Contents
What is a highly variable gene?
With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages.
What is variable gene?
SVGs are defined as genes containing at least one highly variable region, containing more than 70 amino acids, interspersed among well-conserved regions. The rest of the genes in the genome are considered as fully conserved genes.
What causes variable penetrance?
Variable expressivity refers to the range of signs and symptoms that can occur in different people with the same genetic condition. As with reduced penetrance, variable expressivity is probably caused by a combination of genetic, environmental, and lifestyle factors, most of which have not been identified.
What does variably expressed mean?
Variable expressivity refers to the degree in which a genotype is phenotypically expressed. For example, multiple people with the same disease can have the same genotype but one may express more severe symptoms, while another carrier may appear normal.
How is deseq2 used in gene level differential expression analysis?
Modeling is a mathematically formalized way to approximate how the data behaves given a set of parameters (i.e. size factor, dispersion). DESeq2 will use this formula as our model for each gene, and fit the normalized count data to it.
What kind of experimental design does deseq2 use?
A full description of the experimental design can be found at array express and the expression atlas. DEseq2 is a popular differential expression analysis package available through Bioconductor. Its differential expression tests are based on a negative binomial generalized linear model.
How to test for differential expression in RNAseq?
One of the aim of RNAseq data analysis is the detection of differentially expressed genes. The package DESeq2 provides methods to test for differential expression analysis. This document presents an RNAseq differential expression workflow.
What kind of input data is used in deseq2?
Input data for DEseq2 consists of non-normalized sequence read counts at either the gene or transcript level. No preliminary normalization of this data is needed. DEseq2 will internally corrects for differences in library size, using the raw counts. The tool HTseq can be used to obtain this information and is what was used for our example data.