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$`PCV Result`
#> Combinations PCV_mean_Group1 PCV_mean_Group2 PCV_median_Group1
#> 1 vsn_knn 0.01563742 0.01671153 0.009085376
#> 2 vsn_lls 0.01557428 0.01691132 0.008789145
#> 3 vsn_svd 0.02029744 0.02096730 0.009800237
#> 4 loess_knn 0.01548703 0.01656221 0.009068990
#> 5 loess_lls 0.01542212 0.01672238 0.008835757
#> 6 loess_svd 0.02010146 0.02074614 0.009800570
#> 7 rlr_knn 0.01531832 0.01635141 0.008656845
#> 8 rlr_lls 0.01526014 0.01654432 0.008350407
#> 9 rlr_svd 0.02000539 0.02062160 0.009589709
#> PCV_median_Group2 PCV_sd_Group1 PCV_sd_Group2 Overall_PCV_mean
#> 1 0.009162047 0.02188211 0.02789401 0.01609943
#> 2 0.008873765 0.02325404 0.03118426 0.01613564
#> 3 0.009810854 0.02674776 0.03040037 0.02057308
#> 4 0.009096000 0.02185243 0.02791604 0.01594893
#> 5 0.008847887 0.02306504 0.03078569 0.01596888
#> 6 0.009860503 0.02642583 0.02997883 0.02036606
#> 7 0.008705225 0.02188365 0.02779022 0.01576120
#> 8 0.008379560 0.02322101 0.03097194 0.01579786
#> 9 0.009557701 0.02672238 0.03024527 0.02025546
#> Overall_PCV_median Overall_PCV_sd
#> 1 0.009121854 0.02435171
#> 2 0.008841480 0.02642796
#> 3 0.009759333 0.02817807
#> 4 0.009071945 0.02434108
#> 5 0.008820309 0.02616209
#> 6 0.009817460 0.02781700
#> 7 0.008692614 0.02430915
#> 8 0.008368643 0.02632414
#> 9 0.009589021 0.02809690PEV values result
yeast$`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 0.1758276 0.4224673 0.01799075
#> 5 loess_lls 0.1608813 0.3505809 0.01784844
#> 6 loess_svd 0.2319327 1.4575840 0.01799667
#> 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 0.06261321 0.8740799 1.612586 2.495598
#> 5 0.05119753 0.7713778 1.337569 2.726635
#> 6 0.08130053 1.7090384 4.321391 3.333060
#> 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 0.2570070 12.29045
#> 5 0.2412334 14.32552
#> 6 0.3047618 12.13754
#> 7 0.2331567 12.13988
#> 8 0.2153352 14.40870
#> 9 0.2862557 12.22056PMAD values result
yeast$`PMAD Result`
#> Combinations PMAD_mean_Group1 PMAD_mean_Group2 PMAD_median_Group1
#> 1 vsn_knn 0.1062125 0.1788447 0.06149434
#> 2 vsn_lls 0.1029024 0.1643297 0.06134860
#> 3 vsn_svd 0.1060137 0.2028000 0.06149434
#> 4 loess_knn 0.1062706 0.1702549 0.05819243
#> 5 loess_lls 0.1027986 0.1585553 0.05762739
#> 6 loess_svd 0.1061461 0.1991313 0.05819243
#> 7 rlr_knn 0.1069145 0.1716799 0.06077546
#> 8 rlr_lls 0.1034537 0.1565315 0.06060190
#> 9 rlr_svd 0.1067671 0.1972949 0.06077546
#> PMAD_median_Group2 PMAD_sd_Group1 PMAD_sd_Group2 Overall_PMAD_mean
#> 1 0.10204409 0.1572550 0.2600514 0.3275212
#> 2 0.09250152 0.1333701 0.2747744 0.3457268
#> 3 0.10175997 0.1589957 0.3626523 0.2995501
#> 4 0.09767940 0.1602488 0.2531242 0.3264122
#> 5 0.08415886 0.1335858 0.2790020 0.3422907
#> 6 0.09811666 0.1619706 0.3621268 0.2957790
#> 7 0.10079434 0.1577010 0.2555111 0.3125215
#> 8 0.08443967 0.1328456 0.2722582 0.3307206
#> 9 0.09660103 0.1594251 0.3626211 0.2840227
#> Overall_PMAD_median Overall_PMAD_sd
#> 1 0.1744034 0.6779926
#> 2 0.1702934 0.8159244
#> 3 0.1762055 0.4355489
#> 4 0.1716564 0.6902436
#> 5 0.1674727 0.8078632
#> 6 0.1747291 0.4330612
#> 7 0.1577165 0.6793344
#> 8 0.1531687 0.8108007
#> 9 0.1585464 0.4336766Best combinations
yeast$`Best combinations`
#> PCV_best_combination PEV_best_combination PMAD_best_combination
#> 1 rlr_knn, rlr_lls vsn_lls loess_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 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 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=en_US.UTF-8
#> [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_1.51 lfproQC_1.4.3 rmarkdown_2.31
#>
#> loaded via a namespace (and not attached):
#> [1] gridExtra_2.3 rlang_1.2.0 magrittr_2.0.5
#> [4] otel_0.2.0 matrixStats_1.5.0 e1071_1.7-17
#> [7] compiler_4.6.0 vctrs_0.7.3 reshape2_1.4.5
#> [10] stringr_1.6.0 pkgconfig_2.0.3 crayon_1.5.3
#> [13] fastmap_1.2.0 backports_1.5.1 labeling_0.4.3
#> [16] mlr3_1.6.0 preprocessCore_1.75.0 purrr_1.2.2
#> [19] xfun_0.58 cachem_1.1.0 mlr3misc_0.21.0
#> [22] jsonlite_2.0.0 reshape_0.8.10 uuid_1.2-2
#> [25] cluster_2.1.8.2 parallel_4.6.0 R6_2.6.1
#> [28] bslib_0.11.0 stringi_1.8.7 vcd_1.4-13
#> [31] RColorBrewer_1.1-3 ranger_0.18.0 limma_3.69.2
#> [34] rpart_4.1.27 parallelly_1.47.0 car_3.1-5
#> [37] boot_1.3-32 lmtest_0.9-40 jquerylib_0.1.4
#> [40] Rcpp_1.1.1-1.1 zoo_1.8-15 base64enc_0.1-6
#> [43] Matrix_1.7-5 nnet_7.3-20 tidyselect_1.2.1
#> [46] rstudioapi_0.18.0 abind_1.4-8 yaml_2.3.12
#> [49] mlr3tuning_1.6.0 codetools_0.2-20 affy_1.91.0
#> [52] listenv_0.10.1 lattice_0.22-9 tibble_3.3.1
#> [55] plyr_1.8.9 Biobase_2.73.1 withr_3.0.2
#> [58] S7_0.2.2 evaluate_1.0.5 foreign_0.8-91
#> [61] future_1.70.0 proxy_0.4-29 pillar_1.11.1
#> [64] affyio_1.83.0 BiocManager_1.30.27 carData_3.0-6
#> [67] checkmate_2.3.4 VIM_7.0.0 plotly_4.12.0
#> [70] generics_0.1.4 bbotk_1.10.0 sp_2.2-1
#> [73] ggplot2_4.0.3 scales_1.4.0 laeken_0.5.3
#> [76] globals_0.19.1 class_7.3-23 glue_1.8.1
#> [79] Hmisc_5.2-5 lazyeval_0.2.3 maketools_1.3.2
#> [82] tools_4.6.0 mlr3pipelines_0.11.0 robustbase_0.99-7
#> [85] sys_3.4.3 data.table_1.18.4 vsn_3.81.0
#> [88] buildtools_1.0.0 grid_4.6.0 tidyr_1.3.2
#> [91] crosstalk_1.2.2 colorspace_2.1-2 paradox_1.0.1
#> [94] htmlTable_2.5.0 palmerpenguins_0.1.1 Formula_1.2-5
#> [97] cli_3.6.6 viridisLite_0.4.3 dplyr_1.2.1
#> [100] pcaMethods_2.5.0 gtable_0.3.6 DEoptimR_1.1-4
#> [103] sass_0.4.10 digest_0.6.39 BiocGenerics_0.59.6
#> [106] lgr_0.5.2 htmlwidgets_1.6.4 farver_2.1.2
#> [109] htmltools_0.5.9 lifecycle_1.0.5 mlr3learners_0.14.0
#> [112] httr_1.4.8 statmod_1.5.2 MASS_7.3-65