Παρουσίαση/Προβολή

(61229) -  ΠΑΝΑΓΙΩΤΗΣ ΠΑΠΑΣΤΑΜΟΥΛΗΣ

Περιγραφή Μαθήματος

Modern biology is a data-rich science. This course will expose the students to high-throughput biological datasets (such as microarrays, RNA-Seq, ChIP-Seq) and present the main inferential tools to deal with challenges they impose to the statistician. These methods include techniques for

• controlling the False Discovery Rate in multiple testing (such as the Benjamini-Hochberg procedure)
• modelling high-throughput count data (multifactorial designs, generalized linear models)
• performing differential expression analysis in microarray and RNA-Sequencing data
• taking into account heterogeneity in sizeable data (mixture models)
• fitting (frequentist or Bayesian) models specifically designed for estimating gene and transcript expression given a known genome/transcriptome annotation and (big) datasets of short nucleotide reads

The course will mainly use the R programming language, enhanced by the specialized method packages from the Bioconductor project (such as  limma,  DeSeq2, edgeR, BitSeq, rsem-EBSeq). Supplementary command line tools (such as  Bowtie2) will also be used.

 

 

 

 

Ημερομηνία δημιουργίας

Δευτέρα, 1 Φεβρουαρίου 2021