Tag Archives: Clustering

Combining single-cell genomics and computational techniques, a research team including Paul Robson, Ph.D., director of single-cell biology at The Jackson Laboratory (JAX), has defined cell-type composition of cancerous cells from 11 colorectal tumors, as well as adjacent noncancerous cells, a ...

Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here researchers from the University ...

Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. ...

Most existing dimensionality reduction and clustering packages for single cell RNA-Seq (scRNASeq) data deal with dropouts by heavy modelling and computational machinery. Here researchers from the Victor Chang Cardiac Research Institute introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ...

A comparison of transcriptional profiles derived from different tissues in a given species or among different species assumes that commonalities reflect evolutionarily conserved programs and that differences reflect species or tissue responses to environmental conditions or developmental program staging. Apparently ...

Analysis of the composition of heterogeneous tissue has been greatly enabled by recent developments in single-cell transcriptomics. Researchers from UCSF have developed SCell, an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation ...

Grade of membership models, also known as “admixture models”, “topic models” or “Latent Dirichlet Allocation”, are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to ...

RNA sequencing (RNA-Seq) measures genome-wide gene expression. RNA-Seq data is count-based rendering normal distribution models for analysis inappropriate. Normalization of RNA-Seq data to transform the data has limitations which can adversely impact the analysis. Furthermore, there are a few count-based ...

Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling which limit their scope and generality. Researchers from UC Berkely and Stanford University have developed a novel method that departs from standard analysis pipelines, comparing and clustering ...

Date: February 10th-12th, 2016 Location: Room PC95 (first floor), RADIX building (107), Wageningen Campus, Droevendaalsesteeg 1, Wageningen, the Netherlands Directions: directions (check also Map Wageningen UR Campus) Language: English Group size: maximum of 35 participants Credits: 0.8 ECTS Registration and ...