Hi, I am new in the field of deep sequencing analysis and for the past two months I am struggling to understand RNA seq differential expression studies.
I have 1 control sample and 1 sample for each of the three different experimental conditions. No technical replicates. So I guess I cannot apply some GLM approach or estimate mean and variances from the replicates.
I chose to normalize with applying a simple linear correction after fiiting the log # of reads ( condition x vs control) and then to calculate the significance of the Differential expression by calculating a z score for each gene by using my control sample to estimate per gene mean and variance (which have practically the same values). The p values that I get are extremely low even after bonferoni correction (eg top 1000 pvalue still less 1e-100). This is quite counter intuitive for interpretation. Any other suggestions for better normalizing or studying DE?? thanks
I have 1 control sample and 1 sample for each of the three different experimental conditions. No technical replicates. So I guess I cannot apply some GLM approach or estimate mean and variances from the replicates.
I chose to normalize with applying a simple linear correction after fiiting the log # of reads ( condition x vs control) and then to calculate the significance of the Differential expression by calculating a z score for each gene by using my control sample to estimate per gene mean and variance (which have practically the same values). The p values that I get are extremely low even after bonferoni correction (eg top 1000 pvalue still less 1e-100). This is quite counter intuitive for interpretation. Any other suggestions for better normalizing or studying DE?? thanks
Comment