Contents
What is gene expression clustering?
Clustering is often one of the first steps in gene expression analysis. The goal of clustering is to subdivide a set of items (in our case, genes) in such a way that similar items fall into the same cluster, whereas dissimilar items fall in different clusters.
Why are genes clustered?
Gene Clusters The DNA found between each repeated gene in the gene cluster is non-conserved. Portions of the DNA sequence of a gene is found to be identical in genes contained in a gene cluster. Gene conversion is the only method in which gene clusters may become homogenized.
How to perform RNA-Seq data analysis in R?
RNA-Seq data analysis in R – Investigate differentially expressed genes in your data! In this tutorial, negative binomial was used to perform differential gene expression analyis in R using DESeq2, pheatmap and tidyverse packages. The workflow for the RNA-Seq data is:
How are differentially expressed genes found in clusters?
In addition, cells are often clustered, and differentially expressed genes (DEGs) are identified between the different clusters. This approach for finding DEGs by comparing between clusters is widely used in existing methods 8, 11, 12, and enables finding cluster-specific marker genes that facilitate labeling different cell populations.
Are there problems with clustering for differential expression?
Defining more flexible statistical frameworks for predicting complex patterns of differential expression is one of the grand challenges in single-cell data analysis 15. A major problem with clustering-based approaches for DEG prediction is that the definition of cell clusters is often not straightforward.
How are different modalities of RNA expression measured?
Recent advances in single-cell technologies enable us to assess the state of cells by measuring different modalities like RNA and protein expression with single-cell resolution 1, 2, 3, 4, 5.