scholarly journals A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst

2012 ◽  
Vol 28 (18) ◽  
pp. i626-i632 ◽  
Author(s):  
F. Buettner ◽  
F. J. Theis
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Novel Approach ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Single-cell gene expression patterns in lupus monocytes independently indicate disease activity, interferon and therapy

2017 ◽  
Vol 4 (1) ◽  
pp. e000202 ◽  
Author(s):  
Zhongbo Jin ◽  
Wei Fan ◽  
Mark A Jensen ◽  
Jessica M Dorschner ◽  
George F Bonadurer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Activity ◽  
Single Cell ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Human bone marrow-derived mesenchymal stem cell gene expression patterns vary with culture conditions

2013 ◽  
Vol 48 (2) ◽  
pp. 107 ◽  
Author(s):  
Myoung Woo Lee ◽  
Dae Seong Kim ◽  
Keon Hee Yoo ◽  
Hye Ryung Kim ◽  
In Keun Jang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Stem Cell ◽  
Mesenchymal Stem Cell ◽  
Expression Patterns ◽  
Culture Conditions ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene ◽  
Stem Cell Gene

scholarly journals scHLAcount: allele-specific HLA expression from single-cell gene expression data

2020 ◽  
Vol 36 (12) ◽  
pp. 3905-3906 ◽  
Author(s):  
Charlotte A Darby ◽  
Michael J T Stubbington ◽  
Patrick J Marks ◽  
Álvaro Martínez Barrio ◽  
Ian T Fiddes
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Human Leukocyte ◽  
Supplementary Information ◽  
Hla Genes ◽  
Sequencing Studies ◽  
Cell Gene Expression ◽  
Allele Specific ◽  
Cell Gene

Abstract Summary Bulk RNA sequencing studies have demonstrated that human leukocyte antigen (HLA) genes may be expressed in a cell type-specific and allele-specific fashion. Single-cell gene expression assays have the potential to further resolve these expression patterns, but currently available methods do not perform allele-specific quantification at the molecule level. Here, we present scHLAcount, a post-processing workflow for single-cell RNA-seq data that computes allele-specific molecule counts of the HLA genes based on a personalized reference constructed from the sample’s HLA genotypes. Availability and implementation scHLAcount is available under the MIT license at https://github.com/10XGenomics/scHLAcount. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Blood mononuclear cell gene expression patterns denote the onset of persisting post-partum depressive disorder

2009 ◽  
Vol 15 (1) ◽  
pp. 2-2 ◽  
Author(s):  
R H Segman ◽  
T Goltser-Dubner ◽  
I Weiner ◽  
L Canetti ◽  
E Galili-Weisstub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Depressive Disorder ◽  
Mononuclear Cell ◽  
Post Partum ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

2021 ◽  
Author(s):  
Kun Qian ◽  
Shiwei Fu ◽  
Hongwei Li ◽  
Wei Vivian Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Real Data ◽  
Heterogeneous Data ◽  
Biological Processes ◽  
Cell Gene Expression ◽  
Improved Performance ◽  
Cell Gene ◽  
Clinical Problems

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Even though different batch effect removal methods have been developed, none of the existing methods is suitable for heterogeneous single-cell samples coming from multiple biological conditions. To address this challenge, we propose a method named scINSIGHT to learn coordinated gene expression patterns that are common among or specific to different biological conditions, offering a unique chance to identify cellular identities and key biological processes across single-cell samples. We have evaluated scINSIGHT in comparison with state-of-the-art methods using simulated and real data, which consistently demonstrate its improved performance. In addition, our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

scholarly journals Prediction of single-cell gene expression for transcription factor analysis

GigaScience ◽  
2020 ◽  
Vol 9 (11) ◽  
Author(s):  
Fatemeh Behjati Ardakani ◽  
Kathrin Kattler ◽  
Tobias Heinen ◽  
Florian Schmidt ◽  
David Feuerborn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Novel Approach ◽  
Cell Gene Expression ◽  
Specific Regulation ◽  
Cell Gene

Abstract Background Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Results Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Conclusion Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

scholarly journals 91. Retroviral Vector Integrations Relate to Hematopoietic Stem Cell Gene Expression Patterns

2006 ◽  
Vol 13 ◽  
pp. S37-S38
Author(s):  
Martijn H. Brugman ◽  
Karin Pike-Overzet ◽  
Carla Oerlemans- Bergs ◽  
Sigrid Swagemakers ◽  
Dick de Ridder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Hematopoietic Stem Cell ◽  
Expression Patterns ◽  
Retroviral Vector ◽  
Gene Expression Patterns ◽  
Hematopoietic Stem ◽  
Cell Gene Expression ◽  
Cell Gene ◽  
Stem Cell Gene
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