Awesome
scGEAToolbox - a Matlab toolbox for single-cell RNA-seq data analyses
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Quick Installation
Run the following code in MATLAB:
unzip('https://github.com/jamesjcai/scGEAToolbox/archive/main.zip');
addpath('./scGEAToolbox-main');
Getting Started Quickly Using the SCGEATOOL App
Run the following code in MATLAB to start:
scgeatool
Read Documentation
<!--- ## To get started immediately, check out demo scripts: * [Demo script 1](http://htmlpreview.github.io/?https://github.com/jamesjcai/scGEAToolbox/blob/main/demo_script1.html) Filter, Normalization and Batch Correction of Data * [Demo script 2](http://htmlpreview.github.io/?https://github.com/jamesjcai/scGEAToolbox/blob/main/demo_script2.html) Feature Selection * [Demo script 3](http://htmlpreview.github.io/?https://github.com/jamesjcai/scGEAToolbox/blob/main/demo_script3.html) Visualization * [Demo script 4](http://htmlpreview.github.io/?https://github.com/jamesjcai/scGEAToolbox/blob/main/demo_script4.html) Clustering * [Demo script 5](http://htmlpreview.github.io/?https://github.com/jamesjcai/scGEAToolbox/blob/main/demo_script5.html) Pseudotime Analysis and Gene Network * [Demo script 6](http://htmlpreview.github.io/?https://github.com/jamesjcai/scGEAToolbox/blob/main/demo_script6.html) DE Analysis and Marker Gene Identification ## GUI interface After installing the toolbox, the main GUI can be run by calling `scGEApp`.  **Fig. 1. Screenshots of an execution of scGEApp -- the app interface of scGEAToolbox.** (a) Two example panels of the main GUI scGEApp; (b) A 3-D scatter plot showing genes whose position is determined by expression mean, CV and dropout rate; (c) A stem plot showing expression level of 50 selected genes across 2,000 cells: 1,000 in one state (blue) and the other 1,000 in the other state (red). ## Analytical workflow built with scGEAToolbox  **Fig. 2. A software workflow built with scGEAToolbox for single-cell gene regulatory network (scGRN) analyses.** High-dimensional scRNA-seq data is filtered, normalized, and used as input for two paths. The first is a combination of (A) dimension-ality reduction and (B) trajectory/psedotime analysis to provide pseudotime-series data. The second is using network inference algorithms to generate (C) a global, coarse GRN structure. The integration of results from the two paths produces (D) pseudotime-series scGRNs, which can be further analyzed through regulatory modeling using parameter estimation algorithms to infer (E) a refined dynamic scGRN. ## Interactive cell type annotation with scGEAToolbox (sc_celltype explorer) [](https://youtu.be/HRQiXX3Jwpg) -->SEE ALSO - Standalone Application SCGEATOOL :: Single-Cell Gene Expression Analysis Tool
SCGEATOOL.exe is a standalone application running on Windows machines that do not have MATLAB installed. SCGEATOOL is a lightweight and blazing fast desktop application that provides interactive visualization functionality to analyze single-cell transcriptomic data. SCGEATOOL allows you to easily interrogate different views of your scRNA-seq data to quickly gain insights into the underlying biology. SCGEATOOL is a pre-compiled standalone application developed in MATLAB. Pre-compiled standalone releases are meant for those environments without access to MATLAB licenses. Standalone releases provide access to all of the functionality of the SCGEATOOL standard MATLAB release encapsulated in a single application. SCGEATOOL is open-sourced to allow you to experience the added flexibility and speed of the MATLAB environment when needed.
Help
If you have any questions or require assistance using scGEAToolbox, please contact me.
Citation
Cai JJ. scGEAToolbox: a Matlab toolbox for single-cell RNA sequencing data analysis. Bioinformatics. 2019;btz830. doi:10.1093/bioinformatics/btz830