Proceedings ArticleDOI
Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types
Jingjie Xu,Razin A. Shaikh,Vladimir Brusic +2 more
- pp 1435-1438
TLDR
The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.Abstract:
Single cell transcriptomics (SCT) reveals cellular patterns that are masked and hidden in bulk RNA experiments. We analyzed 100 human SCT data sets for summary patterns that quantify gene expression per individual cell as well as per gene. Peripheral Blood Mononuclear Cells (PBMCs) show patterns different to those of cancer cell lines, stem cells, embryonic stem cells and other cell types. The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.read more
Citations
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Type-I interferon modulates monocyte recruitment and maturation in chronic inflammation
Pui Y. Lee,Yi Li,Yutaro Kumagai,Jason S. Weinstein,Dina C. Nacionales,Edward J. Butfiloski,Erinn S. Kellner,Shizuo Akira,Eric S. Sobel,Minoru Satoh,Westley H. Reeves +10 more
TL;DR: A novel role of IFN-I is revealed in promoting the recruitment of inflammatory monocytes via the chemokine receptor CCR2 in chronic peritonitis and the lack of terminal differentiation induced by IFn-I may help sustain the chronic inflammatory response.
Proceedings ArticleDOI
Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks
Razin A. Shaikh,Jiahui Zhong,Minjie Lyu,Sen Lin,Derin B. Keskin,Guang Lan Zhang,Lou Chitkushev,Vladimir Brusic +7 more
TL;DR: Using 27 human single cell transcriptomics data sets, an artificial neural network model for classification of Peripheral Blood Mononuclear Cells (PBMC) is developed by combining multiple independent data sets to form training data sets.
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