Identification of cell types from single-cell transcriptomes using a novel clustering method

By Chen Xu, Zhengchang Su. Bioinform., 2015

529 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/1fbaa51a428f045d7425d88d8827699adcef6ba7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Fabian J Theis
2. Rahul Satija
3. F. Alexander Wolf
4. Malte D. Luecken
5. John C. Marioni
6. Allon M. Klein
7. Christina Kendziorski
8. Rhonda L. Bacher

In Spectroscopy

1. Naoko Koyano-Nakagawa
2. Daniel J. Garry
3. Joshua Wing Kei Ho
4. Andrew W. Butler
5. Aaron T. L. Lun
6. Nikolaus Rajewsky
7. Peijie Lin
8. Christina Kendziorski

Citations in the corpus

• SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (2015, methodology): “We performed a comparative evaluation of SINCERA with three recently available singlecell RNA-seq analysis tools, SNN-Cliq [32], scLVM [27] and SINGuLAR Analysis Toolset (https://cn.”
• Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “For example, zero-inflated differential expression tests have been tailored to scRNA-seq data to identify changes within a single-cell type 16,17 , and clustering approaches 18–23 can detect proportional shifts across conditions if cell types are conserved.”
• Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “Xu and Su [71] recommend using genes with average RPKM >20 and using a log transformation to reduce the effect of outliers.”
• SPRING: a kinetic interface for visualizing high dimensional single-cell expression data (2017, methodology): “K-nearest-neighbor (knn) graphs have proven useful for analyzing continuous cell topologies (Bendall et al., 2014; Setty et al., 2016; Xu and Su, 2015), and one study proposed the use of knn graphs for visualization and data clustering (Islam et al.”
• Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, background): “Such a strategy can effectively handle high-dimensional data 11 .”
• Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, methodology): “We constructed a shared-nearest-neighbour (SNN) graph [29] using the combined cells and the union of the highly variable genes that were commonly expressed across all data set.”
• Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “This assumption is on one hand based on the community’s success with graph-based clustering [18–20], pseudotime inference [8], visualization [39, 40] and tSNE [16, 41].”