STEP 3
For the datasets, it is assessed whether the complex median correlation (Pearson’s r) is biased by the abundance of the respective complex or by the complex variance. For comparability, abundances and variances are rank-sorted and further split into 25%-bins; the median correlation is then monitored in each bin. While they were significant differences between some bins, no general trend could be observed and also those significancies could not be recovered consistently across datasets.
complex-mapped and -filtered proteomics data from Battle et al. (2015), Science (Human Individuals)
complex_filtered_battle_ribo.tsv.gz (4.9MB)complex-mapped and -filtered ribosome profiling data from Battle et al. (2015), Science (Human Individuals)
complex_filtered_battle_rna.tsv.gz (4.9MB)complex-mapped and -filtered RNAseq data from Battle et al. (2015), Science (Human Individuals)
complex_filtered_gygi1.tsv.gz (2.2MB)complex-mapped and -filtered proteomics data from Chick et al. (2016), Nature (Founder Mouse strains, MS-proteomics)
complex_filtered_gygi2.tsv.gz (3.9MB)complex-mapped and -filtered RNAseq data from Chick et al. (2016), Nature (Founder Mouse strains, RNA-seq)
complex_filtered_gygi3.tsv.gz (2.8MB)complex-mapped and -filtered proteomics data from Chick et al. (2016), Nature (Founder Mouse strains, MS-proteomics)
complex_filtered_mann.tsv.gz (3.7MB)complex-mapped and -filtered proteomics data from Geiger et al.(2012), Mol Cell Proteomics (Human Cell Types)
complex_filtered_tcga_breast.tsv.gz (2.2MB)complex-mapped and -filtered proteomics data from Mertins et al. (2016), Nature (TCGA Breast Cancer)
complex_filtered_tcga_color.tsv.gz (1.3MB)complex-mapped and -filtered proteomics data from Roumeliotis et al. (2017),Cell (TCGA Colorectal Cancer)
complex_filtered_tcga_ovarian.tsv.gz (2.9MB)complex-mapped and -filtered proteomics data from Zhang et al. (2016), Cell (TCGA Ovarian Cancer)
Python code required for mapping IDs.
Underlying data for Supplementary Figure 2a
Supplementary Figure 2a