SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data

Source code freely available for download at https://github.com/AluruLab/scement, implemented in C++ and supported on Linux.

Authors

Sriram P. Chockalingam (ORCID: https://orcid.org/0000-0003-1358-7691)
Srinivas Aluru (ORCID: https://orcid.org/0000-0003-4279-469X)
Maneesha Aluru (ORCID: https://orcid.org/0000-0003-1353-9622)

Institutions

Georgia Institute of Technology (US)

Publication Details

Journal: Bioinformatics
Published: 2025-02-01
DOI: https://doi.org/10.1093/bioinformatics/btaf057
Citations: 1
Primary Topic: Single-cell and spatial transcriptomics
Type: article
Field-Weighted Citation Impact: 0.81

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article

SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data

Sriram P. Chockalingam, Srinivas Aluru, Maneesha Aluru

Feb 1, 2025

1 citations

Bioinformatics

Single-cell and spatial transcriptomics

0.81

article

SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data

Sriram P. Chockalingam, Srinivas Aluru, Maneesha Aluru

February 1, 2025article en

1 citations

Abstract

Source code freely available for download at https://github.com/AluruLab/scement, implemented in C++ and supported on Linux.

BioinformaticsVol. 41(2)

Georgia Institute of Technology (US)

Openalex Percentile: Top 31%

Single-cell and spatial transcriptomics

0.81

DOI Open Access PDF OpenAlex

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SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data — Sriram P. Chockalingam, Srinivas Aluru, et al. · Bioinformatics (2025) | TGRS Research Map | TGRS