Additional file 1 of Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond
Creators: name: Baptista, Anthony
name: Brière, Galadriel
name: Baudot, Anaïs
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Additional information. Supplementary Section 1. Multilayer networks. Supplementary Section 2. Node prioritisation to study human genetic diseases. Supplementary Section 3. Supervised prediction of gene-disease associations. Supplementary Section 4. Diffusion profiles comparison to unveil immune diseases similarities. Supplementary Section 5. MultiXrank workflow. Supplementary Table S1. Monoplex, multiplex and bipartite networks used in our three study cases. Of note, this tableincludes only the networks used in our analysis and does not encompass the broader non-network format information used in our studies. Supplementary Table S2. PCHi-C fragment networks characteristics. Supplementary Table S3. MultiXrank parameters used for node prioritisation in leukaemia. Supplementary Table S4. Top 10 prioritised genes for leukaemia, associated MultiXrank scores, degree and distance to seed nodes. Supplementary Table S5. Top 10 prioritised drugs for leukaemia, associated MultiXrank scores, degree and distance to seed nodes. Supplementary Table S6. Four sets of MultiXrank parameters used for node prioritisation in epilepsy. It is to note that only the λ parameter differs. Supplementary Table S7. DrugBank Categories associated to at least 7 drugs from the 41 drugs prioritised by MultiXrank (parameter set number 4, top-100) that are not prioritised by Hetionet. The first column of the table corresponds to the DrugBank Category and the second column (N ) corresponds to the number of drugs mapped to each category. It is to note that some drugs belong to more than one class. Supplementary Table S8.. MultiXrank parameters used for the supervised prediction of gene-disease associations. Supplementary Table S9. XGBoost and Random Forest models and performance metrics for predicting Gene-Diseaseassociations from DisGeNET v2.0 (2014). The models are trained and tested according to the protocol described in Supplementary Figure S5. We trained the XGBoost and Random Forest models using various class weights, large weights penalising classification errors for the corresponding class (pos., for positive associations and neg., for negative associations). We report several performance metrics for each model: Balanced Accuracy (defined as the average of recall obtained on each class), F1-score and number of True Positives (TP), False Positives (FP), True Negatives (TN) and False Negative (FN). The best performing model is highlighted in grey. Supplementary Table S10. XGBoost and Random Forest models and performance metrics for predicting Gene-Disease associations from DisGeNET v7.0 (2020). The models are the same as those presented in Supplementary Table S9, and have not been retrained for predicting DisGeNET v7.0 (2020) associations. We report several performance metrics for each model: Balanced Accuracy (defined as the average of recall obtained on each class), F1-score and number of True Positives (TP), False Positives (FP), True Negatives (TN) and False Negative (FN). The best performing model is highlighted in grey similarities. Supplementary Table S11. MultiXrank parameters used for diffusion profiles comparison to unveil immune diseases. Supplementary Table S12. Composition and characteristics of the immune disease clusters. Supplementary Table S13. List of the 131 immune diseases considered in this study. The first column represents the number used to identify each disease in the t-SNE projection. The second column represents the name of the disease. The third column is the UMLS identifier of the disease. Supplementary Figure S1. Tree lineages of hematopoietic cells. MPP: Multi-Potent Progenitor; CLP: Common LymphoidProgenitor; CMP: Common Myeloid Progenitor; GMP: Granulocyte/Macrophage Progenitor ; MEP:Megakaryocyte/Erythrocyte Progenitor. Lymphoid cells and myeloid cells included in this study are indicated in red and blue, respectively. Supplementary Figure S2 Left: Largest component of the PCHi-C fragment network built from the nB cells dataset. Right:Degree distribution of the PCHi-C fragment network defined on the left, with a zoom on low degree nodes. Supplementary Figure S3. Representation of the multilayer network used for node prioritisation in leukaemia. The network iscomposed of a gene multiplex network, a drug multiplex network and their associated bipartite network. The gene multiplex network contains nodes corresponding to genes/proteins and edges corresponding to protein-protein, molecular complex, and Reactome pathway associations. The drug multiplex network contains nodes corresponding to drugs and edges corresponding to pharmacological, experimental, predicted, and clinical drugdrug interactions (Supplementary section 1.A). For the sake of clarity, the bipartite network containing the interactions connecting the two different types of nodes of the two multiplex networks is represented by the black arrow. Supplementary Figure S4. Ictogenic properties of the 100 drugs prioritised by Hetionet and overlap with the top 100, 200, and 500 drugs prioritised by MultiXrank (parameter set 1 to 3, see Supplementary Table S6). The data on ictogenic properties has been sourced from the Hetionet study. AIGD are anti-ictogenic drugs that have a seizure suppressor effect (beige), IGD are ictogenic drugs (pink), and UKND are drugs with unknown effects (grey). The outer circle of the pie chart displays the number of AIGD, IGD, and UNKD drugs amongst the 100 drugs prioritised by Hetionet. The center of the pie chart displays the number of drugs from each category that were also prioritised by MultiXrank in the top-100 (displayed in lighter shades), top-200 (displayed in middle shade), and top-500 drugs (displayed in darker shades). The drugs prioritised in the top-500 include the drugs prioritised in the top-200, and the top-200 includes the drugs prioritised in the top-100. The white part of the pie chart corresponds to the drugs prioritised by Hetionet that are not in our MultiXrank top-500 prioritised drugs. Supplementary Figure S5. Workflow of the random forest binary classifier. The left panel represents the training step. Here, the bipartite network connecting the gene multiplex and the disease monoplex network is built from DisGeNET v2.0 (2014). The gene-disease associations in DisGeNET v2.0 (2014) are also considered positive gene-disease associations. Negative gene-disease associations are sampled randomly. Then, MultiXrank is run using the gene and the disease nodes of each positive and negative gene-disease association, and the output scores are saved as described in the matrix *. This matrix is used with the positive and negative labels to train a random forest binary classifier. The right panel represents the test step. In this case, the true positive gene-disease associations are created from DisGeNET v7.0 (2020) and true negative gene-disease associations are sampled randomly. We next ran MultiXrank using as seeds the gene and disease nodes from each positive and negative gene-disease association, and saved the output scores in the matrix **. Finally, this ** matrix is used as an input of the previously trained random forest classifier to predict the labels of each gene-disease association. The predicted labels are then compared with the true known labels. The protocol is the same for two or three multiplex networks. Supplementary Figure S6. 2D PCA projection of the Jaccard index similarities between the different hematopoietic cell types. Left: similarities computed on the PCHi-C fragment dataset. Right: similarities computed on the TAD dataset. Red: Lymphoid cells. Blue: Myeloid cells. Supplementary Figure S7. 2D PCA projection of the similarity between the hematopoietic cell types. The tree lineage of hematopoietic cells is correctly found with the PCHi-C fragment, TAD and disease output scores of MultiXrank. However, the tree lineage is not recovered for the protein output scores. Supplementary Figure S8. 2D PCA projection of hematopoietic cell similarities with respect to the integrated MultiXrank output scores effectively visualises the similarity between various hematopoietic cell types. In this projection, lymphoid cells (depicted in red) and myeloid cells (depicted in blue) exhibit a relative separation within the PCA space. Moreover, nCD4 and nCD8 cells are in close proximity to each other and relative proximity with nB cells. Furthermore, Macrophage (Mac0) and its precursor, Monocyte (Mon), appear closely situated. Additionally, Erythrocyte (Ery) and Megakaryocyte (MK), both stemming from the same progenitor, also exhibit high proximity to each other. Supplementary Figure S9. Workflow of the MultiXrank Python package. The networks and the parameters need to be ordered as in the tree file in the left panel. This tree file is used by MultiXrank to run the RWR on the multilayer network defined in the bipartite and multiplex folders with the parameters given in the "config.yml" and "seeds.txt" files. Finally, MultiXrank returns the output scores in an output folder as shown in the right panel, each multiplex/monoplex network is associated with a ranking distribution of the nodes involved in this specific network. A Conda environment is available on GitHub ( https://github.com/anthbapt/multixrank ) to install all dependencies needed by MultiXrank.