Analysis of signaling pathways in human T-cells using bayesian network modeling of single cell data

Author(s):  
K. Sachs ◽  
O.D. Perez ◽  
D. Pe'er ◽  
G.P. Nolan ◽  
D.K. Gifford ◽  
...  
1990 ◽  
Vol 20 (5) ◽  
pp. 1085-1089 ◽  
Author(s):  
Lalitha Kabilan ◽  
Gudrun Andersson ◽  
Francesco Lolli ◽  
Hans-peter Ekre ◽  
Tomas Olsson ◽  
...  

Water ◽  
2015 ◽  
Vol 7 (10) ◽  
pp. 5617-5637 ◽  
Author(s):  
Yusuyunjiang Mamitimin ◽  
Til Feike ◽  
Reiner Doluschitz

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Peter A. Szabo ◽  
Hanna Mendes Levitin ◽  
Michelle Miron ◽  
Mark E. Snyder ◽  
Takashi Senda ◽  
...  

Abstract Human T cells coordinate adaptive immunity in diverse anatomic compartments through production of cytokines and effector molecules, but it is unclear how tissue site influences T cell persistence and function. Here, we use single cell RNA-sequencing (scRNA-seq) to define the heterogeneity of human T cells isolated from lungs, lymph nodes, bone marrow and blood, and their functional responses following stimulation. Through analysis of >50,000 resting and activated T cells, we reveal tissue T cell signatures in mucosal and lymphoid sites, and lineage-specific activation states across all sites including distinct effector states for CD8+ T cells and an interferon-response state for CD4+ T cells. Comparing scRNA-seq profiles of tumor-associated T cells to our dataset reveals predominant activated CD8+ compared to CD4+ T cell states within multiple tumor types. Our results therefore establish a high dimensional reference map of human T cell activation in health for analyzing T cells in disease.


2007 ◽  
pp. 300-318
Author(s):  
Vipin Narang ◽  
Rajesh Chowdhary ◽  
Ankush Mittal ◽  
Wing-Kin Sung

A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to obtain a workable understanding of this tool. This chapter is intended as a tutorial material to assist the reader in efficiently understanding the fundamental concepts involved in Bayesian network applications. It presents a complete step by step solution of a bioinformatics problem using Bayesian network models, with detailed illustration of modeling, parameter estimation, and inference mechanisms. Considerations in determining an appropriate Bayesian network model representation of a physical problem are also discussed.


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