STEP 20
Effect size estimations of genetic and environmental factors on yeast proteins and modules. This analysis was performed with three yeast proteomic datasets that showed a reliable recovery of known protein-protein interactions (AUC > 0.7), namely (i) Varland et al. (2018), Mol Cell Proteomics, (ii) Lahtvee et al. (2017), Cell Systems, and (iii) Skelly et al. (2013), Genome Research. In the datasets (i) and (ii), yeast cells were exposed to different environmental conditions (i.e. osmotic/temperature/ethanol/nutritional stress); dataset (iii) compared genetically diverse yeast strains. To estimate the extent of variation of both protein complex abundance stoichiometry due to these environmental and genetic condition, we used a similar framework as described in Step 12. Samples for dataset (i) and (ii) were respectively grouped into the environmental conditions, whereas samples for dataset (iii) were grouped into related sets of yeast strains (according to source and collection). For each dataset then these categorizations were tested as predictors for complex abundance and stoichiometry. For each complex, the quality of each model was assessed by the coefficient of determination (R2) in a 10-fold cross-validation scheme, as described in Step12.
Data/Code Requirements for downloading
Python object containing complex-normalized values for each input dataset unzip and unpickle to open.
modulewise_norm_pathway_stoichiometry.zip (665KB)Python object containing pathway-normalized values for each input dataset unzip and unpickle to open.
Effects of genetic and environmental factors on protein variation, as well as variation in module abundance and stoichiometry.
Underlying data for ROC calculation for yeast datasets. Underlying data for Supplementary Figure S6.
suppFigure3_additional_yeast.png
AUC matrix on co-variation.
suppFigure4_additional_yeast.png
AUC matrix on co-variation.