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ReactomeFIViz app 6 needs Cytoscape 3.7.0 or above. If you have not installed Cytoscape 3.7.0 or above, please download it from Cytoscape's web site: After launching Cytoscape, use menu "Apps/App Manager" to open the "App Manager" dialog, and search for "ReactomeFI". You should see the ReactomeFIViz app listed in the middle panel (See the Figure below. You may see a different version number. Note: The listed name of this app is "ReactomeFIPlugIn", which is the original name of the app.). Choose the app, and then click the "Install" button at the bottom of the dialog. Follow the procedures to finish the installation.

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We adapted the PARADIGM approach for Reactome pathways by converting reactions drawn in pathway diagrams into factors in factor graphs, a type of probabilistic graphical models (PGMs). For details about the PARADIGM approach, see: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. For introduction to factor graphs, see this wikipedia entry: Factor Graph. For test purposes, you can download two sample data files for 100 TCGA ovarian cancer patients: CNVs and mRNA gene expression. The original TCGA OV files were downloaded from the Broad GDAC Firehose web site.

An array data file should be a tab-delimited text file with table headers. The first column should be gene names. All other columns should be expression values in different samples. The data set in the file should be pre-normalized. For example, see this gene expression file for breast cancer: NejmLogRatioNormGlobalZScore_070111.txt.zip. This data set was download from van de Vijver et al in 2002, and has been normalized.

ReactomeFIViz implements a suite of features for users to conduct scRNA-seq data analysis and visualization. To do this, we have packaged several popuplar Python packages developed for scRNA-seq data analysis and visualization together into a Python standalone application. These packages include scanpy for routine scRNA-seq data analysis and visualization and scVelo for RNA velocity based data analysis and visualization. Note: For scRNA-seq data analysis and visualization, you need to have Python 3.7 installed at your computer. If you have not installed Python at your computer, you can do so by downloading an installer from for your computer. We have tested Python 3.7 only and thefore suggest that you use 3.7 for these features. However, you don't need to install our standalone Python application indepedently from ReactomeFIViz. When needed, ReactomeFIViz will automatically download and update the application for you as long as you point to the correct Python application path (i.e. directory and application file).

Survival analysis is based on a server-side R script to do either coxph or Kaplan-Meier survival analysis. To do survival analysis, a tab-delimited text file containing at least three columns should be provided. The names of three columns should be: Samples, OSDURATION, and OSEVENT. For example, see this survival information file downloaded from van de Vijver et al in 2002: Nejm_Clin_Simple.txt, which has been simplified for our analysis purpose. To do survival analysis, use the popup menu "Analyze Module Functions/Survival Analysis..." (see below) 350c69d7ab

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