Biology subjects

Chockalingam, S. P.

Publications and source records attributed to Chockalingam, S. P..

2 recordsLinked to original sources

bioRxiv2024

SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data

MotivationIntegrative analysis of large-scale single cell data collected from diverse cell populations promises an improved understanding of complex biological systems. While several algorithms have been developed for single cell RNA-sequencing data integration, many lack scalability to handle large numbers of datasets and/or millions of cells due to their memory and run time requirements. The few tools which can handle large data do so by reducing the computational burden through strategies such as subsampling of the data or selecting a reference dataset, to improve computational efficiency and scalability. Such shortcuts however hamper accuracy of downstream analyses, especially those requiring quantitative gene expression information. ResultsWe present SCEMENT, a SCalablE and Memory-Efficient iNTegration method to overcome these limitations. Our new parallel algorithm builds upon and extends the linear regression model previously applied in ComBat, to an unsupervised sparse matrix setting to enable accurate integration of diverse and large collections of single cell RNA-sequencing data. Using tens to hundreds of real single cell RNA-seq datasets, we show that SCEMENT outperforms ComBat as well as FastIntegration and Scanorama in runtime (upto 214X faster) and memory usage (upto 17.5X less). It not only performs batch correction and integration of millions of cells in under 25 minutes, but also facilitates discovery of new rare cell-types and more robust reconstruction of gene regulatory networks with full quantitative gene expression information. Availability and implementationSource code freely available for download at https://github.com/AluruLab/scement, implemented in C++ and supported on Linux. Contactaluru@cc.gatech.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

MCPNet : A parallel maximum capacity-based genome-scale gene network construction framework

MotivationGene regulatory network (GRN) reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective GRN reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes. ResultsWe developed MCP Score, a novel maximum-capacity-path based metric to quantify the relative strengths of direct and indirect gene-gene interactions. We further present MCPNet, an efficient, parallelized GRN reconstruction software based on MCP Score, to reconstruct networks in unsupervised and semi-supervised manners. Using synthetic and real S. cervisiae datasets as well as real A. thaliana datasets, we demonstrate that MCPNet produces better quality networks as measured by AUPR, is significantly faster than all other GRN inference software, and also scales well to tens of thousands of genes and hundreds of CPU cores. Thus, MCPNet represents a new GRN inferencing tool that simultaneously achieves quality, performance, and scalability requirements. AvailabilitySource code freely available for download at https://doi.org/10.5281/zenodo.6499748 and https://github.com/AluruLab/MCPNet, implemented in C++ and supported on Linux. Contactaluru@cc.gatech.edu Supplementary informationSupplementary data are available at Bioinformatics online.

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