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
