Lesson 1: Introduction to Cell Biology
|Key Learning Goals for this Lesson:|
First, let's review the basics so that everybody has the same basic background in biology.
"Genomics" refers to the study of (almost) all the genes in the cell simultaneously. The meaning of "gene" has changed dramatically over the past 20 years. Here we use it to mean any biological relevant piece of DNA in an organism regardless of whether it is directly involved in the protein-building mechanism.
The field of genomics is moving extremely fast. Biology has gone from being 'data poor' to being very 'data rich' practically overnight. There are four main technologies that are driving this. Within the biological tools we have microarrays and sequencing. In terms of informatics tools we have very sophisticated computational tools and we can share data via the Internet.
This course will focus on the statistical analysis of microarray and sequencing data. Our primary computational tools will be R and Bioconductor.
Scientists using these tool have many different objectives. They might want to characterize an organism, know which genes are expressed or are not expressed and understand the pathways through which expression is regulated. Or, they might want to understand a particular process such as the immune response or the development of a seed to a full-grown plant. They may want to understand a disease better. Some are interested in biological variation within a group of organisms and others in how organisms evolve. And, we can also use genomics tools to characterize a sample of mixed organisms (meta-genomics) such as the micro-organisms living in the human gut (the microbiome).
Here is a basic outline of what we will cover in the class.
In terms of biology we need to ask the questions "what are we measuring?" and, "why are we measuring?"
In terms of technology we will take a look at how we are measuring in order to understand the sources of bias and variance, essentially the noise involved.
In terms of statistics this course will cover differential expression and related "differential" analyses.
Throughout, we will be emphasizing reproducible research - using the same data could another researcher reproduce our analysis; using a similar technology and population of organisms could another researcher reproduce our biological inferences? For this we will need to understand how to design our studies, do valid statistical analyses and document our results.