Search settings MaxQuant

Transcription

1 Search settings MaxQuant Briefly, we used MaxQuant version with the following settings. As variable modifications we allowed Acetyl (Protein N-terminus), methionine oxidation and glutamine to pyroglutamate conversion. We used the enzymatic rule of Trypsin/P with a maximum of 2 missed cleavages and allowed MaxQuant to perform matching between runs with a match time window of 0.5 min. and an alignment time window of 20 min. We performed LQF Label free quantitation with MaxQuant s standard settings. The maximal number of modifications per peptide was set to 5. As a search fasta file we used the the yeast UniprotKB/Swiss-Prot protein database (version 15.14), supplemented with the 48 protein sequences of the UPS-1 mixture. As a fixed modification, we selected Carbamidomethyl on cysteine residus as all samples are treated with iodoacetamide. For protein quantification in the proteingroups.txt file (not included in supporting files, but available on simple request) we used unique and razor peptides and allowed all modifications as all samples originate in essence from the same yeast lysate and the same UPS spike-in sample. As minimal Andromeda score for unmodified peptides we required a threshold of 30, for modified peptides, this threshold was set on 40. 1

2 Supporting figures 2

3 3

4 Figure S1: Receiver operating characteristic (ROC) curves for the seven analysis methods in comparisons C-A, D-A, E-A, D-B, E-B and E-C. Dots denote the estimated cut-off for each method at 5% FDR. Termination of the curve before the point (1,1) indicates that proteins are either prematurely removed from the analysis, as is the case in the Perseus workflow, or the inability of the models to fit a protein with too few observations, as is the case in the peptide-based models. Figure S2: F1 score for the peptide-based models. The x-axis shows the samples compared and the UPS ratio. The closer the F1 score to 1, the better the performance both in terms of precision and recall at 0.05 FDR. 4

5 Figure S3: F1 score for the aggregated models. The x-axis shows the samples compared and the UPS ratio. The closer the F1-score to 1, the better the performance both in terms of precision and recall at 0.05 FDR. Figure S4: Comparison of the bias term for yeast proteins for all the methods evaluated. The x-axis shows the samples compared and the UPS ratio. 5

6 Figure S5: Comparison of the bias for the UPS proteins for all the methods evaluated. The x-axis shows the samples compared and the UPS ratio. The mean and median aggregation methods analysed with LIMMA drastically underestimate the true differential expression w.r.t the other methods. Figure S6: Comparison of the root mean squared error (RMSE) for the yeast proteins for all the methods evaluated. The x-axis shows the name of compared samples and the UPS ratio. The mixed effects model and the linear model without sample effect always show the lowest RMSE for yeast proteins. 6

7 Figure S7: Comparison of the root mean squared error (RMSE) for the UPS proteins for all the methods evaluated. The x-axis shows the name of compared samples and the UPS ratio. The mixed effects model and the linear model without sample effect outperform all the other methods, since they show the lowest RMSE. 7

8 Figure S8: Boxplots showing the log 2 peptide-intensities in all 43 samples prior to normalization. Skipping the normalization step will lead to reduced power and an increased risk of detecting false positives. 8

9 Figure S9: Boxplots showing the log 2 peptide-intensities in all 43 samples after quantile normalization. 9

10 Figure S10: MA plot for the linear model without sample effect when comparing sample B to sample A. M: estimated log 2 protein fold change, A: average log 2 protein intensity. UPS proteins are depicted in red. The purple line denotes the theoretical log 2 fold change for the yeast proteins, the red line depicts the theoretical log 2 fold change for the UPS proteins based on the spike-in concentrations given by the authors. 10

11 Figure S11: MA plot for the linear model without sample effect when comparing sample E to sample A. M: estimated log 2 protein fold change, A: average log 2 protein intensity. UPS proteins are depicted in red. The purple line denotes the theoretical log 2 fold change for the yeast proteins, the red line depicts the theoretical log 2 fold change for the UPS proteins based on the spike-in concentrations given by the authors. Despite normalization, most yeast proteins show a slightly negative bias. This might be due to the fact that the UPS proteins, which are spiked-in at very high concentrations in sample E will compete for ionization with the yeast proteins. 11

12 Figure S12: Receiver operating characteristic (ROC) curves for normalization on peptide level followed by mean respectively median aggregation as well as mean resp. median aggregation followed by normalization on protein level in comparisons B-A, C-B, D-C and E-D. All analyses are done with LIMMA. Dots denote the estimated cut-off for each method at 5% FDR. Normalization on peptide level followed by mean aggregation generally performs best. 12

13 Supporting tables 13

14 Table S1: General overview per spike-in condition of (1) the total numbers of identified UPS proteins and yeast proteins, (2) the total numbers of UPS and yeast peptide identifications ( non-unique peptides ) and (3) the total numbers of identified unique UPS and yeast peptide sequences. Spike-in condition A B C D E UPS proteins non-unique UPS peptides unique UPS peptides yeast proteins non-unique yeast peptides unique yeast peptides Table S2: Characteristic table for the peptide-based linear model without sample effect ( lmnosamp ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

15 Table S3: Characteristic table for the peptide-based linear model with sample effect ( lmsamp ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D Table S4: Characteristic table for the peptide-based linear mixed effects model with sample effect as a random effect ( mixedsamp ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

16 Table S5: Characteristic table for the Perseus-based workflow with imputation assuming that missing values originate from low-intensity values ( perseusimp ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

17 Table S6: Characteristic table for the Perseus-based workflow without imputation ( perseusnoimp ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D Table S7: Characteristic table for the LIMMA model based on mean aggregation ( limmamean ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

18 Table S8: Characteristic table for the LIMMA model based on median aggregation ( limmamedian ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

19 Table S9: Characteristic table for the peptide-based linear model without sample effect when testing against the median log2 fold change of all proteins instead of testing against 0 ( lmnosamp_extra ). For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

20 Table S10: Characteristic table for the LIMMA model based on mean aggregation ( limmamean ) when mean aggregation is performed before normalization instead of the other way around. For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

21 Table S11: Characteristic table for the LIMMA model based on median aggregation ( limmamedian ) when median aggregation is performed before normalization instead of the other way around. For an explanation of the characteristics, see table S12. Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D

22 Table S12: Explanation of the characteristics outlined in tables S2 S11. Abbreviation Comp bias0 bias1 sd0 sd1 mad0 mad1 RMSE0 RMSE1 TP5proc FP5proc TN5proc FN5proc 1-PPV Description The comparison between the spike-in conditions The bias of the null proteins (yeast proteome) The bias of the differentially expressed proteins (UPS) The standard deviation of the null proteins (yeast proteome) The standard deviation of the differentially expressed proteins (UPS) The median absolute deviation (a robust measure for the sd) of the null proteins (yeast proteome) The median absolute deviation (a robust measure for the sd) of the differentially expressed proteins (UPS) The root mean squared error (a measure combining bias and variance) of the null proteins (yeast proteome) The root mean squared error (a measure combining bias and variance) of the differentially expressed proteins (UPS) The number of true positives at a 5% FDR cut-off The number of false positives at a 5% FDR cut-off The number of true negatives at a 5% FDR cut-off The number of false negatives at a 5% FDR cut-off 1 minus the positive predictive value; a measure for the true false discovery rate (FDR) at the estimated 5% FDR cut-off 22

23 Table S13: Partial areas under the curves (pauc) for a false positive rate (FPR) < 0.1 for each model in each comparison. lmnosamp_extra is the peptidebased linear model without sample effect when testing against the median log 2 fold change of all proteins instead of testing against 0. For the explanation of the other models, see main article. Comp lmnosamp lmsamp mixedsamp perseusimp perseusnoimp limmamean limmamedian lmnosamp_extra B-A C-A D-A E-A C-B D-B E-B D-C E-C E-D Mean

24 Table S14: Relative partial areas under the curves (rpauc) for a false positive rate (FPR) < 0.1 for each model in each comparison. lmnosamp_extra is the peptide-based linear model without sample effect when testing against the median log 2 fold change of all proteins instead of testing against 0. This table is identical to Table 1 in the main article, except for the addition of the lmnosamp_extra model. For the explanation of the other models, see main article. Comp lmnosamp lmsamp mixedsamp perseusimp perseusnoimp limmamean limmamedian lmnosamp_extra B-A 83.01% 12.41% 83.93% 69.65% 55.70% 68.14% 56.21% 85.09% C-A 98.33% 49.31% 98.60% 85.93% 59.76% 87.22% 77.26% 99.21% D-A 99.20% 72.65% 99.26% 86.31% 63.42% 96.79% 95.18% 99.20% E-A 99.72% 89.06% 99.72% 89.62% 63.79% 97.92% 95.93% 99.85% C-B 95.10% 77.81% 94.60% 52.45% 70.08% 61.79% 50.54% 95.50% D-B 97.05% 89.60% 96.72% 71.00% 72.06% 89.21% 83.54% 96.99% E-B 96.98% 93.50% 95.90% 77.68% 72.41% 90.94% 87.69% 97.12% D-C 96.51% 84.85% 96.01% 64.48% 67.09% 59.77% 54.25% 96.49% E-C 97.34% 94.48% 96.21% 72.92% 74.85% 81.94% 79.23% 97.32% E-D 94.06% 79.45% 92.76% 71.13% 78.02% 74.16% 71.21% 94.77% Mean 95.73% 74.31% 95.37% 74.12% 67.72% 80.79% 75.11% 96.15% 24