Journal ArticleDOI
Challenges in unsupervised clustering of single-cell RNA-seq data.
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This Review discusses the multiple algorithmic options for clustering scRNA-seq data, including various technical, biological and computational considerations.Abstract:
Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.read more
Citations
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scds: Computational Annotation of Doublets in Single Cell RNA Sequencing Data
Abha S. Bais,Dennis Kostka +1 more
TL;DR: Single cell doublet scoring (scds) is presented, a software tool for the in silico identification of doublets in scRNA-seq data and is presented as a scalable, competitive approach that allows for doublet annotations in thousands of cells in a matter of seconds.
Journal ArticleDOI
Immune contexture defined by single cell technology for prognosis prediction and immunotherapy guidance in cancer
TL;DR: There are much more to be uncovered in this rapidly developing field of medicine, and they will predict the prognosis of cancer patients and guide the rational design of immunotherapies for success in cancer eradication.
Journal ArticleDOI
A periodic table of cell types
Bo Xia,Itai Yanai +1 more
TL;DR: A periodic table of cell types is proposed that aligns cell types according to their developmental stages, connecting them to one another according to the universal axis from stem cells to differentiated cells.
Posted ContentDOI
Fast and precise single-cell data analysis using hierarchical autoencoder
TL;DR: An extensive analysis demonstrates that the hierarchical autoencoder approach vastly outperforms state-of-the-art techniques in many research sub-fields of scRNA-seq analysis, including cell segregation through unsupervised learning, visualization of transcriptome landscape, cell classification, and pseudo-time inference.
Journal ArticleDOI
Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis
Thomas A Geddes,Taiyun Kim,Lihao Nan,James G. Burchfield,Jean Yee Hwa Yang,Dacheng Tao,Pengyi Yang,Pengyi Yang +7 more
TL;DR: The proposed autoencoder-based cluster ensemble framework can facilitate more accurate cell type identification as well as other downstream analyses and can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithms and a state-of-the-art kernel-based clustering algorithm designed specifically for scRNA-seq data.
References
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TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
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