MSstatsGroupComparisonSingleProtein.RdGroup comparison for a single protein
MSstatsGroupComparisonSingleProtein( single_protein, contrast_matrix, repeated, groups, samples_info, save_fitted_models, has_imputed )
| single_protein | data.table with summarized data for a single protein |
|---|---|
| contrast_matrix | contrast matrix |
| repeated | if TRUE, repeated measurements will be modeled |
| groups | unique labels of experimental conditions |
| samples_info | number of runs per group |
| save_fitted_models | if TRUE, fitted model will be saved. If not, it will be replaced with NULL |
| has_imputed | TRUE if missing values have been imputed |
#> INFO [2021-07-05 20:05:26] ** Features with one or two measurements across runs are removed. #> INFO [2021-07-05 20:05:26] ** Fractionation handled. #> INFO [2021-07-05 20:05:26] ** Updated quantification data to make balanced design. Missing values are marked by NA #> INFO [2021-07-05 20:05:26] ** Log2 intensities under cutoff = 3.776 were considered as censored missing values. #> INFO [2021-07-05 20:05:26] ** Log2 intensities = NA were considered as censored missing values. #> INFO [2021-07-05 20:05:26] ** Use all features that the dataset originally has. #> INFO [2021-07-05 20:05:26] #> # proteins: 2 #> # peptides per protein: 2-2 #> # features per peptide: 3-3 #> INFO [2021-07-05 20:05:26] #> 1 2 3 4 5 6 7 8 9 10 #> # runs 3 3 3 3 3 3 3 3 3 3 #> # bioreplicates 3 3 3 3 3 3 3 3 3 3 #> # tech. replicates 1 1 1 1 1 1 1 1 1 1 #> INFO [2021-07-05 20:05:26] == Start the summarization per subplot... #> | | | 0% | |=================================== | 50% | |======================================================================| 100% #> INFO [2021-07-05 20:05:27] == Summarization is done.group_comparison_input <- MSstatsPrepareForGroupComparison(QuantData) levels(QuantData$ProteinLevelData$GROUP)#> [1] "1" "10" "2" "3" "4" "5" "6" "7" "8" "9"comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1) row.names(comparison) <- "T7-T1" groups = levels(QuantData$ProteinLevelData$GROUP) colnames(comparison) <- groups[order(as.numeric(groups))] samples_info <- getSamplesInfo(QuantData) repeated <- checkRepeatedDesign(QuantData) single_output <- MSstatsGroupComparisonSingleProtein( group_comparison_input[[1]], comparison, repeated, groups, samples_info, FALSE, TRUE) single_output # same as a single element of MSstatsGroupComparison output#> [[1]] #> RUN Protein ABUNDANCE originalRUN GROUP SUBJECT TotalGroupMeasurements #> 1: 1 IDHC 5.577050 1 1 ReplA 18 #> 2: 2 IDHC 6.811034 2 1 ReplB 18 #> 3: 3 IDHC 6.909093 3 1 ReplC 18 #> 4: 4 IDHC 6.373069 4 2 ReplA 18 #> 5: 5 IDHC 6.595750 5 2 ReplB 18 #> 6: 6 IDHC 6.349843 6 2 ReplC 18 #> 7: 7 IDHC 6.140378 7 3 ReplA 18 #> 8: 8 IDHC 6.600981 8 3 ReplB 18 #> 9: 9 IDHC 7.033944 9 3 ReplC 18 #> 10: 10 IDHC 6.790116 10 4 ReplA 18 #> 11: 11 IDHC 6.174932 11 4 ReplB 18 #> 12: 12 IDHC 7.313115 12 4 ReplC 18 #> 13: 13 IDHC 7.260217 13 5 ReplA 18 #> 14: 14 IDHC 7.209141 14 5 ReplB 18 #> 15: 15 IDHC 6.519457 15 5 ReplC 18 #> 16: 16 IDHC 9.653219 16 6 ReplA 18 #> 17: 17 IDHC 9.542782 17 6 ReplB 18 #> 18: 18 IDHC 9.553527 18 6 ReplC 18 #> 19: 19 IDHC 12.556377 19 7 ReplA 18 #> 20: 20 IDHC 12.653104 20 7 ReplB 18 #> 21: 21 IDHC 12.550847 21 7 ReplC 18 #> 22: 22 IDHC 12.722041 22 8 ReplA 18 #> 23: 23 IDHC 12.782814 23 8 ReplB 18 #> 24: 24 IDHC 12.806758 24 8 ReplC 18 #> 25: 25 IDHC 12.663855 25 9 ReplA 18 #> 26: 26 IDHC 12.728728 26 9 ReplB 18 #> 27: 27 IDHC 12.666809 27 9 ReplC 18 #> 28: 28 IDHC 12.720290 28 10 ReplA 18 #> 29: 29 IDHC 12.776446 29 10 ReplB 18 #> 30: 30 IDHC 12.716287 30 10 ReplC 18 #> RUN Protein ABUNDANCE originalRUN GROUP SUBJECT TotalGroupMeasurements #> NumMeasuredFeature MissingPercentage more50missing NumImputedFeature #> 1: 6 0.0000000 FALSE 0 #> 2: 6 0.0000000 FALSE 0 #> 3: 6 0.0000000 FALSE 0 #> 4: 6 0.0000000 FALSE 0 #> 5: 6 0.0000000 FALSE 0 #> 6: 6 0.0000000 FALSE 0 #> 7: 6 0.0000000 FALSE 0 #> 8: 6 0.0000000 FALSE 0 #> 9: 6 0.0000000 FALSE 0 #> 10: 6 0.0000000 FALSE 0 #> 11: 6 0.0000000 FALSE 0 #> 12: 6 0.0000000 FALSE 0 #> 13: 6 0.0000000 FALSE 0 #> 14: 6 0.0000000 FALSE 0 #> 15: 5 0.1666667 FALSE 1 #> 16: 6 0.0000000 FALSE 0 #> 17: 6 0.0000000 FALSE 0 #> 18: 6 0.0000000 FALSE 0 #> 19: 6 0.0000000 FALSE 0 #> 20: 6 0.0000000 FALSE 0 #> 21: 6 0.0000000 FALSE 0 #> 22: 6 0.0000000 FALSE 0 #> 23: 6 0.0000000 FALSE 0 #> 24: 6 0.0000000 FALSE 0 #> 25: 6 0.0000000 FALSE 0 #> 26: 6 0.0000000 FALSE 0 #> 27: 6 0.0000000 FALSE 0 #> 28: 6 0.0000000 FALSE 0 #> 29: 6 0.0000000 FALSE 0 #> 30: 6 0.0000000 FALSE 0 #> NumMeasuredFeature MissingPercentage more50missing NumImputedFeature #> residuals fitted #> 1: -0.855342665 6.432392 #> 2: 0.378641827 6.432392 #> 3: 0.476700838 6.432392 #> 4: -0.066484645 6.439554 #> 5: 0.156195841 6.439554 #> 6: -0.089711196 6.439554 #> 7: -0.451390142 6.591768 #> 8: 0.009213409 6.591768 #> 9: 0.442176733 6.591768 #> 10: 0.030728167 6.759388 #> 11: -0.584455998 6.759388 #> 12: 0.553727831 6.759388 #> 13: 0.263945670 6.996272 #> 14: 0.212869266 6.996272 #> 15: -0.476814936 6.996272 #> 16: 0.070042510 9.583176 #> 17: -0.040393598 9.583176 #> 18: -0.029648912 9.583176 #> 19: -0.030399386 12.586776 #> 20: 0.066328066 12.586776 #> 21: -0.035928680 12.586776 #> 22: -0.048496419 12.770538 #> 23: 0.012275943 12.770538 #> 24: 0.036220476 12.770538 #> 25: -0.022608684 12.686464 #> 26: 0.042264117 12.686464 #> 27: -0.019655434 12.686464 #> 28: -0.017384065 12.737674 #> 29: 0.038771530 12.737674 #> 30: -0.021387464 12.737674 #> residuals fitted #> #> [[2]] #> logFC SE Tvalue DF pvalue Protein Label issue #> 1: 6.154384 0.2917031 21.09811 18 3.819167e-14 IDHC T7-T1 NA #> MissingPercentage ImputationPercentage #> 1: 0 0 #> #> [[3]] #> NULL #>