Numerous gene expression datasets from diverse tissue samples from the plant variety Arabidopsis thaliana have been already deposited in the public domain. There have been several attempts to do large scale meta‐analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta‐analysis of the publicly available Arabidopsis datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated. We pointed out serious issues in the data normalization steps widely accepted and published recently in this context. We put together a web resource where gene expression relationships can be explored online which helps visualize the logical relationships between genes. We believe that this website will be useful in identifying important genes in different biological context. The web link is http://hegemon.ucsd.edu/plant/ .
Retinoblastoma (RB) is a good model to study drug resistance to cell-cycle inhibitors because it is driven by mutations in the core components of cell-cycle, i.e, Rb gene. However, there is limited gene expression dataset in RB which has major reproducibility issues. We have developed invariant differential expression analysis (iDEA) that improves the state of the art in differential expression analysis (DEA). iDEA uses strong Boolean implication relationships in a large diverse human dataset GSE119087 (n = 25,955) to filter the noisy differentially expressed genes (DEGs). iDEA was applied to RB datasets and a gene signature was computed that led to prediction and mechanism of drug sensitivity. The prediction was confirmed using drugs-sensitive/resistant RB cell-lines and mouse xenograft models using CDC25 inhibitor NSC663284. iDEA improved reproducibility of differential expression across diverse retina/RB cohorts and RB cell-lines with different drug sensitivity (Y79/Weri vs NCC). Pathway analysis revealed WNT/β-catenin involved in distinguishing drug sensitivity to CDC25 inhibitor NSC663284. NSC663284 inhibited tumour cell proliferation in mouse xenograft model containing Y79 cells indicating novel therapeutic option in RB. Invariant differentially expressed genes (iDEGs) are robustly associated with outcome in diverse cancer datasets and supports for a fundamental mechanism of drug resistance.
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