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

Vincent Gardeux, Ahmet D. Arslan, Ikbel Achour, Tsui Ting Ho, William T. Beck, Yves A Lussier

Research output: Contribution to journalArticle

15 Citations (Scopus)

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.

Original languageEnglish (US)
Article numberS1
JournalBMC Medical Genomics
Volume7
Issue numberSUPPL.1
DOIs
StatePublished - May 8 2014

Fingerprint

Transcriptome
RNA Splicing
Precision Medicine
Gene Expression Profiling
Semantics
Genes
Cell Cycle
Genome
RNA
Gene Expression
Cell Line
Datasets
Therapeutics

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics
  • Medicine(all)

Cite this

Concordance of deregulated mechanisms unveiled in underpowered experiments : PTBP1 knockdown case study. / Gardeux, Vincent; Arslan, Ahmet D.; Achour, Ikbel; Ho, Tsui Ting; Beck, William T.; Lussier, Yves A.

In: BMC Medical Genomics, Vol. 7, No. SUPPL.1, S1, 08.05.2014.

Research output: Contribution to journalArticle

Gardeux, Vincent ; Arslan, Ahmet D. ; Achour, Ikbel ; Ho, Tsui Ting ; Beck, William T. ; Lussier, Yves A. / Concordance of deregulated mechanisms unveiled in underpowered experiments : PTBP1 knockdown case study. In: BMC Medical Genomics. 2014 ; Vol. 7, No. SUPPL.1.
@article{6c9cf693711c4451b1fed0090be89846,
title = "Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study",
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.",
author = "Vincent Gardeux and Arslan, {Ahmet D.} and Ikbel Achour and Ho, {Tsui Ting} and Beck, {William T.} and Lussier, {Yves A}",
year = "2014",
month = "5",
day = "8",
doi = "10.1186/1755-8794-7-S1-S1",
language = "English (US)",
volume = "7",
journal = "BMC Medical Genomics",
issn = "1755-8794",
publisher = "BioMed Central",
number = "SUPPL.1",

}

TY - JOUR

T1 - Concordance of deregulated mechanisms unveiled in underpowered experiments

T2 - PTBP1 knockdown case study

AU - Gardeux, Vincent

AU - Arslan, Ahmet D.

AU - Achour, Ikbel

AU - Ho, Tsui Ting

AU - Beck, William T.

AU - Lussier, Yves A

PY - 2014/5/8

Y1 - 2014/5/8

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84900404206&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84900404206&partnerID=8YFLogxK

U2 - 10.1186/1755-8794-7-S1-S1

DO - 10.1186/1755-8794-7-S1-S1

M3 - Article

C2 - 25079003

AN - SCOPUS:84900404206

VL - 7

JO - BMC Medical Genomics

JF - BMC Medical Genomics

SN - 1755-8794

IS - SUPPL.1

M1 - S1

ER -