scholarly journals singleCellHaystack: Finding surprising genes in 2-dimensional representations of single cell transcriptome data

2019 ◽  
Author(s):  
Alexis Vandenbon ◽  
Diego Diez
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
R Package ◽  
Biological Knowledge ◽  
Transcriptome Data ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Leibler Divergence ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractSummarySingle-cell sequencing data is often visualized in 2-dimensional plots, including t-SNE plots. However, it is not straightforward to extract biological knowledge, such as differentially expressed genes, from these plots. Here we introduce singleCellHaystack, a methodology that addresses this problem. singleCellHaystack uses Kullback-Leibler Divergence to find genes that are expressed in subsets of cells that are non-randomly positioned on a 2D plot. We illustrate the usage of singleCellHaystack through applications on several single-cell datasets. singleCellHaystack is implemented as an R package, and includes additional functions for clustering and visualization of genes with interesting expression patterns.Availability and implementationhttps://github.com/alexisvdb/[email protected]

scholarly journals Building the mega single-cell transcriptome ocular meta-atlas

GigaScience ◽  
2021 ◽  
Vol 10 (10) ◽  
Author(s):  
Vinay S Swamy ◽  
Temesgen D Fufa ◽  
Robert B Hufnagel ◽  
David M McGaughey
Keyword(s):  
Single Cell ◽  
Biological Effects ◽  
R Package ◽  
Technical Noise ◽  
Batch Correction ◽  
Workflow System ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Compare And Contrast ◽  
Cluster Purity

Abstract Background: The development of highly scalable single-cell transcriptome technology has resulted in the creation of thousands of datasets, >30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable because this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects because there are substantial batch effects from computational processing, single-cell technology utilized, and the natural biological variation. While many single-cell transcriptome-specific batch correction methods purport to remove the technical noise, it is difficult to ascertain which method functions best. Results: We developed a lightweight R package (scPOP, single-cell Pick Optimal Parameters) that brings in batch integration methods and uses a simple heuristic to balance batch merging and cell type/cluster purity. We use this package along with a Snakefile-based workflow system to demonstrate how to optimally merge 766,615 cells from 33 retina datsets and 3 species to create a massive ocular single-cell transcriptome meta-atlas. Conclusions: This provides a model for how to efficiently create meta-atlases for tissues and cells of interest.

scholarly journals BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

2018 ◽  
Author(s):  
Zhe Sun ◽  
Li Chen ◽  
Hongyi Xin ◽  
Qianhui Huang ◽  
Anthony R Cillo ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
R Package ◽  
Clustering Methods ◽  
Model Framework ◽  
Bayesian Hierarchical ◽  
Bayesian Mixture ◽  
Population Scale ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractThe recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

scholarly journals A 3D system to model human pancreas development and its reference single-cell transcriptome atlas identify signaling pathways required for progenitor expansion

2021 ◽  
Author(s):  
Carla A. Gonçalves ◽  
Michael Larsen ◽  
Sascha Jung ◽  
Johannes Stratmann ◽  
Akiko Nakamura ◽  
...  
Keyword(s):  
Single Cell ◽  
R Package ◽  
Fetal Tissue ◽  
Defined Medium ◽  
Three Dimensions ◽  
Practical Reasons ◽  
Human Pancreas ◽  
Fetal Pancreas ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Human organogenesis remains relatively unexplored for ethical and practical reasons. Here we report the establishment of a single cell transcriptome atlas of the human fetal pancreas between 7 and 10 post-conceptional weeks of development. To interrogate cell-cell interactions we developed InterCom, an R-Package for identifying receptors-ligand pairs and their downstream effects. We further report the establishment of a human pancreas culture system starting from fetal tissue or human pluripotent stem cells, enabling the long-term maintenance of pancreas progenitors in a minimal, defined medium in three-dimensions. Benchmarking the cells produced in 2D and those expanded in 3D to fetal tissue reveals that progenitors expanded in 3D are transcriptionally closer to the fetal pancreas. We further demonstrate the potential of this system as a screening platform and identify the importance of the EGF and FGF pathways controlling human pancreas progenitor expansion.

scholarly journals Single-Cell Transcriptome Analysis of Human Adipose-Derived Stromal Cells Identifies a Contractile Cell Subpopulation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Elize Wolmarans ◽  
Juanita Mellet ◽  
Chrisna Durandt ◽  
Fourie Joubert ◽  
Michael S. Pepper
Keyword(s):  
Single Cell ◽  
Transcriptome Analysis ◽  
Stromal Cells ◽  
Exploratory Study ◽  
Expression Patterns ◽  
Cell Subpopulation ◽  
Adipose Derived Stromal Cells ◽  
Cell Transcriptome ◽  
Population Doublings ◽  
Single Cell Transcriptome

The potential for human adipose-derived stromal cells (hASCs) to be used as a therapeutic product is being assessed in multiple clinical trials. However, much is still to be learned about these cells before they can be used with confidence in the clinical setting. An inherent characteristic of hASCs that is not well understood is their heterogeneity. The aim of this exploratory study was to characterize the heterogeneity of freshly isolated hASCs after two population doublings (P2) using single-cell transcriptome analysis. A minimum of two subpopulations were identified at P2. A major subpopulation was identified as contractile cells which, based on gene expression patterns, are likely to be pericytes and/or vascular smooth muscle cells (vSMCs).

scholarly journals The Drosophila Embryo at Single Cell Transcriptome Resolution

2017 ◽  
Author(s):  
Nikos Karaiskos ◽  
Philipp Wahle ◽  
Jonathan Alles ◽  
Anastasiya Boltengagen ◽  
Salah Ayoub ◽  
...  
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Drosophila Embryo ◽  
Evolutionary Changes ◽  
Cell Transcriptome ◽  
Mechanisms Of Development ◽  
In Situ Hybridizations ◽  
Single Cell Transcriptome

ABSTRACTDrosophila is a premier model system for understanding the molecular mechanisms of development. By the onset of morphogenesis, ~6000 cells express distinct gene combinations according to embryonic position. Despite extensive mRNA in situ screens, combinatorial gene expression within individual cells is largely unknown. Therefore, it is difficult to comprehensively identify the coding and non-coding transcripts that drive patterning and to decipher the molecular basis of cellular identity. Here, we single-cell sequence precisely staged embryos, measuring >3100 genes per cell. We produce a ‘transcriptomic blueprint’ of development – a virtual embryo where 3D locations of sequenced cells are confidently identified. Our “Drosophila-Virtual-Expression-eXplorer” performs virtual in situ hybridizations and computes expression gradients. Using DVEX, we predict spatial expression and discover patterned lncRNAs. DEVX is sensitive enough to detect subtle evolutionary changes in expression patterns between Drosophila species. We believe DVEX is a prototype for powerful single cell studies in complex tissues.

scholarly journals Contribution of Single-Cell Transcriptomics to the Characterization of Human Spermatogonial Stem Cells: Toward an Application in Male Fertility Regenerative Medicine?

2019 ◽  
Vol 20 (22) ◽  
pp. 5773 ◽  
Author(s):  
Anne-Sophie Gille ◽  
Clémentine Lapoujade ◽  
Jean-Philippe Wolf ◽  
Pierre Fouchet ◽  
Virginie Barraud-Lange
Keyword(s):  
Stem Cells ◽  
Regenerative Medicine ◽  
Single Cell ◽  
Developmental Process ◽  
Expression Patterns ◽  
Spermatogonial Stem Cells ◽  
Genomic Technologies ◽  
Cell Transcriptome ◽  
Definition Of ◽  
Single Cell Transcriptome

Ongoing progress in genomic technologies offers exciting tools that can help to resolve transcriptome and genome-wide DNA modifications at single-cell resolution. These methods can be used to characterize individual cells within complex tissue organizations and to highlight various molecular interactions. Here, we will discuss recent advances in the definition of spermatogonial stem cells (SSC) and their progenitors in humans using the single-cell transcriptome sequencing (scRNAseq) approach. Exploration of gene expression patterns allows one to investigate stem cell heterogeneity. It leads to tracing the spermatogenic developmental process and its underlying biology, which is highly influenced by the microenvironment. scRNAseq already represents a new diagnostic tool for the personalized investigation of male infertility. One may hope that a better understanding of SSC biology could facilitate the use of these cells in the context of fertility preservation of prepubertal children, as a key component of regenerative medicine.

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