Expression Variability of Absorption, Distribution, Metabolism, Excretion–Related MicroRNAs in Human Liver: Influence of Nongenetic Factors and Association with Gene Expression

2013 ◽  
Vol 41 (10) ◽  
pp. 1752-1762 ◽  
Author(s):  
Jessica K. Rieger ◽  
Kathrin Klein ◽  
Stefan Winter ◽  
Ulrich M. Zanger
Keyword(s):  
Gene Expression ◽  
Human Liver ◽  
Expression Variability

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

scholarly journals Gene Expression Profiling of Alcoholic Liver Disease in the Baboon (Papio hamadryas) and Human Liver

2003 ◽  
Vol 163 (6) ◽  
pp. 2303-2317 ◽  
Author(s):  
Devanshi Seth ◽  
Maria A. Leo ◽  
Peter H. McGuinness ◽  
Charles S. Lieber ◽  
Yvonne Brennan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Disease ◽  
Gene Expression Profiling ◽  
Alcoholic Liver Disease ◽  
Expression Profiling ◽  
Human Liver ◽  
Papio Hamadryas

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals Inferring gene regulation dynamics from static snapshots of gene expression variability

2021 ◽  
Vol 104 (4) ◽  
Author(s):  
Euan Joly-Smith ◽  
Zitong Jerry Wang ◽  
Andreas Hilfinger
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Expression Variability
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