NXG Logic ChipST2C: Analysis Package for RNA-Seq and and Biochip Data
ChipST2C (Chip Statistical Testing to Clustering) is a software package for RNA-Seq, DNA microarray, and biochip data analysis. Capabilities of ChipST2C include 2- and k-sample parametric and non-parametric hypothesis testing, automatic hierarchical cluster analysis of statistically differentially significant genes, heat maps, k-means cluster analysis, principal components analysis (PCA), within-gene and between randomization tests, and various approaches for the multiple testing problem (Bonferroni, false discovery rate, and Storey q-values). In addition, K-means cluster analysis can be performed on significant genes for 2- and k-sample tests in order to drill down further into co-regulatory expression patterns.
Benefits
ChipST2C rapidly analyzes RNA-Seq, DNA microarray, or biochip data by first identifying significant genes, and then displaying the significant genes in their relevant sample-based and group-based clusters. Benefits for using ChipST2C include:
- Run k-sample ANOVA and Kruskal-Wallis tests.
- Run 2-sample t- and Mann-Whitney tests.
- Automatically generate cluster heat maps (and dendograms) for genes significantly differentially expressed between all possible class comparisons.
- Perform K-means cluster analysis and principal components analysis (PCA) of expression results.
- Perform within-gene and between-gene randomization tests.
- Determine Bonferroni, false discovery rate (FDR) and Storey q-values for the multiple testing problem.
Screenshots
Features
- Summarize: Perform summary statistics on samples including mean and standard deviation.
- Transformation: Mean-zero standardization (z-score), log (base 2 and 10)
- Independence: Equality of means tests for 2- and k-group parametric and non-parametric t-tests, Mann-Whitney, ANOVA, and Kruskal-Wallis.
- Class discovery: Correlation-based principal components analysis (PCA) and hierarchical cluster analysis (HCA).
- IPA uploads: Generates individual spreadsheets with names and either mean difference in expression or fold change based on GM for top N or significant genes.
System Requirements
Windows 7, 8, 10 (16GB RAM)