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add LFQ protein level analysis tutorial source
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--- | ||
title: "FragPipeAnalystR LFQ tutorial" | ||
output: | ||
html_document: | ||
keep_md: yes | ||
--- | ||
|
||
# Introduction | ||
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||
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. | ||
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## Reading input files | ||
``` {r include=T, warning=F} | ||
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") | ||
``` | ||
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||
``` {r include=T, warning=F} | ||
plot_pca(se) | ||
``` | ||
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||
``` {r include=T, warning=F} | ||
plot_correlation_heatmap(se) | ||
``` | ||
|
||
``` {r include=T, warning=F} | ||
plot_missval_heatmap(se) | ||
``` | ||
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||
``` {r include=T, warning=F} | ||
plot_feature_numbers(se) | ||
``` | ||
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||
```{r include=T, warning=F} | ||
colData(se)$condition | ||
``` | ||
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## Imputation | ||
```{r include=T, warning=F} | ||
imputed_se <- manual_impute(se) | ||
``` | ||
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||
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||
``` {r include=T, warning=F} | ||
plot_pca(imputed_se) | ||
``` | ||
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||
``` {r include=T, warning=F} | ||
plot_correlation_heatmap(imputed_se) | ||
``` | ||
|
||
## Differential expression analysis | ||
```{r include=T, warning=F} | ||
de_result <- test_limma(imputed_se, type = "all") | ||
de_result_updated <- add_rejections(de_result) | ||
plot_volcano(de_result_updated, "CCND1_vs_CONTROL") | ||
``` | ||
|
||
``` {r include=T, warning=F} | ||
sessionInfo() | ||
``` |
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--- | ||
title: "FragPipeAnalystR LFQ tutorial" | ||
output: | ||
html_document: | ||
keep_md: yes | ||
--- | ||
|
||
# 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 | ||
|
||
```r | ||
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") | ||
``` | ||
|
||
|
||
```r | ||
plot_pca(se) | ||
``` | ||
|
||
![](global_LFQ_prot_tutorial_files/figure-html/unnamed-chunk-2-1.png)<!-- --> | ||
|
||
|
||
```r | ||
plot_correlation_heatmap(se) | ||
``` | ||
|
||
![](global_LFQ_prot_tutorial_files/figure-html/unnamed-chunk-3-1.png)<!-- --> | ||
|
||
|
||
```r | ||
plot_missval_heatmap(se) | ||
``` | ||
|
||
![](global_LFQ_prot_tutorial_files/figure-html/unnamed-chunk-4-1.png)<!-- --> | ||
|
||
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||
```r | ||
plot_feature_numbers(se) | ||
``` | ||
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||
![](global_LFQ_prot_tutorial_files/figure-html/unnamed-chunk-5-1.png)<!-- --> | ||
|
||
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```r | ||
colData(se)$condition | ||
``` | ||
|
||
``` | ||
## [1] "CCND1" "CCND1" "CCND1" "CONTROL" "CONTROL" "CONTROL" "CONTROL" | ||
``` | ||
|
||
## Imputation | ||
|
||
```r | ||
imputed_se <- manual_impute(se) | ||
``` | ||
|
||
|
||
|
||
```r | ||
plot_pca(imputed_se) | ||
``` | ||
|
||
![](global_LFQ_prot_tutorial_files/figure-html/unnamed-chunk-8-1.png)<!-- --> | ||
|
||
|
||
```r | ||
plot_correlation_heatmap(imputed_se) | ||
``` | ||
|
||
![](global_LFQ_prot_tutorial_files/figure-html/unnamed-chunk-9-1.png)<!-- --> | ||
|
||
## Differential expression analysis | ||
|
||
```r | ||
de_result <- test_limma(imputed_se, type = "all") | ||
``` | ||
|
||
``` | ||
## Tested contrasts: CCND1_vs_CONTROL | ||
``` | ||
|
||
```r | ||
de_result_updated <- add_rejections(de_result) | ||
plot_volcano(de_result_updated, "CCND1_vs_CONTROL") | ||
``` | ||
|
||
![](global_LFQ_prot_tutorial_files/figure-html/unnamed-chunk-10-1.png)<!-- --> | ||
|
||
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```r | ||
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_0.1.4 | ||
## | ||
## 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_1.1.1 | ||
## [17] XVector_0.42.0 labeling_0.4.3 | ||
## [19] utf8_1.2.4 rmarkdown_2.26 | ||
## [21] tzdb_0.4.0 preprocessCore_1.64.0 | ||
## [23] purrr_1.0.2 xfun_0.43 | ||
## [25] zlibbioc_1.48.2 cachem_1.0.8 | ||
## [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_1.8.3 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] lattice_0.21-9 tibble_3.2.1 | ||
## [61] plyr_1.8.9 withr_3.0.0 | ||
## [63] Biobase_2.62.0 evaluate_0.23 | ||
## [65] ConsensusClusterPlus_1.66.0 circlize_0.4.16 | ||
## [67] pillar_1.9.0 affyio_1.72.0 | ||
## [69] BiocManager_1.30.22 MatrixGenerics_1.14.0 | ||
## [71] renv_0.17.0 foreach_1.5.2 | ||
## [73] stats4_4.3.1 plotly_4.10.4 | ||
## [75] MSnbase_2.28.1 MALDIquant_1.22.2 | ||
## [77] ncdf4_1.22 generics_0.1.3 | ||
## [79] RCurl_1.98-1.14 hms_1.1.3 | ||
## [81] S4Vectors_0.40.2 ggplot2_3.5.1 | ||
## [83] munsell_0.5.1 scales_1.3.0 | ||
## [85] glue_1.7.0 lazyeval_0.2.2 | ||
## [87] tools_4.3.1 data.table_1.15.4 | ||
## [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 | ||
## [103] fansi_1.0.6 viridisLite_0.4.2 | ||
## [105] cytolib_2.14.1 S4Arrays_1.2.1 | ||
## [107] ComplexHeatmap_2.18.0 dplyr_1.1.4 | ||
## [109] pcaMethods_1.94.0 gtable_0.3.5 | ||
## [111] sass_0.4.9 digest_0.6.35 | ||
## [113] BiocGenerics_0.48.1 ggrepel_0.9.5 | ||
## [115] SparseArray_1.2.4 farver_2.1.1 | ||
## [117] htmlwidgets_1.6.4 rjson_0.2.21 | ||
## [119] htmltools_0.5.8.1 lifecycle_1.0.4 | ||
## [121] httr_1.4.7 alluvial_0.1-2 | ||
## [123] GlobalOptions_0.1.2 statmod_1.5.0 | ||
## [125] MASS_7.3-60 | ||
``` |
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