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Quality Control01:05

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Systematic Quality Control Analysis of LINCS Data.

L Cheng1,2, L Li1,2

  • 1Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.

CPT: Pharmacometrics & Systems Pharmacology
|November 1, 2016
PubMed
Summary
This summary is machine-generated.

The L1000 platform reliably measures cell transcriptomes before perturbations, showing 80% correlation with other platforms. However, its response to perturbations requires careful interpretation, necessitating quality control for reliable biological insights.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • The Library of Integrated Cellular Signatures (LINCS) project aims to profile human cell line transcriptomes.
  • The L1000 platform infers 14,292 gene transcript levels from 978 landmark genes.

Purpose of the Study:

  • To perform quality control analysis of the L1000 platform data.
  • To assess the reliability of the L1000 platform before and after chemical and genetic perturbations.

Main Methods:

  • Utilized MCF7, PC3, and A375 cell lines for quality control analysis.
  • Compared L1000 transcriptome data with Affymetrix HU133A platform data.
  • Analyzed differential gene expression overlap after shRNA perturbations.

Main Results:

  • Observed an 80% transcriptome correlation between L1000 and Affymetrix platforms before perturbations.
  • Found a moderate 30% overlap in differentially expressed genes after shRNA perturbations.
  • Identified MAPK, VEGF, and TCR pathways as significantly shared between chemical and genetic perturbations.

Conclusions:

  • The L1000 platform is reliable for transcriptome assessment prior to perturbations.
  • Interpretation of L1000 data post-perturbation requires caution.
  • A quality control pipeline for L1000 data is recommended before biological analysis.