Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints | Paper Citation Record

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This paper cites A comparison of automatic cell identification methods for single- cell RNA sequencing data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints A comparison of automatic cell identification methods for single- cell RNA sequencing data

Reference 1

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Observation 9b71a2f1-c950-426f-a061-6798e7ef3f74 · outbound

This paper cites scPred: accurate supervised method for cell-type clas- sification from single-cell RNA-seq data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints scPred: accurate supervised method for cell-type clas- sification from single-cell RNA-seq data

Reference 2

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This paper cites flowMatch: Cell population matching and meta-clustering in Flow Cytometry.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints flowMatch: Cell population matching and meta-clustering in Flow Cytometry

Reference 3

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This paper cites Low-avidity CD4+ T cell responses to SARS-CoV-2 in unexposed individuals and humans with severe COVID-19.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Low-avidity CD4+ T cell responses to SARS-CoV-2 in unexposed individuals and humans with severe COVID-19

Reference 4

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This paper cites A Single-Cell Transcriptomic Map of the Human and Mouse Pan- creas Reveals Inter- and Intra-cell Population Structure.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints A Single-Cell Transcriptomic Map of the Human and Mouse Pan- creas Reveals Inter- and Intra-cell Population Structure

Reference 5

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This paper cites Iterative Bregman projections for regularized transportation problems.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Iterative Bregman projections for regularized transportation problems

Reference 6

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This paper cites A unified computational framework for single-cell data integration with optimal transport.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints A unified computational framework for single-cell data integration with optimal transport

Reference 7

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This paper cites Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods

Reference 8

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Observation 62f70c4f-2e6b-4fbd-9477-1ab8f9f71287 · outbound

This paper cites POT: Python Optimal Transport.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints POT: Python Optimal Transport

Reference 9

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This paper cites ClusterMap: compare multiple single cell RNA-Seq datasets across dif- ferent experimental conditions.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints ClusterMap: compare multiple single cell RNA-Seq datasets across dif- ferent experimental conditions

Reference 10

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Observation 27acdcef-1b38-4d4a-a70e-c5fd06e5e579 · outbound

This paper cites Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

Reference 11

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Observation a63d5bbf-b163-41a8-ae28-3983b041b28a · outbound

This paper cites Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

Reference 12

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This paper cites scMatch: a single-cell gene expres- sion profile annotation tool using reference datasets.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints scMatch: a single-cell gene expres- sion profile annotation tool using reference datasets

Reference 13

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This paper cites Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis

Reference 14

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This paper cites Optimal Transport improves cell- cell similarity inference in single-cell omics data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Optimal Transport improves cell- cell similarity inference in single-cell omics data

Reference 15

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This paper cites Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Reference 16

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This paper cites Stalled developmental programs at the root of pediatric brain tumors.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Stalled developmental programs at the root of pediatric brain tumors

Reference 17

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Observation 42bb48d9-3050-4ad6-a1e6-c3f2c99feb5e · outbound

This paper cites Bayesian approach to single- cell differential expression analysis.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Bayesian approach to single- cell differential expression analysis

Reference 18

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This paper cites Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing

Reference 19

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Observation 671e4bc0-e769-4469-adc7-bbf23b63c915 · outbound

This paper cites Challenges in unsuper- vised clustering of single-cell RNA-seq data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Challenges in unsuper- vised clustering of single-cell RNA-seq data

Reference 20

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This paper cites scmap - A tool for unsupervised projection of single cell RNA-seq data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints scmap - A tool for unsupervised projection of single cell RNA-seq data

Reference 21

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This paper cites Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus

Reference 22

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This paper cites Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes

Reference 23

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This paper cites Machine learning for cell type classification from single nucleus RNA sequencing data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Machine learning for cell type classification from single nucleus RNA sequencing data

Reference 24

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Observation cacfd923-d3c9-44d3-82bd-771e64d6a3d1 · outbound

This paper cites Optimal Transport with Relaxed Marginal Constraints.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Optimal Transport with Relaxed Marginal Constraints

Reference 25

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This paper cites A neural network-based method for exhaustive cell label assign- ment using single cell RNA-seq data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints A neural network-based method for exhaustive cell label assign- ment using single cell RNA-seq data

Reference 26

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This paper cites Multisource single-cell data integration by MA W barycenter for Gaussian mixture models.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Multisource single-cell data integration by MA W barycenter for Gaussian mixture models

Reference 27

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This paper cites A comparison of batch effect removal methods for enhancement of predic- tion performance using MAQC-II microarray gene expression data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints A comparison of batch effect removal methods for enhancement of predic- tion performance using MAQC-II microarray gene expression data

Reference 28

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This paper cites Putative cell type discovery from single-cell gene expression data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Putative cell type discovery from single-cell gene expression data

Reference 29

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This paper cites Enabling reproducible re-analysis of single-cell data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Enabling reproducible re-analysis of single-cell data

Reference 30

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This paper cites Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing

Reference 31

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This paper cites QFMatch: multidimensional flow and mass cytometry samples alignment.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints QFMatch: multidimensional flow and mass cytometry samples alignment

Reference 32

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This paper cites A comparison of marker gene selection methods for single-cell RNA sequencing data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints A comparison of marker gene selection methods for single-cell RNA sequencing data

Reference 33

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This paper cites PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data

Reference 34

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This paper cites Identification of cell-type-specific marker genes from co-expression patterns in tissue samples.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Reference 35

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This paper cites PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data

Reference 36

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This paper cites Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes

Reference 37

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This paper cites Exponential scaling of single-cell RNA-seq in the past decade.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Exponential scaling of single-cell RNA-seq in the past decade

Reference 38

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Observation d77c2d62-a3cd-419f-94ff-483bee9fffd0 · outbound

This paper cites Adult mouse cortical cell taxonomy revealed by single cell transcrip- tomics.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Adult mouse cortical cell taxonomy revealed by single cell transcrip- tomics

Reference 39

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This paper cites A Survey on Optimal Transport for Machine Learning: Theory and Applications.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints A Survey on Optimal Transport for Machine Learning: Theory and Applications

Reference 40

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This paper cites Batch effects and the effective design of single-cell gene expression studies.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Batch effects and the effective design of single-cell gene expression studies

Reference 41

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This paper cites Identifying cell types to interpret scRNA-seq data: how, why and more possibilities.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Identifying cell types to interpret scRNA-seq data: how, why and more possibilities

Reference 42

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This paper cites Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering

Reference 43

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Observation 5f76a912-c59e-4f41-ac67-35d4e9223671 · outbound

This paper cites Single-cell RNA sequencing to identify cellular heterogeneity and targets in cardiovascular diseases: from bench to bedside.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Single-cell RNA sequencing to identify cellular heterogeneity and targets in cardiovascular diseases: from bench to bedside

Reference 44

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Observation 177a841d-3e70-47d8-b1fa-7af9eec514c4 · outbound

This paper cites scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data

Reference 45

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Observation 38f5af9e-ac39-4374-9b28-fb250ee1873e · outbound

This paper cites scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets

Reference 46

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Observation 983a92ff-6835-42bc-885b-50e47f017fda · outbound

This paper cites Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data

Reference 47

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Observation 91baf775-30dd-4ac8-898c-8434df4adaeb · outbound

This paper cites BSDE: barycenter single-cell differential expression for case–control studies.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints BSDE: barycenter single-cell differential expression for case–control studies

Reference 48

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Observation 52901840-89d1-45df-9f95-813cb9ffa7d3 · outbound

This paper cites FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman–Rafsky non-parametric test.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman–Rafsky non-parametric test

Reference 49

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This paper cites Single-cell RNA sequencing reveals the heterogeneity of liver- resident immune cells in human.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Single-cell RNA sequencing reveals the heterogeneity of liver- resident immune cells in human

Reference 50

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This paper cites Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species.

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species

Reference 51

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