Results 41 to 50 of about 2,352,277 (290)

Protein Expression Analyses at the Single Cell Level

Molecules, 2014
The central dogma of molecular biology explains how genetic information is converted into its end product, proteins, which are responsible for the phenotypic state of the cell. Along with the protein type, the phenotypic state depends on the protein copy
Masae Ohno, Peter Karagiannis, Yuichi Taniguchi   +2 more
doaj   +1 more source

Exploiting single-cell expression to characterize co-expression replicability [PDF]

, 2016
BACKGROUND: Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes.
Ballouz, S., Crow, M., Gillis, J., Huang, Z. J., Paul, A.   +4 more
core   +1 more source

Application of machine learning to associative scRNA-seq data gene expression and alternative polyadenylation sites clustering [PDF]

BIO Web of Conferences, 2023
Cell type identification is a vital step in the analysis of scRNA-seq data. Transcriptome subtype pivotal information such as alternative polyadenylation (APA) obtained from standard scRNA-seq data can also provide valid clues for cell type ...
Hu Jiongsong, Ren Chao, Shu Wenjie, Zhou Gangqiao   +3 more
doaj   +1 more source

Gene expression atlas of a developing tissue by single cell expression correlation analysis [PDF]

Nature Methods, 2018
ABSTRACTThe Drosophila wing disc has been a fundamental model system for the discovery of key signaling pathways and for our understanding of developmental processes. However, a complete map of gene expression in this tissue is lacking. To obtain a complete gene expression atlas in the wing disc, we employed single-cell sequencing (scRNA-seq) and ...
Josephine Bageritz, Philipp Willnow, Erica Valentini, Svenja Leible, Michael Boutros, Aurelio A. Teleman   +5 more
openaire   +3 more sources

Trajectory-based differential expression analysis for single-cell sequencing data [PDF]

, 2020
Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the ...
Cannoodt, Robrecht, Clement, Lieven, Dudoit, Sandrine, Roux de Bézieux, Hector, Saelens, Wouter, Saeys, Yvan, Street, Kelly, Van den Berge, Koen   +7 more
core   +2 more sources

Generation and characterization of a mitotane-resistant adrenocortical cell line [PDF]

, 2020
Mitotane is the only drug approved for the therapy of adrenocortical carcinoma (ACC). Its clinical use is limited by the occurrence of relapse during therapy.
Bachmann, Sebastian, Bahethi, Rohini, Beule, Dieter, Choi, Murim, Eisenhofer, Graeme, Liebisch, Gerhard, Messerschmidt, Clemens, Obermayer, Benedikt, Peitzsch, Mirko, Scholl, Ute I., Schrade, Petra, Seidel, Eric, Walenda, Gudrun, Wallace, Paal, Yoo, Taekyeong   +14 more
core   +2 more sources

scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information

Cell & Bioscience, 2022
Background Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream ...
Jing Qi, Qiongyu Sheng, Yang Zhou, Jiao Hua, Shutong Xiao, Shuilin Jin   +5 more
doaj   +1 more source

Temporal dynamics and transcriptional control using single-cell gene expression analysis [PDF]

Genome Biology, 2013
Abstract Background Changes in environmental conditions lead to expression variation that manifest at the level of gene regulatory networks. Despite a strong understanding of the role noise plays in synthetic biological systems, it remains unclear how propagation of expression heterogeneity in an ...
Kouno, Tsukasa, de Hoon, Michiel, Mar, Jessica C., Tomaru, Yasuhiro, Kawano, Mitsuoki, Carninci, Piero, Suzuki, Harukazu, Hayashizaki, Yoshihide, Shin, Jay W.   +8 more
openaire   +3 more sources

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer

Cell Reports: Methods
Summary: Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values.
Yupu Xu, Yuzhou Wang, Shisong Ma
doaj   +1 more source

SMART-Q:  An Integrative Pipeline Quantifying Cell Type-Specific RNA Transcription.

PLoS ONE, 2020
Accurate RNA quantification at the single-cell level is critical for understanding the dynamics of gene expression and regulation across space and time.
Xiaoyu Yang, Seth Bergenholtz, Lenka Maliskova, Mark-Phillip Pebworth, Arnold R Kriegstein, Yun Li, Yin Shen   +6 more
doaj   +1 more source