SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species

By Yuqi Tan, Patrick Cahan. bioRxiv, 2018

309 citations recorded by the source corpus.

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the regulators of developmental programs. A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. SingleCellNet compares favorably to other methods, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments. Highlight SingleCellNet (SCN) enables the classification of scRNA-Seq data across platforms and species SCN is open source and extendible We illustrate the utility of SCN with three example applications

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fe663bed822dd0f1dce1b56417127c8949aa5a86

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In Molecular Biology

1. Yuxing Liao
2. Hanlee P. Ji
3. Zhiao Shi
4. Jerzy K. Kulski

Citations in the corpus

• scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data (2019, result): “We then compared the prediction performance of scmap [33], caSTLe [34], singleCellNet [35], and scID [36] following the same approach.”
• WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics (2024, methodology): “Therefore, for small input gene lists, we recommend users to verify W ebGestalt’ s outputs with machine learning-based approaches such as scPred ( 24 ) and SingleCellNet ( 25 ).”
• scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, methodology): “Several methods including Sin-gleR (8), CHETAH (9), scMap (10), scID (11), scPred (12), ACTINN (13), CellAssign (14), Garnett (15), SCINA (16), singleCellNet (17) and support vector machine (SVM) belong to this category as described in the recent review (18).”