FragPipeAnalystR TMT Global Proteome
Introduction
FragPipeAnalystR is a R package intended for FragPipe downstream analysis. We also make it compatible with the result obtained from FragPipe-Analyst. Users are able to reproduce and customize the plots generated in FragPipe-Analyst.
Quick Start Example
library(FragPipeAnalystR)
ccrcc <- make_se_from_files("/Users/hsiaoyi/Documents/workspace/FragPipeR_manuscript/data/TMT_4plex/abundance_protein_MD.tsv",
"/Users/hsiaoyi/Documents/workspace/FragPipeR_manuscript/data/TMT_4plex/experiment_annotation_clean.tsv",
type = "TMT", level = "protein")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)
You may want to check some of known markers through box plots:
plot_feature(ccrcc, c("Q16790", # CA9
"Q8IVF2", # AHNAK2
"P19404", # NDUFV2
"P01833" # PIGR
))
This could be done via Gene column as well:
plot_feature(ccrcc, c("CA9", "AHNAK2", "NDUFV2", "PIGR"), index="Gene")
de_result <- test_limma(ccrcc, type = "all")## Tested contrasts: Tumor_vs_NATde_result_updated <- add_rejections(de_result)Volcano plot is designed for visualizing differential expression analysis result:
plot_volcano(de_result_updated, "Tumor_vs_NAT")
It could be labelled by different column available in the
rowData(de_result_updated) such as Gene:
plot_volcano(de_result_updated, "Tumor_vs_NAT", name_col="Gene")
or_result <- or_test(de_result_updated, database = "Hallmark", direction = "UP")## Background## Uploading data to Enrichr... Done.
## Querying MSigDB_Hallmark_2020... Done.
## Parsing results... Done.## Tumor_vs_NAT## 774 genes are submitted## Uploading data to Enrichr... Done.
## Querying MSigDB_Hallmark_2020... Done.
## Parsing results... Done.
## Background correction... Done.plot_or(or_result)
or_result <- or_test(de_result_updated, database = "Hallmark", direction = "DOWN")## Background## Uploading data to Enrichr... Done.
## Querying MSigDB_Hallmark_2020... Done.
## Parsing results... Done.## Tumor_vs_NAT## 1432 genes are submitted## Uploading data to Enrichr... Done.
## Querying MSigDB_Hallmark_2020... Done.
## Parsing results... Done.
## Background correction... Done.plot_or(or_result)
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
## [15] crayon_1.5.2 fastmap_1.2.0
## [17] XVector_0.42.0 labeling_0.4.3
## [19] utf8_1.2.4 rmarkdown_2.27
## [21] tzdb_0.4.0 preprocessCore_1.64.0
## [23] purrr_1.0.2 xfun_0.44
## [25] zlibbioc_1.48.2 cachem_1.1.0
## [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
## [45] SummarizedExperiment_1.32.0 iterators_1.0.14
## [47] knitr_1.46 readr_2.1.5
## [49] flowCore_2.14.2 IRanges_2.36.0
## [51] Matrix_1.6-1.1 tidyselect_1.2.1
## [53] rstudioapi_0.16.0 abind_1.4-5
## [55] yaml_2.3.8 doParallel_1.0.17
## [57] codetools_0.2-19 affy_1.80.0
## [59] curl_5.2.1 lattice_0.21-9
## [61] tibble_3.2.1 plyr_1.8.9
## [63] withr_3.0.0 Biobase_2.62.0
## [65] evaluate_0.23 ConsensusClusterPlus_1.66.0
## [67] circlize_0.4.16 pillar_1.9.0
## [69] affyio_1.72.0 BiocManager_1.30.23
## [71] MatrixGenerics_1.14.0 renv_0.17.0
## [73] foreach_1.5.2 stats4_4.3.1
## [75] plotly_4.10.4 MSnbase_2.28.1
## [77] MALDIquant_1.22.2 ncdf4_1.22
## [79] generics_0.1.3 RCurl_1.98-1.14
## [81] hms_1.1.3 S4Vectors_0.40.2
## [83] ggplot2_3.5.1 munsell_0.5.1
## [85] scales_1.3.0 glue_1.7.0
## [87] lazyeval_0.2.2 tools_4.3.1
## [89] data.table_1.15.4 mzID_1.40.0
## [91] vsn_3.70.0 mzR_2.36.0
## [93] XML_3.99-0.16.1 grid_4.3.1
## [95] impute_1.76.0 tidyr_1.3.1
## [97] RProtoBufLib_2.14.1 prettyGraphs_2.1.6
## [99] MsCoreUtils_1.14.1 colorspace_2.1-0
## [101] GenomeInfoDbData_1.2.11 cmapR_1.14.0
## [103] cli_3.6.2 fansi_1.0.6
## [105] viridisLite_0.4.2 cytolib_2.14.1
## [107] S4Arrays_1.2.1 ComplexHeatmap_2.18.0
## [109] dplyr_1.1.4 pcaMethods_1.94.0
## [111] gtable_0.3.5 sass_0.4.9
## [113] digest_0.6.35 BiocGenerics_0.48.1
## [115] ggrepel_0.9.5 SparseArray_1.2.4
## [117] farver_2.1.2 htmlwidgets_1.6.4
## [119] rjson_0.2.21 htmltools_0.5.8.1
## [121] lifecycle_1.0.4 httr_1.4.7
## [123] alluvial_0.1-2 GlobalOptions_0.1.2
## [125] statmod_1.5.0 MASS_7.3-60