2
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## [79] RCurl_1.98-1.14 hms_1.1.3
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## [83] munsell_0.5.1 scales_1.3.0
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## [91] mzR_2.36.0 XML_3.99-0.16.1
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## [99] colorspace_2.1-0 GenomeInfoDbData_1.2.11
## [101] cmapR_1.14.0 cli_3.6.2
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## [105] cytolib_2.14.1 S4Arrays_1.2.1
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## [125] MASS_7.3-60