FragPipeAnalystR LFQ tutorial
Introduction
Here we presented the usage of FragPipeAnalystR to reproduce AP-MS analysis previously demonstrated in the manuscript. Note that in the manuscript, we used the FragPipeAnalyst website, but you could reproduce the same analysis with FragPipeR.
Reading input files
library(FragPipeAnalystR)
se <- make_se_from_files("/Users/hsiaoyi/Documents/workspace/FragPipeR_manuscript/data/AP-MS/combined_protein.tsv",
"/Users/hsiaoyi/Documents/workspace/FragPipeR_manuscript/data/AP-MS/experiment_annotation.tsv",
type = "LFQ", level = "protein")print(head(rownames(se)))## [1] "A0A075B6R9" "A0A075B6S2" "A0A0C4DH68" "A0A2R8Y4L2" "A0FGR8"
## [6] "A0MZ66"plot_pca(se)
plot_correlation_heatmap(se)
plot_missval_heatmap(se)
plot_feature_numbers(se)
colData(se)$condition## [1] "CCND1" "CCND1" "CCND1" "CONTROL" "CONTROL" "CONTROL" "CONTROL"Imputation
imputed_se <- manual_impute(se)plot_pca(imputed_se)
plot_correlation_heatmap(imputed_se)
Differential expression analysis
de_result <- test_limma(imputed_se, type = "all")## Tested contrasts: CCND1_vs_CONTROLde_result_updated <- add_rejections(de_result)plot_volcano(de_result_updated, "CCND1_vs_CONTROL")
The volcano could be labelled in a different way via
name_col argument of the function:
plot_volcano(de_result_updated, "CCND1_vs_CONTROL", name_col="Gene")
sessionInfo()## R version 4.3.1 Patched (2023-10-12 r85331)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4
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## 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
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## time zone: America/Detroit
## tzcode source: internal
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## attached base packages:
## [1] stats graphics grDevices datasets utils methods base
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## other attached packages:
## [1] FragPipeAnalystR_0.1.5
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## 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
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## [73] stats4_4.3.1 plotly_4.10.4
## [75] MSnbase_2.28.1 MALDIquant_1.22.2
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## [83] munsell_0.5.1 scales_1.3.0
## [85] glue_1.7.0 lazyeval_0.2.2
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## [89] mzID_1.40.0 vsn_3.70.0
## [91] mzR_2.36.0 XML_3.99-0.16.1
## [93] grid_4.3.1 impute_1.76.0
## [95] tidyr_1.3.1 RProtoBufLib_2.14.1
## [97] prettyGraphs_2.1.6 MsCoreUtils_1.14.1
## [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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## [109] pcaMethods_1.94.0 gtable_0.3.5
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## [113] BiocGenerics_0.48.1 ggrepel_0.9.5
## [115] SparseArray_1.2.4 farver_2.1.2
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## [121] httr_1.4.7 alluvial_0.1-2
## [123] GlobalOptions_0.1.2 statmod_1.5.0
## [125] MASS_7.3-60