ArticleBioinformatics (Oxford, England)2021
Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.Cancer research · 2026Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- Variational autoencoder enhanced analysis of energy metabolism and autophagy in exercising cardiomyocytes.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data.BMC genomics · 2024Article
- Inferring single-cell copy number profiles through cross-cell segmentation of read counts.BMC genomics · 2024Article
- Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.Briefings in bioinformatics · 2023Article
- Similarity-assisted variational autoencoder for nonlinear dimension reduction with application to single-cell RNA sequencing data.BMC bioinformatics · 2023Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- An empirical Bayes method for differential expression analysis of single cells with deep generative models.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
- bmVAE: a variational autoencoder method for clustering single-cell mutation data.Bioinformatics (Oxford, England) · 2023Article
- Bibliometric and visual analysis of single-cell sequencing from 2010 to 2022.Frontiers in genetics · 2023Article
- Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.Briefings in bioinformatics · 2022Article
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
- Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches.Evolutionary bioinformatics online · 2022Article
- Latent representation learning in biology and translational medicine.Patterns (New York, N.Y.) · 2021Review
- Enhancing scientific discoveries in molecular biology with deep generative models.Molecular systems biology · 2020Review
- Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.Genome biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
motivationIntra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data.
resultsHere we describe 'Dhaka', a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and six single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data. AVAILABILITY AND IMPLEMENTATION: All the datasets used in the paper are publicly available and developed software package and supporting info is available on Github https://github.com/MicrosoftGenomics/Dhaka. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Identifiers
What OpenQuestion holds
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.