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Amey Pasarkar

dblp:394/4418 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › single-cell analysis › cell lineage analysis
cell lineage inference
0.812024
Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data · Bioinform. 2024
Bioinformatics and computational biology
phylogenetics
0.812024
Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data · Bioinform. 2024
Bioinformatics and computational biology › single-cell analysis
single-cell sequencing
0.812024
Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data · Bioinform. 2024
Bioinformatics and computational biology
cancer genomics
0.212024
Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

nested perfect phylogeny mixture · 0.8mixed integer linear programming · 0.8combinatorial characterization · 0.8
YearPublicationVenuePosition
2024 Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data
abstract
MOTIVATION: Eukaryotic cells contain organelles called mitochondria that have their own genome. Most cells contain thousands of mitochondria which replicate, even in nondividing cells, by means of a relatively error-prone process resulting in somatic mutations in their genome. Because of the higher mutation rate compared to the nuclear genome, mitochondrial mutations have been used to track cellular lineage, particularly using single-cell sequencing that measures mitochondrial mutations in individual cells. However, existing methods to infer the cell lineage tree from mitochondrial mutations do not model "heteroplasmy," which is the presence of multiple mitochondrial clones with distinct sets of mutations in an individual cell. Single-cell sequencing data thus provide a mixture of the mitochondrial clones in individual cells, with the ancestral relationships between these clones described by a mitochondrial clone tree. While deconvolution of somatic mutations from a mixture of evolutionarily related genomes has been extensively studied in the context of bulk sequencing of cancer tumor samples, the problem of mitochondrial deconvolution has the additional constraint that the mitochondrial clone tree must be concordant with the cell lineage tree. RESULTS: We formalize the problem of inferring a concordant pair of a mitochondrial clone tree and a cell lineage tree from single-cell sequencing data as the Nested Perfect Phylogeny Mixture (NPPM) problem. We derive a combinatorial characterization of the solutions to the NPPM problem, and formulate an algorithm, MERLIN, to solve this problem exactly using a mixed integer linear program. We show on simulated data that MERLIN outperforms existing methods that do not model mitochondrial heteroplasmy nor the concordance between the mitochondrial clone tree and the cell lineage tree. We use MERLIN to analyze single-cell whole-genome sequencing data of 5220 cells of a gastric cancer cell line and show that MERLIN infers a more biologically plausible cell lineage tree and mitochondrial clone tree compared to existing methods. AVAILABILITY AND IMPLEMENTATION: https://github.com/raphael-group/MERLIN.
Palash Sashittal, Viola Chen, Amey Pasarkar, Benjamin J. Raphael
Bioinform.3