Optimal gene selection for cell type discrimination in single cell analyses
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TLDR
Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers.Abstract:
Single-cell technologies characterize complex cell populations across multiple data modalities at un-precedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers to identify and differentiate specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers than existing methods. When applied to a data set given a hierarchy of cell type labels, the markers found by our method enable the recovery of the label hierarchy through a computationally efficient and principled optimization.read more
Citations
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Journal ArticleDOI
MarkerMap: nonlinear marker selection for single-cell studies
TL;DR: MarkerMap is introduced, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction, and benchmark MarkerMap’s competitive performance against previously published approaches on real single cell gene expression data sets.
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InGene: Finding influential genes from embeddings of nonlinear dimension reduction techniques
TL;DR: InGene as mentioned in this paper assigns an importance score to each expressed gene based on its contribution to the construction of the low-dimensional map, which can provide insight into the cellular heterogeneity of scRNA-seq data and accurately identify genes associated with cell-type populations or diseases.
Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets
Sean K. Maden,Sang Ho Kwon,Louise Huuki-Myers,Leonardo Collado-Torres,Stephanie C. Hicks,Kristen R. Maynard +5 more
TL;DR: In this article , the authors discuss several experimental and computational challenges in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues.
Posted ContentDOI
Gene panel design for spatial transcriptomics with prioritized gene sets
Mashrur Ahmed Yafi,Md. Hasibul Husain Hisham,Francisco Grisanti,Atif Rahman,Md. Abul Hassan Samee +4 more
TL;DR: This work proposes scGIST– a deep neural network that designs sc-ST panels through constrained feature selection that outperformed alternative methods in terms of cell type detection accuracy and allows genes of interest to be prioritized for inclusion in the panel while staying within the size constraint.
References
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Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
Proceedings Article
Distance Metric Learning for Large Margin Nearest Neighbor Classification
TL;DR: In this article, a Mahanalobis distance metric for k-NN classification is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin.
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Massively parallel digital transcriptional profiling of single cells
Grace X.Y. Zheng,Jessica M. Terry,Phillip Belgrader,Paul Ryvkin,Zachary Bent,Ryan Wilson,Solongo B. Ziraldo,Tobias Daniel Wheeler,Geoffrey P. McDermott,Junjie Zhu,Mark T. Gregory,Joe Shuga,Luz Montesclaros,Jason G. Underwood,Donald A. Masquelier,Stefanie Y. Nishimura,Michael Schnall-Levin,Paul Wyatt,Christopher Hindson,Rajiv Bharadwaj,Alexander Wong,Kevin D. Ness,Lan Beppu,H. Joachim Deeg,Christopher McFarland,Keith R. Loeb,Keith R. Loeb,William J. Valente,William J. Valente,Nolan G. Ericson,Emily A. Stevens,Jerald P. Radich,Tarjei S. Mikkelsen,Benjamin J. Hindson,Jason H. Bielas +34 more
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
TL;DR: This paper shows how to learn a Mahalanobis distance metric for kNN classification from labeled examples in a globally integrated manner and finds that metrics trained in this way lead to significant improvements in kNN Classification.
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq as discussed by the authors analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin, and identifies 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes.