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Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification

https://doi.org/10.1101/671404
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What this work says

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

inferredcites →https://doi.org/10.1016/0893-6080(88)90003-2openalex-snapshot
Increased rates of convergence through learning rate adaptation
inferredcites →https://doi.org/10.1016/0893-6080(88)90326-7openalex-snapshot
Neural network as generalized transversal filters
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.2016.07.054openalex-snapshot
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
inferredcites →https://doi.org/10.1016/j.cell.2019.05.031openalex-snapshot
Comprehensive Integration of Single-Cell Data
inferredcites →https://doi.org/10.1016/j.celrep.2017.03.004openalex-snapshot
Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity
inferredcites →https://doi.org/10.1016/j.cels.2016.08.011openalex-snapshot
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
inferredcites →https://doi.org/10.1016/j.cmet.2016.08.018openalex-snapshot
RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes
inferredcites →https://doi.org/10.1016/j.neucom.2014.12.095openalex-snapshot
Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure
inferredcites →https://doi.org/10.1038/550451aopenalex-snapshot
The Human Cell Atlas: from vision to reality
inferredcites →https://doi.org/10.1038/nbt.4091openalex-snapshot
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
inferredcites →https://doi.org/10.1038/nbt.4096openalex-snapshot
Integrating single-cell transcriptomic data across different conditions, technologies, and species
inferredcites →https://doi.org/10.1038/nbt.4314openalex-snapshot
Dimensionality reduction for visualizing single-cell data using UMAP
inferredcites →https://doi.org/10.1038/nmeth.2772openalex-snapshot
Quantitative single-cell RNA-seq with unique molecular identifiers
inferredcites →https://doi.org/10.1038/nmeth.4644openalex-snapshot
scmap: projection of single-cell RNA-seq data across data sets
inferredcites →https://doi.org/10.1038/s41467-019-13056-xopenalex-snapshot
The art of using t-SNE for single-cell transcriptomics
inferredcites →https://doi.org/10.1038/s41592-018-0308-4openalex-snapshot
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
inferredcites →https://doi.org/10.1038/s41593-017-0029-5openalex-snapshot
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
inferredcites →https://doi.org/10.1109/iccv.2011.6126344openalex-snapshot
Domain adaptation for object recognition: An unsupervised approach
inferredcites →https://doi.org/10.1145/2347736.2347755openalex-snapshot
A few useful things to know about machine learning
inferredcites →https://doi.org/10.1186/gb-2005-6-2-r21openalex-snapshot
An ontology for cell types
inferredcites →https://doi.org/10.1186/s13059-017-1382-0openalex-snapshot
SCANPY: large-scale single-cell gene expression data analysis
inferredcites →https://doi.org/10.21105/joss.00861openalex-snapshot
UMAP: Uniform Manifold Approximation and Projection
inferredcites →https://doi.org/10.23915/distill.00002openalex-snapshot
How to Use t-SNE Effectively
inferredcites →https://doi.org/10.48550/arxiv.1712.09005openalex-snapshot
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
inferredcites →https://doi.org/10.48550/arxiv.1802.03426openalex-snapshot
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
inferredcites →https://doi.org/10.5555/2627435.2697068openalex-snapshot
Accelerating t-SNE using tree-based algorithms
inferredcites →https://doi.org/10.7551/mitpress/9780262170055.001.0001openalex-snapshot
Dataset Shift in Machine Learning

What points at this work

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

inferred← citeshttps://doi.org/10.1002/jemt.24704openalex-snapshot
High‐Accuracy and Lightweight Image Classification Network for Optimizing Lymphoblastic Leukemia Diagnosis
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.130561openalex-snapshot
Assessing feature scorer results on high-dimensional datasets with t-SNE
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.2c00267openalex-snapshot
Predicting Endocrine Disruption Using Conformal Prediction – A Prioritization Strategy to Identify Hazardous Chemicals with Confidence
inferred← citeshttps://doi.org/10.1038/s41467-019-13056-xopenalex-snapshot
The art of using t-SNE for single-cell transcriptomics
inferred← citeshttps://doi.org/10.1093/bioinformatics/btad449openalex-snapshot
Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in single-cell RNA-sequencing
inferred← citeshttps://doi.org/10.1101/2020.07.17.207993openalex-snapshot
VAE-SNE: a deep generative model for simultaneous dimensionality reduction and clustering
inferred← citeshttps://doi.org/10.1101/731877openalex-snapshot
openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding
inferred← citeshttps://doi.org/10.1126/science.abj6856openalex-snapshot
The widespread IS200/IS605 transposon family encodes diverse programmable RNA-guided endonucleases
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1012241openalex-snapshot
PARE: A framework for removal of confounding effects from any distance-based dimension reduction method
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