NDLI: Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study

Content Provider	Springer Nature : BioMed Central
Author	Gardeux, Vincent Arslan, Ahmet D Achour, Ikbel Ho, Tsui-Ting Beck, William T Lussier, Yves A
Abstract	Background Genome-wide transcriptome profiling generated by microarray and RNA-Seq often provides deregulated genes or pathways applicable only to larger cohort. On the other hand, individualized interpretation of transcriptomes is increasely pursued to improve diagnosis, prognosis, and patient treatment processes. Yet, robust and accurate methods based on a single paired-sample remain an unmet challenge. Methods \"N-of-1-pathways\" translates gene expression data profiles into mechanism-level profiles on single pairs of samples (one p-value per geneset). It relies on three principles: i) statistical universe is a single paired sample, which serves as its own control; ii) statistics can be derived from multiple gene expression measures that share common biological mechanisms assimilated to genesets; iii) semantic similarity metric takes into account inter-mechanisms' relationships to better assess commonality and differences, within and cross study-samples (e.g. patients, cell-lines, tissues, etc.), which helps the interpretation of the underpinning biology. Results In the context of underpowered experiments, N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within-and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets (Odds-Ratios≥13, p-values≤1 × 10−5), such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples. Conclusions N-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods. Further, it identifies unique sample/ patient mechanisms, a requirement for precision medicine.
Related Links	https://bmcmedgenomics.biomedcentral.com/counter/pdf/10.1186/1755-8794-7-S1-S1.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	17558794
DOI	10.1186/1755-8794-7-S1-S1
Journal	BMC Medical Genomics
Issue Number	1
Volume Number	7
Language	English
Publisher	BioMed Central
Publisher Date	2014-05-08
Access Restriction	Open
Subject Keyword	Human Genetics Microarrays Gene Expression Differentially Express Gene Ovarian Cancer Cell Line Neuronal Cell Line Short Tandem Repeat Profile Ovarian Cancer Dataset
Content Type	Text
Resource Type	Article
Subject	Genetics (clinical) Genetics
Journal Impact Factor	2.1/2023
5-Year Journal Impact Factor	2.5/2023

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