A list by @biology · 4 papers · 0 followers
Min Dai, Xiaobing Pei, Xiu‐Jie Wang
COSG identifies marker genes for cell type distinction in single-cell and spatial transcriptome data with high accuracy and efficiency.
Min Dai, Tivadar Torok, Dawei Sun…·Fei Chen
LARIS identifies cell type-specific and spatially restricted ligand-receptor interactions in spatial transcriptomics data with high accuracy and scalability.
Lei Shi, Min Dai, Yongbo Zhang…·Xiu‐Jie Wang
CellClick is an interactive platform that improves cell type annotation accuracy in single-cell and spatial omics data through automated and user-guided tools.
William N. Colgan, Eric Weine, Luke W. Koblan…·Nir Yosef
Pycea provides a toolkit for analyzing single-cell lineage tracing data, integrating with Scanpy and offering visualization and statistical methods for lineage trees.
Papers closest to the centre of this list, not already in it.
Maria Brbić, Kaidi Cao, John W. Hickey…·Jure Leskovec
STELLAR annotates spatially resolved single-cell data using geometric deep learning, discovering cell types and states while transferring annotations across tissues and donors.
Sheng Wang, Angela Oliveira Pisco, Aaron McGeever…·Russ Biagio Altman
OnClass classifies single-cell data into Cell Ontology terms and identifies marker genes for all categories, even those absent in training data.
Ivan Ferrari, Mattia Battistella, Francesca Vincenti…·Eugenia Galeota
CIA provides a reference-free method for cell type annotation in scRNA-seq data, outperforming existing approaches with lower computational cost.
SingleCellNet classifies single cell RNA-Seq data across platforms and species by comparing query data to reference data, showing improved accuracy and cross-species performance.
Suoqin Jin, Maksim V. Plikus, Qing Hua Nie
The paper introduces CellChat v2, a tool for analyzing cell-cell communication from single-cell and spatial transcriptomics data, including spatial proximity analysis and expanded ligand-receptor databases.
Yingxin Lin, Yue Cao, Hani Jieun Kim…·Jean Yee Hwa Yang
scClassify improves cell type classification in scRNA-seq data by using ensemble learning, achieving more accurate and fine-grained results than original publications.
Yang Xu, Simon J. Baumgart, Christian M. Stegmann, Sikander Hayat
MACA is a tool for automatic cell-type annotation in single-cell transcriptomics datasets, showing improved accuracy and speed compared to existing methods.
Corentin Thuilliez, Gaël Moquin‐Beaudry, Pierre Khneisser…·Antonin Marchais
CellsFromSpace is a reference-free tool for deconvolving and annotating spatial transcriptomic data, outperforming existing methods in accuracy and speed across multiple technologies.