ArticleNucleic acids research2022
Integration of single cell data by disentangled representation learning.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A single-nucleus multiomic study of preoptic area cells in prepubertal control vs prenatally androgenized female mice.Endocrinology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- Article
- Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network.BMC bioinformatics · 2024Article
- scCross: efficient search for rare subpopulations across multiple single-cell samples.Bioinformatics (Oxford, England) · 2024Article
- Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation.BMC bioinformatics · 2024Article
- stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation.GigaScience · 2024Article
- SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies.Genome biology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent developments of single cell RNA-sequencing technologies lead to the exponential growth of single cell sequencing datasets across different conditions. Combining these datasets helps to better understand cellular identity and function. However, it is challenging to integrate different datasets from different laboratories or technologies due to batch effect, which are interspersed with biological variances. To overcome this problem, we have proposed Single Cell Integration by Disentangled Representation Learning (SCIDRL), a domain adaption-based method, to learn low-dimensional representations invariant to batch effect. This method can efficiently remove batch effect while retaining cell type purity. We applied it to thirteen diverse simulated and real datasets. Benchmark results show that SCIDRL outperforms other methods in most cases and exhibits excellent performances in two common situations: (i) effective integration of batch-shared rare cell types and preservation of batch-specific rare cell types; (ii) reliable integration of datasets with different cell compositions. This demonstrates SCIDRL will offer a valuable tool for researchers to decode the enigma of cell heterogeneity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.