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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics
Jiayi Dong1,2, Yin Zhang1,2, Fei Wang3,4
1Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.
Background:
With the development of modern sequencing technology, hundreds of thousands of single-cell RNA-sequencing (scRNA-seq) profiles allow to explore the heterogeneity in the cell level, but it faces the challenges of high dimensions and high sparsity. Dimensionality reduction is essential for downstream analysis, such as clustering to identify cell subpopulations. Usually, dimensionality reduction follows unsupervised approach.
Results:
In this paper, we introduce a semi-supervised dimensionality reduction method named scSemiAE, which is based on an autoencoder model. It transfers the information contained in available datasets with cell subpopulation labels to guide the search of better low-dimensional representations, which can ease further analysis.
Conclusions:
Experiments on five public datasets show that, scSemiAE outperforms both unsupervised and semi-supervised baselines whether the transferred information embodied in the number of labeled cells and labeled cell subpopulations is much or less.
