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MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data

Authors: Alexander Gerniers; Orian Bricard; Pierre Dupont;

MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data

Abstract

This repository contains the breast cancer scRNA-seq data used in sections 3.1, 3.3 and 3.4 of the MicroCellClust paper, published in Bioinformatics [1]. The other datasets, used in sections 3.2 and 3.5, are also publicly available (Sequence Read Archive, SRX1723923, and Gene Expression Omnibus, GSE65525). This repository also contains the version (v1.2) of the MicroCellClust solver implementation used to produce the results described in the paper. The latest version of the software is available at https://github.com/agerniers/MicroCellClust Version 2 of MicroCellClust [2], suitable for large-scale single-cell data, is available at https://github.com/agerniers/MicroCellClust References [1] A. Gerniers, O. Bricard and P. Dupont (2021). MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data. Bioinformatics, 37(19), 3220-3227. https://doi.org/10.1093/bioinformatics/btab239 [2] A. Gerniers, P. Dupont (2022). MicroCellClust 2: a hybrid approach for multivariate rare cell mining in large-scale single-cell data. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 148-153.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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