Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks: Experiment data
Meghana Venkata Palukuri, Edward M. Marcotte · Zenodo (CERN European Organization for Nuclear Research) · 2021
Details of experiments are given in the paper, titled 'Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.': https://doi.org/10.1371/journal.pone.0262056 For additional details, please see https://sites.google.com/view/supercomplex/super-complex-v3-0 Supporting code is available on github at: https://github.com/marcottelab/super.complex Details of files provided for each experiment are given below: Toy network experiment Input data: Toy network, available as a weighted edge list. Format: node1 node2 edge-weight All raw toy communities, available as node lists. Format: node1 node2 node3 .. Each line represents a community. Intermediate output results: Training toy communities, available as node lists. Format: node1 node2 node3 .. Each line represents a community. Testing toy communities, available as node lists. Format: node1 node2 node3 .. Each line represents a community. Training toy communities feature matrix, available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative community. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative community, indicated by 1 or 0 respectively) Testing toy communities feature matrix, available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative community. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative community, indicated by 1 or 0 respectively) Output results: Trained toy community fitness function, available as pickled files of a data pre-processor and a machine learning model from sklearn, that can be imported in python using the pickle module, for example using the commands, import pickle; pickle.load(filename) Learned toy communities, available as node lists. Format: node1 node2 node3 .. nodeN Score. Each line represents a community. The score is the community fitness function of the community. Learned toy communities, available as edge lists. Format: node1 node2 edge-weight. A blank line (i.e. two newline characters) separates the edges of one community from another community's edges. hu.MAP experiment: Input data: hu.MAP PPI (protein-protein interaction) network, available as a weighted edge list. Format: gene_ID1 gene_ID2 edge-weight All raw human protein complexes from CORUM, available as node lists. Format: gene_ID1 gene_ID2 gene_ID3 .. Each line represents a protein complex. Intermediate output results: Training complexes from CORUM, available as node lists. Format: gene_ID1 gene_ID2 gene_ID3 .. Each line represents a protein complex. Testing complexes from CORUM, available as node lists. Format: gene_ID1 gene_ID2 gene_ID3 .. Each line represents a protein complex. Training data, i.e. feature matrix of CORUM complexes (with edge weights from hu.MAP PPI network), available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative protein complex. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative protein complex, indicated by 1 or 0 respectively) Testing data, i.e. feature matrix of CORUM complexes (with edge weights from hu.MAP PPI network), available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative protein complex. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative protein complex, indicated by 1 or 0 respectively) Output results: Trained community fitness function of CORUM complexes (with edge weights from hu.MAP), available as pickled files of a data pre-processor and a machine learning model from sklearn, that can be imported in python using the pickle module, for example using the commands, import pickle; pickle.load(filename) Learned protein complexes from hu.MAP PPI network, available as node lists. Format: Excel file, where the columns are - Learned complex name (Named as the most similar CORUM complex, prepended by the Jaccard coefficient similarity) , Proteins in learned complex (gene names, i.e gene_name1 gene_name2 gene_name3 .. gene_nameN ), Proteins in learned complex (gene IDs, i.e gene_ID1 gene_ID2 gene_ID3 .. gene_IDN ) and Score (Community fitness function of the learned protein complex) Learned protein complexes from hu.MAP PPI network, available as gene ID edge lists. Format: gene_ID1 gene_ID2 edge-weight. A blank line (i.e. two newline characters) separates the edges of one complex from another complex's edges. Learned protein complexes from hu.MAP PPI network, available as gene name edge lists. Format: gene_name1 gene_name2 edge-weight. A blank line (i.e. two newline characters) separates the edges of one complex from another complex's edges. Yeast experiments: Input data: DIP yeast PPI network, available as a weighted edge list. Format: gene_ID1* gene_ID2 edge-weight Yeast protein complexes from MIPS, available as node lists. Format: gene_ID1 gene_ID2 gene_ID3 .. Each line represents a protein complex. Yeast protein complexes from TAP-MS, available as node lists. Format: gene_ID1 gene_ID2 gene_ID3 .. Each line represents a protein complex. Experiment 1: Training on TAP-MS and Testing on MIPS: Experiment 1 Intermediate output results: Training data, i.e. feature matrix of TAP-MS complexes (with edge weights from hu.MAP), available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative protein complex. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative protein complex, indicated by 1 or 0 respectively) Testing data, i.e. feature matrix of MIPS complexes (with edge weights from hu.MAP), available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative protein complex. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative protein complex, indicated by 1 or 0 respectively) Experiment 1 output results: Trained community fitness function of TAP-MS complexes (with edge weights from DIP PPI network), available as pickled files of a data pre-processor and a machine learning model from sklearn, that can be imported in python using the pickle module, for example using the commands, import pickle; pickle.load(filename) Learned protein complexes from DIP PPI network, available as node lists. Format: gene_ID1 gene_ID2 gene_ID3 .. gene_IDN Score. Each line represents a protein complex. The score is the community fitness function of the protein complex. Learned protein complexes from DIP PPI network, available as edge lists. Format: gene_ID1 gene_ID2 edge-weight. A blank line (i.e. two newline characters) separates the edges of one complex from another complex's edges. Experiment 2: Training on MIPS and Testing on TAP-MS: Experiment 2 Intermediate output results: Training data, i.e. feature matrix of MIPS complexes (with edge weights from hu.MAP), available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative protein complex. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative protein complex, indicated by 1 or 0 respectively) Testing data, i.e. feature matrix of TAP-MS complexes (with edge weights from hu.MAP), available as a spreadsheet of rows of feature vectors, each corresponding to a positive or negative protein complex. Format: density, number of nodes, degree statistics- maximum, mean, median and variance, clustering coefficient (CC) statistics- maximum, mean and variance, edge weight statistics- mean, maximum and variance, degree correlation (DC) statistics - mean, variance and maximum, 3 Singular values of the subgraph's adjacency matrix, Label (positive or negative protein complex, indicated by 1 or 0 respectively) Experiment 2 output results: Trained community fitness function of MIPS complexes (with edge weights from DIP PPI network), available as pickled files of a dat