R Tutorial : Differential Gene Expression Overview

Key Takeaways

differential gene expression analysis is a powerful technique to determine whether genes are expressed at significantly different levels between two or more sample groups we will use the Dec to package to model the gene counts and identify differentially expressed genes listen and we see a heat map of genes as rose-coloured by number of counts these genes represent the genes with large expression differences or full of changes between sample groups to determine which genes are differentially expressed one might ask why not just identify the genes with the largest full changes in expression between sample groups to get the answer let's observe the plot of normalized counts for gene a the points represent the gene expression levels for five biological replicates for untreated and treated conditions the mean expression for the tria condition is over twice that of the untreated however there appears to be greater variation in the treated condition and the difference in expression may not be significant we need to account for variation in the data when we determine what their genes are differentially expressed therefore the goal of differential expression analysis is to determine for each gene whether the difference is an expression between groups is significant given the amount of variation within groups or between the biological replicates to explore the workflow we'll be using a publicly available RNA seek data set from Jared do seek at all from the journal JCI insight in this paper the goal of the RNA seek experiment was to explore why mice overexpressing the SMAW true gene or producing more Smok to RNA than normal are more likely to develop kidney fibrosis smack to you or secreted modular calcium binding protein 2 has been shown to have increased expression in kidney fibrosis which is characterized by an excess of extracellular matrix in the space between tubules and capillaries within the kidney however is unknown house mark 2 functions in the induction and progression of fibrosis there are four sample groups being tested normal control mice referred to as wild-type mice with and without fibrosis and smack to overexpressing mice with and without fibrosis there are three biological replicates for all normal samples and four replicates for all fibrosis samples initially we will explore the effective fibrosis and gene expression using wild-type samples during lectures and smack to overexpression data during exercises to test whether the expression of genes between two or more groups is significantly different we need an appropriate statistical model an appropriate statistical model is determined by the count distribution when we plot the distribution of counts for a single sample we can visualize key features of rna-seq data including a large proportion of genes with low counts and many genes with zero counts also note the long right tail which is due to there being no limit for max expression in RNA seek data if there was no expression variation between biological replicates a frequently used count distribution known as the Poisson distribution would be an appropriate model but there is always biological variation and this additional variation president RNA seek data can be modeled well using a negative binomial model which we will be using as part of Dec 2 to the B a data frame with gene IDs as row names and sample names as column names each cell represents the number of reads aligned to the corresponding gene for a given sample in addition to our raw counts we require a sample metadata at the very least we need to know which of our samples correspond to each condition to generate our metadata we create a vector for each column and combine the vectors into a data frame the sample names are added as the row names after we have the count and metadata files we can start the differential expression analysis workflow let's practice exploring counts and getting our files ready for an