BioinformaticsBioinformaticsPeer-reviewed journal article
CAMML with the Integration of Marker Proteins (ChIMP)
Abstract
MotivationCell typing is a critical task in the analysis of single cell data, particularly when studying diseased tissues that contain a complex mixture of normal tissue and infiltrating immune cells. Unfortunately, the sparsity and noise of single cell data make accurate cell typing at the level of individual cells extremely difficult. To address these challenges, we previously developed the CAMML method for multi-label cell typing of single cell RNA-sequencing (scRNA-seq) data. CAMML uses weighted gene sets to score each profiled cell for multiple potential cell types. While CAMML outperforms other scRNA-seq cell typing techniques, it only leverages transcriptomic data so cannot take advantage of newer multi-omic single cell assays that jointly profile gene expression and protein abundance (e.g., joint scRNA-seq/CITE-seq). ResultWe developed the ChIMP (CAMML with the Integration of Marker Proteins) method to support multi-label cell typing of individual cells jointly profiled via scRNA-seq and CITE-seq. ChIMP combines cell type scores computed on scRNA-seq data via the CAMML approach with discretized CITE-seq measurements for cell type marker proteins. The multi-omic cell type scores generated by ChIMP allow researchers to more precisely and conservatively cell type joint scRNA-seq/CITE-seq data.
- Citations
- 4
- FWCI
- 0.33
- Field percentile
- 47th percentile
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues
Daniel P. Caron, William L. Specht, … Peter A. Sims·Cell Reports MethodsPeer-reviewed·Jan 1, 2025
- SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics
Xinjun Wang, Zhongli Xu, … Wei Chen·PNAS NexusPeer-reviewed·Aug 19, 2022
- Optimal gene selection for cell type discrimination in single cell analyses
Bianca Dumitrascu, Soledad Villar, … Barbara Elizabeth Engelhardt·Nature CommunicationsPeer-reviewed·Feb 19, 2021
- Tutorial: guidelines for manual cell type annotation of single-cell multi-omics datasets using interactive software
Yang Joon Kim, Alexander J. Tarashansky, … Alejandro A. Granados·bioRxiv·Jul 11, 2023
- Clustering single cell CITE-seq data with a canonical correlation based deep learning method
Musu Yuan, Liang Chen, Minghua Deng·bioRxiv·Sep 7, 2021
- multiHIVE: Hierarchical Multimodal Deep Generative Model for Single-cell Multiomics Integration
Anirudh Nanduri, Musale Krushna Pavan, … Hamim Zafar·bioRxiv·Jan 28, 2025