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Constrained Standardization of Count Data from Massive Parallel Sequencing.

Joris Van Houtven1, Bart Cuypers2, Pieter Meysman3

  • 1Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium; Universiteit Hasselt, Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan, Diepenbeek BE 3590, Belgium; Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen BE 2020, Belgium.

Journal of Molecular Biology
|April 1, 2021
PubMed
Summary
This summary is machine-generated.

CONSTANd, a normalization method, quickly assesses experimental quality for transcriptomics and proteomics data. It enables joint analysis across diverse experiments, mitigating bias and revealing biological structure efficiently.

Keywords:
RNA-seqmulti-omicsnormalizationproteomicstranscriptomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput omics studies require rapid experimental quality assessment before statistical analysis.
  • Efficient triage methods are crucial for managing large omics datasets.

Purpose of the Study:

  • To evaluate the suitability of the CONSTANd normalization method for transcriptomics count data.
  • To adapt CONSTANd for joint analysis of identically balanced but differently sized experiments.
  • To assess CONSTANd's performance in terms of speed, bias mitigation, and biological structure discovery.

Main Methods:

  • Qualitative comparison of CONSTANd results with DESeq2 for transcriptomics count data.
  • Development of an adjusted CONSTANd method for joint analysis of diverse experimental datasets.
  • Benchmarking CONSTANd's processing speed on large datasets (over 1 million count records/second).

Main Results:

  • CONSTANd effectively processes large transcriptomics datasets at high speed (>1 million records/sec).
  • The method mitigates unwanted systematic bias, aiding in the rapid discovery of biological structure when combined with PCA or hierarchical clustering.
  • An adjusted CONSTANd facilitates joint analysis of datasets from different batches, protocols, and labs, using only sample set delineation.

Conclusions:

  • CONSTANd is a fast and effective normalization method suitable for transcriptomics count data.
  • The adjusted CONSTANd enables robust joint analysis of heterogeneous omics datasets.
  • CONSTANd's versatility across proteomics and transcriptomics positions it for integration into multi-omics workflows.