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Batch Effect Removal via Batch-Free Encoding

https://doi.org/10.1101/380816
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.cell.2015.05.002openalex-snapshot
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
inferredcites →https://doi.org/10.1016/j.cell.2016.04.019openalex-snapshot
Mass Cytometry: Single Cells, Many Features
inferredcites →https://doi.org/10.1038/nrg2825openalex-snapshot
Tackling the widespread and critical impact of batch effects in high-throughput data
inferredcites →https://doi.org/10.1093/bioinformatics/btx196openalex-snapshot
Removal of batch effects using distribution-matching residual networks
inferredcites →https://doi.org/10.1093/bioinformatics/btx448openalex-snapshot
Gating mass cytometry data by deep learning
inferredcites →https://doi.org/10.1093/bioinformatics/bty190openalex-snapshot
Generalizing biomedical relation classification with neural adversarial domain adaptation
inferredcites →https://doi.org/10.1101/237065openalex-snapshot
Exploring Single-Cell Data with Deep Multitasking Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-snapshot
Auto-Encoding Variational Bayes
inferredcites →https://doi.org/10.48550/arxiv.1704.00028openalex-snapshot
Improved Training of Wasserstein GANs
inferredcites →https://doi.org/10.5555/2188385.2188410openalex-snapshot
A kernel two-sample test

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cyto.a.24709openalex-snapshot
In‐silico generation of high‐dimensional immune response data in patients using a deep neural network
inferred← citeshttps://doi.org/10.1016/j.biopha.2021.112558openalex-snapshot
Immunotherapy of cancer in single-cell RNA sequencing era: A precision medicine perspective
inferred← citeshttps://doi.org/10.1016/j.biopha.2023.115077openalex-snapshot
Deep learning applications in single-cell genomics and transcriptomics data analysis
inferred← citeshttps://doi.org/10.1038/s41467-022-29383-5openalex-snapshot
cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies
inferred← citeshttps://doi.org/10.1093/bib/bbaf667openalex-snapshot
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa796openalex-snapshot
Adversarial deconfounding autoencoder for learning robust gene expression embeddings
inferred← citeshttps://doi.org/10.1101/2021.11.26.470166openalex-snapshot
Deep Learning Applications in Single-Cell Omics Data Analysis
inferred← citeshttps://doi.org/10.1101/2024.08.11.607498openalex-snapshot
Adversarial learning enables unbiased organism-wide cross-species alignment of single-cell RNA data at scale
inferred← citeshttps://doi.org/10.1145/3845984openalex-snapshot
A Survey on the Evolution and Future Trajectory of Flow Cytometry Analysis
inferred← citeshttps://doi.org/10.1186/s12967-025-07031-yopenalex-snapshot
Generative and integrative modeling for transcriptomics with formalin fixed paraffin embedded material
inferred← citeshttps://doi.org/10.48550/arxiv.1812.03188openalex-snapshot
METCC: METric learning for Confounder Control Making distance matter in high dimensional biological analysis
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/1openalex-snapshot
Peer Review #1 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/2openalex-snapshot
Peer Review #2 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/3openalex-snapshot
Peer Review #3 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.2/reviews/1openalex-snapshot
Peer Review #1 of "Correcting nuisance variation using Wasserstein distance (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.2/reviews/3openalex-snapshot
Peer Review #3 of "Correcting nuisance variation using Wasserstein distance (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj.8594openalex-snapshot
Correcting nuisance variation using Wasserstein distance
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.