RICK (RNA Interactive Computing Kit) is a tool developed by the Bioinformatics Core at the Salk Institute.
RICK accepts as input a file with raw read counts for each transcript and sample.
Gene annotation is performed using the R package AnnotationDbi (Pagès H, 2017) and raw gene counts are normalized using the DESeq2 package (Love, Huber, & Anders, 2014) and transformed with the regularized-logarithm transformation (rlogTransformation in DESeq2) for visualization.
Hierarchical clustering is carried out based on the similarity of the top 1000 most highly expressed genes using the R hclust function. Principal Component Analysis (PCA) is an alternate approach for visualizing sample similarity. Top 500 genes with the highest variance from two groups (ex. Treatment and WT) are plotted according to the top two (2D plot) and three (3D plot) principal components.
To find significantly differentially expressed genes, two popular methods for differential expression analysis are provided: edgeR (McCarthy, Chen, & Smyth, 2012) and DESeq2 (Love et al., 2014) which estimate biological variability using replicate information and test for significant changes in gene expression between two groups of samples that are defined by the user. In addition, the edgeR and DESeq2 methods can be combined to identify genes that are differentially expressed using either the minimum, maximum or average adjusted p-value from edgeR and DESeq2, enabling permissive, conservative, or balanced identification of significantly different genes. The up and down-regulated genes can be downloaded as a table with adj. p-values and fold-changes.
Gene set analysis (GSA) compares the pre-defined gene set expression changes to the background set to identify pathways that are being perturbed. GSA does not require a user-defined hard cutoff for the expression significance making it more sensitive to pathway-wide coordinated expression changes. To check which KEGG (Kanehisa & Goto, 2000) or Gene Ontology (Ashburner et al., 2000) terms are undergoing expression changes in both directions, the GAGE R package (Luo, Friedman, Shedden, Hankenson, & Woolf, 2009) is used as it shows significantly better results when compared to two common GSA methods. To graphically visualize which genes within overrepresented KEGG pathways are changing, RICK uses the Pathview R package (Luo & Brouwer, 2013).
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It's fine, everythings is fine. There is an infinite number of realities Morty, and in a few dozens of those I got lucky and turned everything back to normal. -Rick