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2021
Houtven, Joris Van; Cuypers, Bart; Meysman, Pieter; Hooyberghs, Jef; Laukens, Kris; Valkenborg, Dirk
Constrained Standardization of Count Data from Massive Parallel Sequencing Journal Article
In: Journal of Molecular Biology, pp. 166966, 2021, ISSN: 0022-2836.
@article{VANHOUTVEN2021166966,
title = {Constrained Standardization of Count Data from Massive Parallel Sequencing},
author = {Joris Van Houtven and Bart Cuypers and Pieter Meysman and Jef Hooyberghs and Kris Laukens and Dirk Valkenborg},
url = {https://www.sciencedirect.com/science/article/pii/S0022283621001674},
doi = {https://doi.org/10.1016/j.jmb.2021.166966},
issn = {0022-2836},
year = {2021},
date = {2021-01-01},
journal = {Journal of Molecular Biology},
pages = {166966},
abstract = {In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets at well over 1 million count records per second whilst mitigating unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd’s simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets at well over 1 million count records per second whilst mitigating unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd’s simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.