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Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data

https://doi.org/10.1101/456129
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What this work says

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

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A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
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A Single-Cell Transcriptome Atlas of the Human Pancreas
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Single-cell messenger RNA sequencing reveals rare intestinal cell types
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Revealing the vectors of cellular identity with single-cell genomics
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Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
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Massively parallel digital transcriptional profiling of single cells
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Validation of noise models for single-cell transcriptomics
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scmap: projection of single-cell RNA-seq data across data sets
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Exponential scaling of single-cell RNA-seq in the past decade
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Tackling the widespread and critical impact of batch effects in high-throughput data
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Single-cell RNA sequencing to explore immune cell heterogeneity
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Single-cell RNA sequencing for the study of development, physiology and disease
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Application of single-cell sequencing in human cancer
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Statistical significance of variables driving systematic variation in high-dimensional data
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K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data
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scPred: Cell type prediction at single-cell resolution
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Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types
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Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
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Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
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Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
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A systematic performance evaluation of clustering methods for single-cell RNA-seq data
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The Human Cell Atlas
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The Human Cell Atlas

What points at this work

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

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Automated methods for cell type annotation on scRNA-seq data
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Artificial intelligence in cell annotation for high-resolution RNA sequencing data
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Putative cell type discovery from single-cell gene expression data
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Deep learning powered single-cell clustering framework with enhanced accuracy and stability
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Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis
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Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale
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ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes
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scMUSCL: multi-source transfer learning for clustering scRNA-seq data
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Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts
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A Novel Effectiveness Assessment Framework for Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer Based on Multi-modal Intelligence
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Single-Cell RNA Sequencing for Studying Human Cancers
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Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts
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Additional file 1 of Single-cell classification using graph convolutional networks
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Additional file 2 of Single-cell classification using graph convolutional networks
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Additional file 3 of Single-cell classification using graph convolutional networks
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LIDER: cell embedding based deep neural network classifier for supervised cell type identification
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