1. For the gene expression data (microarray dataset which is been extracted from the Gene Expression Omnibus (GEO) platform), which of the following normalisation techniques are suggested as the best in order to handle the outliers: quantile, log, z score,… As I was following articles where they were normalising by combining quantile and log, but when I check for the dataset I’m working on, there are outliers which are then negatively skewed after normalising. Is it normal to have skewness even when they are normalised? If not, are there any other ways where we can normalise them without any skewness?
2. I was using the Student t-test and Fold change values, to identify the DEG for two different cores, where I ended up getting 202 genes in total, where 44 are common between these two cores. Is it normal to get some common differentially expressed genes for two different conditions ? If not, what mistake probably would have occured?
3. Any precise formula to calculate the fold change values from the gene expression values? All over the internet, there are plenty of formulas. So, I'm confused about which formula to use.
1. It is not uncommon to get skewness even after normalization of the data. Some degree of skewness is inherent to RNA-seq data due to the natural variation in gene expression.
2. It is normal to get some common differentially expressed genes between two different conditions. This might occur due to perturbation in common biological processes or pathways.
3. You do not require any sophisticated formula to calculate fold change. The simplest way is:
a. For each gene, you calculate the average expression value across all replicates in each condition (conditions 1 and 2).
b. Divide the mean expression in condition 2 by the mean expression in condition 1.