ISCVA (pronounced as Eye-VA, i.e., "SC" is silent), Interactive Single Cell Visual Analytics - a JavaScript-based visual analytics for analyzing scRNA-seq data: http://iscva.moffitt.org. The templates, functions and scripts are available here. The tool ISCVA has been used to analyze scRNA-seq datasets to characterize tumor immune microenvironment (TME) and/or identify novel therapies (e.g., Clinical Cancer Reserach 2021 and Cancer Immunology Research 2021, Neuro-Oncology 2022, Clinical Cancer Research 2022).

SinCHet-MS: A MATLAB toolbox for analyzing single cell mass spectrometry (SCMS) heterogeneity. To be submitted. The pre-compiled SinCHet-MS is available for download here. The User manual could be download here and source files here.

DRepMel, a multiomics drug repurposing approach with a Shiny app, predicts combination therapies for melanoma patients by integrating exome sequencing, RNA-seq data, clinical outcomes and potential functional impact (using eQTL analysis) in two large independent melanoma patient cohorts. This also provides the potential impact on TME using scRNA-seq data from 28K cells from melanoma patients. The user manual could be downloaded here.

GMSImpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis. Bioinformatics. 2020;. doi: 10.1093/bioinformatics/btz488. The R package is available at CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html

SinCHet: a MATLAB GUI toolbox for Single Cell Heterogeneity analysis in cancer. Bioinformatics 2017, 33(18):2951-2953. The SinCHet software is freely available for non-profit academic use. You could download the source code here: SinCHet_source_code.zip or pre-compiled code here: SinCHet Compiled.zip. Installation details are included in the SinCHet user manual here.

NNC (Nonparametric Nonlinear Correlation) - A Matlab toolbox for quantifying nonlinear correlation. Download the software as part of the supplements from our paper, Chen YA, Almeida JS, Richards AJ, Müller P, Carroll RJ, Rohrer B. A nonparametric approach to detect nonlinear correlation in gene expression. Journal of Computational and Graphical Statistics 2010, 19(3):552-568

cancer patient single cell data