Genomic Data Analysis: Identifying Differentially Expressed Genes

This lecture covers statistical methods for identifying differentially expressed genes in genomic data analysis. It discusses informal methods for gene identification, such as fold change thresholds and QQ plots. The lecture also delves into the challenges of assessing significance in replicated experiments and the use of moderated t-statistics to rank genes based on evidence of differential expression.