Introduction Reference System Requirements GIST (Gibbs sampler Infers Signal Transduction)... 4

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1 IMPACT user manual

2 Introduction... 3 Reference... 3 System Requirements... 3 GIST (Gibbs sampler Infers Signal Transduction)... 4 Framework... 4 Input Files... 5 Key Functions and Usage... 6 Output files SOUL (Structural Organization Uncovers pathway Landscape) Framework Input Files Key Functions and Usage Output files Examples Running GIST Running SOUL Re-running SOUL... 20

3 Introduction The IMPACT (Infer Modularization of PAthway CrossTalk) algorithm is a pipeline for pathway identification, which consists of two methods: GIST (Gibbs sampler Infer Signal Transduction) and SOUL (Structural Organization Uncovers pathway Landscape). GIST is a distribution learning method that builds pathways among given source and target proteins. By sampling pathway states according to pathway potential distributions, GIST extracts signal transduction pathways that are associated with phenotype and consent with knowledge. SOUL is a post-processing method that utilizes pathway samples from GIST to further investigate pathway modularization and crosstalk. IMPACT offers a novel perspective to identify aberrant signal transduction in cancer study by emphasizing on nodal proteins that crosslink multiple signaling pathways instead of studying individual pathways independently. The identified nodal proteins by IMPACT may be potential drug targets that oversee multiple disease related pathways. Reference Jinghua Gu, Jianhua Xuan, Ayesha N. Shajahan-Haq, Diane M. Demas, and Robert Clarke, Unraveling the intracellular signal transduction and pathway crosstalk by exploring pathway landscape, submitted to ** System Requirements The software was developed on Matlab R2012a and is compatible with both Windows and Linux enviroments. This package also includes Matlab BGL library for calculating betweenness centrality (./matlab_bgl/*). Please see LICENSE1.txt, LICENSE2.txt, and readme.txt for copy right information and disclaimer.

4 GIST (Gibbs sampler Infers Signal Transduction) Framework

5 Input Files 1. Gene_expression_data.txt Gene_expression_data.txt provides the gene expression of the genes across multiple samples with each row corresponding to one gene and each column corresponding to one sample. Sample names are listed in the first row and gene names are listed in the first column. Both Entrez_id and Gene_symbol can be used as the gene names with title Entrez_id and Gene_symbol, respectively. Note: Microarray gene expression data after logarithm is preferred for the sake of statistics calculated and used in the algorithm. 2. Phenotype_info.txt Phenotype_info.txt provides the phenotype information of the samples, where the sample names are listed in the first column and the corresponding phenotypes are listed in the second column. Note: The current version of the package is designed for comparison between samples from two different phenotypes. Differential study among multiple phenotypes will be extended in the future. 3. input_network.txt input_network.txt provides the interaction information of the genes with Entrez_id as the gene names. 4. Source_gene.txt, Sink_gene.txt Source_gene.txt and Sink_gene.txt provide the start and target genes of the pathways of interest. Official gene symbols are used as the gene names. 5. location_info.xls* location_info.xls provides the cellular location information of the genes. Users can use the one included in the package or provide their own information. 6. weight_parameters.txt*

6 weight_parameters.txt provides the weights used in the cost function. Users can modify the weight parameters for their specific case study. If not provided, the default values of the weight parameters are all one. *: If they are not provided, default settings are used. Key Functions and Usage Function: Main_GIST_func(L, ite, PATHNUM, INCONS, weights); Description: The main function of the GIST algorithm, which implements GIST algorithm on the given input files with predefined parameters which can be user defined or default values. The results are the selected top ranked samples of pathways as well as the estimated pathway landscape. Arguments: Table 1 Arguments of Main_GIST_func() Name Description Default value L Length of pathway 8 ite Number of samples of pathways PATHNUM Number of top ranked pathways saved for SOUL 100 INCONS Indicator of whether inconsistent pathways would be filtered weights Name of the weight parameter file All ones 1 Function: [G_entrez, G_loc, G_locn, G_symbol, G_p, G_nodez, G0, G1, G_fld, source, sink] = Get_input(); Description: Arguments: Calculate the values needed for the GIST algorithm from input files.

7 Table 2 Arguments of Get_input() Name Description Example Is mandatory G_entrez Entrez gene id 7157 NO G_loc Subcellular location Nucleus Yes** G_locn Subcellular location id 4 Yes G_symbol Official gene symbol TP53 Yes G_p Node p-value Yes** G_nodesz Node z-score Yes G0 Sparse binary network (undirected) 0 or 1 Yes G1 Sparse weighted network (undirected) Yes G_fld Log2 fold change Yes** source source gene symbols ESR1 Yes sink target gene symbols TP53 Yes **: data that is not directly required for the algorithm, but is only needed for complete summarization (annotation) of output results (e.g., fold change of genes). Function: F0 = bldflownet(g0,source,sink,l); Description: Build flow network between the given source nodes and sink nodes. Arguments: Name G0 source sink L F0 Table 3 Arguments of bldflownet() Description Sparse binary network (undirected) Source/start proteins Target/end proteins Length of pathway Flow network (unweighted)

8 Function: G = bldweightmatrix(f0,g0,g1,delta); Description: Build directed weighted matrix for given group. Arguments: Name F0 G0 G1 delta G Table 4 Arguments of bldweightmatrix() Description Flow network (unweighted) Sparse binary network (undirected) Sparse weighted network (undirected) Baseline score for pseudo-edges (delta = -5 as default) Sparse weighted network (directed) Function: Description: [S V W valid_path valid_edge] = rndinitial(g,g0,f0,h,l); Random initialization: a path is randomly generated as the starting state of the sampling procedure, where pseudo-edges may be included. Arguments: Name G G0 F0 H L S V W Table 5 Arguments of rndinitial() Description Sparse weighted network (directed) Sparse binary network (undirected) Flow network (unweighted) Node z-score Pathway length Initial pathway Pathway node potential Pathway edge potential

9 valid_path valid_edge If pathway contains pseudo-edges (binary indicator) (L-1) 1 vector indicating if an edge is a pseud-edge Function: [sampledpaths, pathfreq, pathscore] = GIST(G, G0, G_locn, F0, G_nodez, V, W, S, valid_edge, L, ite, T, weight); Description: Arguments: GIST algorithm: generate a sequence of samples from a sampling procedure. Name G G0 G_locn F0 G_nodez V W S valid_path valid_edge L ite T weight sampledpaths pathfreq pathscore Table 6 Arguments of GIST() Description Sparse weighted network (directed) Sparse binary network (undirected) 1:extracellular space; 2: plasma membrane; 3: cytoplasm; 4: nucleus Flow network (unweighted) Node z-score Pathway node potential of the initial pathway Pathway edge potential of the initial pathway Initial pathway If pathway contains pseudo-edges (binary indicator) (L-1) 1 vector indicating if an edge is a pseud-edge Pathway length Number of sampling iterations Temperature weight_parameters.txt Sampled pathways Frequency of sampled pathway Likelihood score of sampled pathway

10 Function: [gist_slist, gist_wlist, gist_symb, network, G_nscore, G_bc] = EST_edge(rankedSampledPaths1, rankedpathscore1, rankedpathsymb1, G, G_locn, G_symbol, PATHNUM,INCONS); Description: Arguments: Estimate the pathway landscape from the obtained samples of pathways. Name rankedsampledpaths1 rankedpathscore1 rankedpathsymb1 G G_locn G_symbol PATHNUM INCONS gist_slist gist_wlist gist_symb network G_nscore G_bc Table 7 Arguments of est_edge() Description Gene index of the samples of pathways ranked according to pathway potential score Pathway potential score Gene symbols of the ranked pathways Sparse weighted network (directed) 1:extracellular space; 2: plasma membrane; 3: cytoplasm; 4: nucleus Official gene symbol Number of top pathways used to estimate edge attributes Indicator of whether inconsistent pathways would be filtered Gene index of the selected top pathways Potential score of the selected top pathways Gene symbols of the selected top pathways Estimated pathway landscape with estimated edge score and estimated edge direction Estimated node score in the estimated pathway landscape Betweenness centrality of the nodes in the estimated pathway landscape Output files

11 The outputs are saved in the./outputs folder with both.mat and.xls formats. 1. output_gist_results.mat Selected top ranked samples of pathways are saved with.mat format for the following SOUL algorithm. Pathway landscape estimated from the top ranked samples in this case study is also saved in the file. 2. output_gist_network.xls; output_gist_node_attr.xls Estimated pathway landscape as well as the calculated statistics of the genes and the interactions is saved in these two files, which can be imported to Cytoscape for visualization. 3.output_GIST_SelectedPathScore.xls; output_gist_selectedpathsymb.xls These two files save top ranked samples of pathways for the following SOUL algorithm.

12 SOUL (Structural Organization Uncovers pathway Landscape) Framework

13 Input Files SOUL algorithm further analyzes the samples generated from GIST algorithm to reveal pathway landscape. Thus, the outputs of the GIST algorithm are the inputs of the SOUL algorithm. Users can pile up multiple sets of samples generated from GIST in different case studies as the input files for SOUL to identify cross talk of the pathways interested in different case studies. 1. output_gist_node_attr.xls Characteristics of the genes involved in the selected top ranked samples of pathways. 2.output_GIST_SelectedPathScore.xls; output_gist_selectedpathsymb.xls Selected top ranked samples of pathways generated by the GIST algorithm. Key Functions and Usage Function: main_soul_func(incluster, clusterid) Description: The main function of the SOUL algorithm, which implements the SOUL algorithm on the selected top ranked samples of pathways generated by the GIST algorithm. The results are the estimated pathway landscape. Arguments: Table 8 Arguments of Main_SOUL_func() Name Description Default value INCLUSTER clusterid* Indicator of whether further select samples using clustering method Name of the file of the index of pathways of interest 1 Not available

14 *When the SOUL algorithm is implemented for the first time, the clusterid file is not available. Then, the pathways automatically selected from hieratical clustering will be used for pathway landscape estimation, and the index of the further selected pathways corresponding to the clustering labels will be saved in cluster_id.xls. Based on the structural heatmap of the clustering result and the pathway scores, user may further select clusters of pathways of interest, and then modify the cluster_id.xls file to re-estimate pathway landscape using SOUL_part2(). Function: SOUL_part2() Description: Use the clustering results together with the index of samples of pathways as the inputs to estimate the pathway landscape. Both the inputs and the outputs are the.xls files. The output files are the output files of the main_soul_func(). Function: [network, G_nscore, G_bc] = est_edge_sub(gist_slist, gist_wlist, G_symbol) Description: Estimate the pathway landscape from the obtained samples of pathways. est_edge_sub(), which is the simplified version of EST_edge(), uses all of the samples of pathways in the inputs for pathway landscape estimation. Arguments: Name gist_slist gist_wlist G_symbol network G_nscore G_bc Table 9 Arguments of est_edge() Description Gene index of the selected top pathways Potential score of the selected top pathways Official gene symbol Estimated pathway landscape with estimated edge score and estimated edge direction Estimated node score in the estimated pathway landscape Betweenness centrality of the nodes in the estimated pathway landscape

15 Output files 1. output_soul_network.xls; output_soul_node_attr.xls The major results of the IMPACT algorithm, which can be imported into Cytoscape for visualization. Estimated pathway landscape as well as the calculated statistics of the genes and the interactions is saved in these two files. Table 10 Format of SOUL_network_xls Column Description Example 1 Protein 1* IRS1 2 Protein 2* BIRC5 3 Edge direction probability 1 4 Normalized edge score *: in the context of directed network, an edge always starts from protein1 to protein 2. Table 11 Format of SOUL_node_attr.xls Column Description Example 1 Official gene symbol BRCA1 2 Log2 fold change p-value Subcellular location Nucleus 5 Subcellular location id 4 6 Node score Betweenness centrality Log2(Betweenness centrality) Clustering result.fig; Pathway score.fig Clustering result.fig is the structural heatmap generated from hierarchical clustering. Pathway score.fig is the score the pathways with the same order as in the Clustering result.fig. 3*. Results from clustering output_soul_sorted_similarity_matrix.xls: Similarity matrix of the samples of the paths sorted by the hierarchical clustering result (the same order as in the Clustering result.fig ).

16 output_soul_clustered_paths_label.xls: Labels of the samples of pathways generated from hierarchical clustering. output_soul_score_sm_sorted.xls: Pathway scores in the same order as in Clustering result.fig. 4*. Sorted samples of pathways from the hierarchical clustering result output_soul_gist_symb_sorted.xls; output_soul_gist_slist_sorted.xls; output_soul_wlist_sorted.xls; output_soul_gene_symb.xls; *: Intermediate output files.

Examples Running GIST With input files in the folder./inputs, run Main_GIST_func(8,10000, 200,1,'weight_parameters.txt'), and the program will show the running status in Matlab command window: Fig.

17 Examples Running GIST With input files in the folder./inputs, run Main_GIST_func(8,10000, 200,1,'weight_parameters.txt'), and the program will show the running status in Matlab command window: Fig. 1 Running window of GIST After GIST algorithm completes, five output files (output_gist_results.mat, output_gist_network.xls, output_gist_node_attr.xls, output_gist_selectedpathscore.xls, output_gist_selectedpathsymb.xls) will be generated and saved in the folder./outputs.

18 Running SOUL Run main_soul_func(1) in Matlab, and the current status of the program will be displayed in command line window: Fig. 2 Running window of SOUL After SOUL program completes, the major results will be saved in the two files: ouput_soul.networ.xls and output_soul_node_attr.xls. In addition, the algorithm will also generate figures of pathway landscape: structural heatmap and re-organized potential distribution (Fig. 3).

19 Structural heatmap Fig. 3 Pathway landscape Clusters of pathways can be selected considering the figures in Fig. 3 to reestimate the pathway landscape, since the automatic selection only considers the similarity of the samples.

20 Re-running SOUL Select three clusters from the two figures, and modify the cluster_id.xls. For example, three clusters of samples are selected as shown in Fig.4. The corresponding index of the samples are [9:22 66: :179]. Then, run SOUL_part2() to estimate pathway landscape from the selected clusters of samples. Structural heatmap Fig. 4 Selected clusters of samples

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