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Xianjie Huang

dblp:309/3842 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2026
—ORCID · conflict

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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
genomics
0.512021
Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021
Bioinformatics and computational biology › genomics
genotyping
0.512021
Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021
Bioinformatics and computational biology › single-cell analysis
single-cell sequencing
0.512021
Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021
Bioinformatics and computational biology › genomics
variant calling
0.112021
Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021

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

HTSlib · 0.5
YearPublicationVenuePosition
2026 SDGraphMeta: A novel similarity-driven framework for graph meta-learning
Xianjie Huang, Jian Li 0032, Guanjun Liu
Pattern Recognit. Lett.1
2021 Cellsnp-lite: an efficient tool for genotyping single cells
abstract
SUMMARY: Single-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here, we introduce a software, cellsnp-lite, implemented in C/C++ and based on well-supported package htslib, for genotyping in single-cell sequencing data for both droplet and well-based platforms. On various experimental datasets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite, therefore, lightens the genetic analysis for increasingly large single-cell data. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/single-cell-genetics/cellsnp-lite. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Xianjie Huang, Yuanhua Huang
Bioinform.1