Working with lfproQC package

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

To find the best combination of normalization and imputation method for the dataset

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     0.02809690

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       12.22056

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       0.4336766

To visualize the normality by different exploratory plots

1. By boxplot

Boxplot_data(yeast$`rlr_knn_data`)

2. By density plot

Densityplot_data(yeast$`rlr_knn_data`)

3. By correlation heatmap

Corrplot_data(yeast$`rlr_knn_data`)

4. By MDS plot

MDSplot_data(yeast$`rlr_knn_data`)

5. By QQ-plot

QQplot_data(yeast$`rlr_knn_data`)

Differential expression analysis

To Calculate the top-table values

top_table_yeast <- top_table_fn(yeast$`rlr_knn_data`, yeast_groups, 2, 1)

To visualize the different kinds of differentially abundant proteins, such as up-regulated, down-regulated, significant and non-significant proteins

By MA plot

de_yeast_MA <- MAplot_DE_fn(top_table_yeast,-1,1,0.05)
de_yeast_MA$`MA Plot`

By volcano plot

de_yeast_volcano <- volcanoplot_DE_fn (top_table_yeast,-1,1,0.05)
de_yeast_volcano$`Volcano Plot`

Both of the above plots give same result.

To obtain the overall differentially abundant proteins result

de_yeast_MA$`Result `

To find the up-regulated proteins

de_yeast_MA$`Up-regulated`

To find the down-regulated proteins

de_yeast_MA$`Down-regulated`

To find the other significant proteins

de_yeast_MA$`Significant`

To find the non-significant proteins

de_yeast_MA$`Non-significant`

The overall workflow of working with the ‘lfproQC’ package

Session Information

sessionInfo()
#> R version 4.4.1 (2024-06-14 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 22621)
#> 
#> Matrix products: default
#> 
#> 
#> locale:
#> [1] LC_COLLATE=C                   LC_CTYPE=English_India.utf8   
#> [3] LC_MONETARY=English_India.utf8 LC_NUMERIC=C                  
#> [5] LC_TIME=English_India.utf8    
#> 
#> time zone: Asia/Calcutta
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] knitr_1.48    lfproQC_1.4.0
#> 
#> 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_0.6.36        
#> [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_5.1-2           sass_0.4.9            tools_4.4.1          
#> [19] utf8_1.2.4            yaml_2.3.9            data.table_1.15.4    
#> [22] labeling_0.4.3        htmlwidgets_1.6.4     sp_2.1-4             
#> [25] plyr_1.8.9            abind_1.4-5           foreign_0.8-86       
#> [28] withr_3.0.0           purrr_1.0.2           BiocGenerics_0.50.0  
#> [31] nnet_7.3-19           grid_4.4.1            preprocessCore_1.66.0
#> [34] fansi_1.0.6           e1071_1.7-14          colorspace_2.1-0     
#> [37] ggplot2_3.5.1         scales_1.3.0          MASS_7.3-60.2        
#> [40] cli_3.6.3             rmarkdown_2.27        generics_0.1.3       
#> [43] rstudioapi_0.16.0     robustbase_0.99-3     httr_1.4.7           
#> [46] reshape2_1.4.4        cachem_1.1.0          affy_1.82.0          
#> [49] proxy_0.4-27          stringr_1.5.1         zlibbioc_1.50.0      
#> [52] BiocManager_1.30.23   vsn_3.72.0            base64enc_0.1-3      
#> [55] matrixStats_1.3.0     vctrs_0.6.5           boot_1.3-30          
#> [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] crosstalk_1.2.1       vcd_1.4-12            limma_3.60.4         
#> [67] plotly_4.10.4         tidyr_1.3.1           jquerylib_0.1.4      
#> [70] affyio_1.74.0         glue_1.7.0            DEoptimR_1.1-3       
#> [73] stringi_1.8.4         gtable_0.3.5          lmtest_0.9-40        
#> [76] munsell_0.5.1         tibble_3.2.1          pillar_1.9.0         
#> [79] pcaMethods_1.96.0     htmltools_0.5.8.1     VIM_6.2.2            
#> [82] R6_2.5.1              evaluate_0.24.0       lattice_0.22-6       
#> [85] Biobase_2.64.0        highr_0.11            backports_1.5.0      
#> [88] bslib_0.7.0           class_7.3-22          Rcpp_1.0.13          
#> [91] checkmate_2.3.1       gridExtra_2.3         laeken_0.5.3         
#> [94] ranger_0.16.0         xfun_0.46             zoo_1.8-12           
#> [97] pkgconfig_2.0.3

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.