Working with lfproQC package
To attach the package in R studio
To find the best combination of normalization and imputation method for the dataset
PCV values result
yeast$`PCV Result`
#> Combinations PCV_mean_Group1 PCV_mean_Group2 PCV_median_Group1
#> 1 vsn_knn 0.01563742 0.01671153 0.009085376
#> 2 vsn_lls 0.01557428 0.01691132 0.008789145
#> 3 vsn_svd 0.02029744 0.02096730 0.009800237
#> 4 loess_knn 0.01548619 0.01655803 0.008986443
#> 5 loess_lls 0.01541044 0.01670319 0.008791060
#> 6 loess_svd 0.02009301 0.02073306 0.009817465
#> 7 rlr_knn 0.01531832 0.01635141 0.008656845
#> 8 rlr_lls 0.01526014 0.01654432 0.008350407
#> 9 rlr_svd 0.02000539 0.02062160 0.009589709
#> PCV_median_Group2 PCV_sd_Group1 PCV_sd_Group2 Overall_PCV_mean
#> 1 0.009162047 0.02188211 0.02789401 0.01609943
#> 2 0.008873765 0.02325404 0.03118426 0.01613564
#> 3 0.009810854 0.02674776 0.03040037 0.02057308
#> 4 0.009064154 0.02183528 0.02788257 0.01594661
#> 5 0.008825419 0.02302518 0.03069866 0.01595420
#> 6 0.009819619 0.02638870 0.02991608 0.02035557
#> 7 0.008705225 0.02188365 0.02779022 0.01576120
#> 8 0.008379560 0.02322101 0.03097194 0.01579786
#> 9 0.009557701 0.02672238 0.03024527 0.02025546
#> Overall_PCV_median Overall_PCV_sd
#> 1 0.009121854 0.02435171
#> 2 0.008841480 0.02642796
#> 3 0.009759333 0.02817807
#> 4 0.009029431 0.02431686
#> 5 0.008796762 0.02610429
#> 6 0.009867787 0.02776905
#> 7 0.008692614 0.02430915
#> 8 0.008368643 0.02632414
#> 9 0.009589021 0.02809690PEV values result
yeast$`PEV Result`
#> Combinations PEV_mean_Group1 PEV_mean_Group2 PEV_median_Group1
#> 1 vsn_knn 0.1750346 0.4416583 0.01844883
#> 2 vsn_lls 0.1605545 0.3526731 0.01771234
#> 3 vsn_svd 0.2332971 1.5140417 0.01844883
#> 4 loess_knn 0.1756443 0.4226978 0.01768420
#> 5 loess_lls 0.1607610 0.3510016 0.01731269
#> 6 loess_svd 0.2323864 1.4532736 0.01768420
#> 7 rlr_knn 0.1753304 0.4426088 0.01867395
#> 8 rlr_lls 0.1607318 0.3615946 0.01817239
#> 9 rlr_svd 0.2333951 1.4919739 0.01896238
#> PEV_median_Group2 PEV_sd_Group1 PEV_sd_Group2 Overall_PEV_mean
#> 1 0.06687189 0.8601269 1.658083 2.508121
#> 2 0.05193112 0.7656055 1.322830 2.774895
#> 3 0.08735927 1.7173591 4.468394 3.405475
#> 4 0.06251937 0.8708275 1.619565 2.493276
#> 5 0.05096026 0.7697870 1.341117 2.718944
#> 6 0.08046607 1.7147858 4.290605 3.326045
#> 7 0.06143150 0.8653132 1.641726 2.473773
#> 8 0.04608619 0.7694183 1.335029 2.732593
#> 9 0.08425770 1.7178645 4.391970 3.359488
#> Overall_PEV_median Overall_PEV_sd
#> 1 0.2538019 12.26901
#> 2 0.2390899 14.60724
#> 3 0.3108805 12.40196
#> 4 0.2548058 12.27346
#> 5 0.2391203 14.27067
#> 6 0.3027093 12.09070
#> 7 0.2331567 12.13988
#> 8 0.2153352 14.40870
#> 9 0.2862557 12.22056PMAD values result
yeast$`PMAD Result`
#> Combinations PMAD_mean_Group1 PMAD_mean_Group2 PMAD_median_Group1
#> 1 vsn_knn 0.1062125 0.1788447 0.06149434
#> 2 vsn_lls 0.1029024 0.1643297 0.06134860
#> 3 vsn_svd 0.1060137 0.2028000 0.06149434
#> 4 loess_knn 0.1063133 0.1703496 0.05911223
#> 5 loess_lls 0.1028750 0.1593060 0.05907470
#> 6 loess_svd 0.1061947 0.1999361 0.05911223
#> 7 rlr_knn 0.1069145 0.1716799 0.06077546
#> 8 rlr_lls 0.1034537 0.1565315 0.06060190
#> 9 rlr_svd 0.1067671 0.1972949 0.06077546
#> PMAD_median_Group2 PMAD_sd_Group1 PMAD_sd_Group2 Overall_PMAD_mean
#> 1 0.10204409 0.1572550 0.2600514 0.3275212
#> 2 0.09250152 0.1333701 0.2747744 0.3457268
#> 3 0.10175997 0.1589957 0.3626523 0.2995501
#> 4 0.09948055 0.1593045 0.2502206 0.3262883
#> 5 0.08558012 0.1332326 0.2785570 0.3416470
#> 6 0.10034390 0.1610855 0.3597532 0.2954359
#> 7 0.10079434 0.1577010 0.2555111 0.3125215
#> 8 0.08443967 0.1328456 0.2722582 0.3307206
#> 9 0.09660103 0.1594251 0.3626211 0.2840227
#> Overall_PMAD_median Overall_PMAD_sd
#> 1 0.1744034 0.6779926
#> 2 0.1702934 0.8159244
#> 3 0.1762055 0.4355489
#> 4 0.1723007 0.6894671
#> 5 0.1693064 0.8061786
#> 6 0.1735256 0.4326712
#> 7 0.1577165 0.6793344
#> 8 0.1531687 0.8108007
#> 9 0.1585464 0.4336766Best combinations
yeast$`Best combinations`
#> PCV_best_combination PEV_best_combination PMAD_best_combination
#> 1 rlr_knn vsn_lls rlr_llsTo visualize the normality by different exploratory plots
1. By boxplot
2. By density plot
3. By correlation heatmap
4. By MDS plot
5. By QQ-plot
Differential expression analysis
To Calculate the top-table values
To visualize the different kinds of differentially abundant proteins, such as up-regulated, down-regulated, significant and non-significant proteins
By MA plot
By volcano plot
Both of the above plots give same result.
To obtain the overall differentially abundant proteins result
To find the up-regulated proteins
To find the down-regulated proteins
To find the other significant proteins
To find the non-significant proteins
The overall workflow of working with the ‘lfproQC’ package
Session Information
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] knitr_1.48 lfproQC_1.3.0 rmarkdown_2.28
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 viridisLite_0.4.2 dplyr_1.1.4
#> [4] farver_2.1.2 fastmap_1.2.0 lazyeval_0.2.2
#> [7] reshape_0.8.9 rpart_4.1.23 digest_0.6.37
#> [10] lifecycle_1.0.4 cluster_2.1.6 statmod_1.5.0
#> [13] magrittr_2.0.3 compiler_4.4.1 rlang_1.1.4
#> [16] Hmisc_5.1-3 sass_0.4.9 tools_4.4.1
#> [19] utf8_1.2.4 yaml_2.3.10 data.table_1.16.0
#> [22] labeling_0.4.3 htmlwidgets_1.6.4 sp_2.1-4
#> [25] plyr_1.8.9 abind_1.4-8 foreign_0.8-87
#> [28] withr_3.0.1 purrr_1.0.2 BiocGenerics_0.51.1
#> [31] sys_3.4.2 nnet_7.3-19 grid_4.4.1
#> [34] preprocessCore_1.67.0 fansi_1.0.6 e1071_1.7-16
#> [37] colorspace_2.1-1 ggplot2_3.5.1 scales_1.3.0
#> [40] MASS_7.3-61 cli_3.6.3 generics_0.1.3
#> [43] rstudioapi_0.16.0 robustbase_0.99-4 httr_1.4.7
#> [46] reshape2_1.4.4 cachem_1.1.0 affy_1.83.0
#> [49] proxy_0.4-27 stringr_1.5.1 zlibbioc_1.51.1
#> [52] BiocManager_1.30.25 vsn_3.73.0 base64enc_0.1-3
#> [55] matrixStats_1.4.1 vctrs_0.6.5 boot_1.3-31
#> [58] Matrix_1.7-0 jsonlite_1.8.8 carData_3.0-5
#> [61] car_3.1-2 htmlTable_2.4.3 Formula_1.2-5
#> [64] maketools_1.3.0 crosstalk_1.2.1 vcd_1.4-13
#> [67] limma_3.61.9 plotly_4.10.4 tidyr_1.3.1
#> [70] jquerylib_0.1.4 affyio_1.75.0 glue_1.7.0
#> [73] DEoptimR_1.1-3 stringi_1.8.4 gtable_0.3.5
#> [76] lmtest_0.9-40 munsell_0.5.1 tibble_3.2.1
#> [79] pillar_1.9.0 pcaMethods_1.97.0 htmltools_0.5.8.1
#> [82] VIM_6.2.2 R6_2.5.1 evaluate_1.0.0
#> [85] lattice_0.22-6 Biobase_2.65.1 highr_0.11
#> [88] backports_1.5.0 bslib_0.8.0 class_7.3-22
#> [91] Rcpp_1.0.13 checkmate_2.3.2 gridExtra_2.3
#> [94] laeken_0.5.3 ranger_0.16.0 xfun_0.47
#> [97] zoo_1.8-12 buildtools_1.0.0 pkgconfig_2.0.3