Summary and Setup

This lesson presents basic machine learning concepts featured in Data Analysis for the Life Sciences by Rafael A. Irizarry and Michael I. Love. The lesson is adapted from software for Chapter 9: Practical Machine Learning, which is published under this MIT license. Adaptation was funded by NIH grant 1R25GM141520 awarded to Dr. Gary Churchill at The Jackson Laboratory.

Project organization

  1. Start RStudio.

  2. Create a new project in your Desktop called ml-biomed.

  • Click the File menu button, then New Project.
  • Click New Directory.
  • Click New Project.
  • Type ml-biomed as the directory name. Browse to your Desktop to create the project there.
  • Click the Create Project button.

  1. Use the Files tab to create a data folder to hold the data, a scripts folder to hold your scripts, and a results folder to hold results. Alternatively, you can use the R console to run the following commands for step 3 only. You still need to create a project with step 2.

    dir.create("./data")
dir.create("./scripts")
dir.create("./results")

Data Sets

Download the tissue gene expression data directly from Github and place them in your new data directory.

The data represent RNA expression levels for eight tissues, each with several individuals.

Software Setup

  1. Install R packages and load the libraries.

    install.packages("rafalib", "RColorBrewer", "gplots",
                "UsingR", "class", "caret")
library(rafalib)
library(RColorBrewer)
library(gplots)
library(UsingR)
library(class)
library(caret)

  2. Install and load packages from Bioconductor.

    if (!require("BiocManager", quietly = TRUE))
     install.packages("BiocManager")

BiocManager::install(c("genefilter", "Biobase", "SpikeIn", "hgu95acdf"))
library(genefilter)
library(Biobase)
library(SpikeIn)
library(hgu95acdf)
data(SpikeIn95)