yeast$`PCV Result`
#> Combinations PCV_mean_Group1 PCV_mean_Group2 PCV_median_Group1
#> 1 knn_vsn 0.01899332 0.02098271 0.010103608
#> 2 knn_loess 0.01894030 0.02092748 0.010089302
#> 3 knn_rlr 0.01841752 0.02035559 0.009524317
#> 4 lls_vsn 0.01907642 0.02125475 0.010014649
#> 5 lls_loess 0.01899846 0.02115904 0.010010652
#> 6 lls_rlr 0.01848859 0.02060921 0.009467251
#> 7 svd_vsn 0.02130958 0.02291101 0.010168022
#> 8 svd_loess 0.02120246 0.02279454 0.010125946
#> 9 svd_rlr 0.02073089 0.02227780 0.009539828
#> PCV_median_Group2 PCV_sd_Group1 PCV_sd_Group2 Overall_PCV_mean
#> 1 0.010248687 0.02510652 0.03202079 0.01988242
#> 2 0.010229406 0.02512094 0.03200966 0.01982868
#> 3 0.009623503 0.02514387 0.03196261 0.01928309
#> 4 0.010188587 0.02607649 0.03436493 0.02003469
#> 5 0.010086266 0.02601915 0.03419267 0.01995005
#> 6 0.009555411 0.02608782 0.03424729 0.01942093
#> 7 0.010275416 0.02666718 0.03180185 0.02203013
#> 8 0.010245751 0.02653714 0.03162730 0.02191917
#> 9 0.009641606 0.02668266 0.03171780 0.02142605
#> Overall_PCV_median Overall_PCV_sd
#> 1 0.010176707 0.02806882
#> 2 0.010154654 0.02807341
#> 3 0.009586895 0.02806629
#> 4 0.010100157 0.02952776
#> 5 0.010037649 0.02942941
#> 6 0.009528427 0.02948794
#> 7 0.010243535 0.02886470
#> 8 0.010174640 0.02871838
#> 9 0.009595804 0.02883757
yeast$`PEV Result`
#> Combinations PEV_mean_Group1 PEV_mean_Group2 PEV_median_Group1
#> 1 knn_vsn 0.06928119 0.2240044 0.01451975
#> 2 knn_loess 0.06934554 0.2236549 0.01372566
#> 3 knn_rlr 0.06940930 0.2287259 0.01407422
#> 4 lls_vsn 0.06557431 0.1924492 0.01415163
#> 5 lls_loess 0.06569981 0.1951490 0.01365153
#> 6 lls_rlr 0.06571568 0.1987836 0.01373442
#> 7 svd_vsn 0.11093175 1.1061681 0.01461283
#> 8 svd_loess 0.11068496 1.0775794 0.01377477
#> 9 svd_rlr 0.11086912 1.0912673 0.01410799
#> PEV_median_Group2 PEV_sd_Group1 PEV_sd_Group2 Overall_PEV_mean
#> 1 0.03569579 0.2318642 0.7077972 3.724615
#> 2 0.03094776 0.2310270 0.7145988 3.718879
#> 3 0.03079165 0.2316963 0.7240438 3.654317
#> 4 0.03066763 0.2131602 0.6289284 3.950675
#> 5 0.02723237 0.2130089 0.6455851 3.926824
#> 6 0.02745115 0.2131217 0.6525758 3.873370
#> 7 0.03798477 0.7564404 3.3158990 4.086699
#> 8 0.03479958 0.7511748 3.2081410 4.048987
#> 9 0.03431090 0.7542221 3.2545234 4.004299
#> Overall_PEV_median Overall_PEV_sd
#> 1 0.3327141 13.04018
#> 2 0.3281350 12.99852
#> 3 0.2951418 12.92005
#> 4 0.3292249 14.94162
#> 5 0.3272115 14.78315
#> 6 0.2939210 14.78547
#> 7 0.3395755 12.49347
#> 8 0.3411323 12.34334
#> 9 0.3048646 12.34118
yeast$`PMAD Result`
#> Combinations PMAD_mean_Group1 PMAD_mean_Group2 PMAD_median_Group1
#> 1 knn_vsn 0.09646213 0.1345474 0.05987853
#> 2 knn_loess 0.09574330 0.1301592 0.05668150
#> 3 knn_rlr 0.09615877 0.1318876 0.05645906
#> 4 lls_vsn 0.09431624 0.1230112 0.05962906
#> 5 lls_loess 0.09353270 0.1203137 0.05666601
#> 6 lls_rlr 0.09397122 0.1199652 0.05586186
#> 7 svd_vsn 0.09526975 0.1532618 0.06110608
#> 8 svd_loess 0.09452733 0.1513812 0.05670321
#> 9 svd_rlr 0.09502991 0.1507820 0.05702269
#> PMAD_median_Group2 PMAD_sd_Group1 PMAD_sd_Group2 Overall_PMAD_mean
#> 1 0.07721333 0.1203723 0.1884689 0.5311463
#> 2 0.07440246 0.1217955 0.1841856 0.5280928
#> 3 0.07107893 0.1211655 0.1899826 0.5067583
#> 4 0.07004497 0.1120849 0.1615279 0.5415995
#> 5 0.06831660 0.1134873 0.1599695 0.5375127
#> 6 0.06530233 0.1127784 0.1621876 0.5169754
#> 7 0.07911398 0.1104997 0.2632386 0.4580580
#> 8 0.07647156 0.1120496 0.2613974 0.4546155
#> 9 0.07120029 0.1112492 0.2637285 0.4335150
#> Overall_PMAD_median Overall_PMAD_sd
#> 1 0.2514293 0.8692545
#> 2 0.2488485 0.8708762
#> 3 0.2218097 0.8683267
#> 4 0.2494979 0.9427988
#> 5 0.2451474 0.9376984
#> 6 0.2194960 0.9390322
#> 7 0.2516930 0.6016608
#> 8 0.2505181 0.6019441
#> 9 0.2232107 0.6003317
To visualize the normality by different exploratory plots
1. By boxplot
Boxplot_data(yeast$knn_rlr_data)
#> Using Majority protein IDs as id variables
2. By density plot
Densityplot_data(yeast$knn_rlr_data)
3. By correlation
heatmap
Corrplot_data(yeast$knn_rlr_data)
4. By MDS plot
MDSplot_data(yeast$knn_rlr_data)
5. By QQ-plot
QQplot_data(yeast$knn_rlr_data)
Differential expression analysis
To Calculate the top-table
values
top_table_yeast <- top_table_fn(yeast$knn_rlr_data, yeast_groups, 2, 1)
To visualize the different kinds of differentially abundant
proteins, such as up-regulated, down-regulated, significant and
non-significant proteins
By MA plot
de_yeast_MA <- MAplot_DE_fn(top_table_yeast,-1,1,0.05)
de_yeast_MA$`MA Plot`
By volcano plot
de_yeast_volcano <- volcanoplot_DE_fn (top_table_yeast,-1,1,0.05)
de_yeast_volcano$`Volcano Plot`
Both of the above plots give same result.
To obtain the overall differentially
abundant proteins result
To find the up-regulated
proteins
de_yeast_MA$`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
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.