What is Machine Learning

Table of contents

1. Key learning outcomes
2. ML/LLM Basic
3. Relevant Biostatistcs
4. Linux: Learn it, Love it
5. ML/LLM Training - Image Analysis

Key learning outcomes

In this module we will focus on the basics - how to use linux, basic relevant biostatistical theory (data, statistical approaches, etc), and what machine learning is (and importantly, is not) conceptually.

ML/LLM: The Basics

To be added.

Questions?

• To be added

Conceptual Exercise: PCA

Principle component analysis is a useful technique that allows researchers to visualize high dimensional data in lower space by rotating the axes in such a way that the lower dimensions (or components) maximize the total variance explained. In statistical genetics this involves "rotating" million-dimensional data - something that is very hard to visualize! For this reason, we begin with a simpler exercise. For the following three two dimensional shapes, spend some time identifying the principle components or sketching the line across for which variance is maximized. Check your answers below:

❓QUESTIONS:

Below you can view the shapes in principal component space.

Now that we understand how PCA works in two dimensions we will consider a higher dimensional example. In the three dimensional space below, see if you can visualize a plane that maximizes the variance across two dimensions:

Did you get it right? If so, realize that this is equivalent to what we do in genetics - we find rotate the data through millions of dimensions of space to find the plane that maximizes the variance in two dimensions:

To run PCA with real data please enter the exercise3 directory, and type the following command to run PCA on the 1000 Genome data:

./code/plink --bfile data/chr1-22 --indep-pairwise 250 25 0.1 --maf 0.1 --threads 30 --out chr1-22.ldpruned_all_1kgv2
./code/plink --bfile data/chr1-22 --extract chr1-22.ldpruned_all_1kgv2.prune.in  --pca --threads 30

This will generate the principal components that maximize the variance in the data. To plot the result run the following commands from with an R-terminal:

require('RColorBrewer')
options(scipen=100, digits=3)
eigenvec <- read.table('plink.eigenvec', header = F, skip=0, sep = ' ')
rownames(eigenvec) <- eigenvec[,2]
eigenvec <- eigenvec[,3:ncol(eigenvec)]
colnames(eigenvec) <- paste('Principal Component ', c(1:20), sep = '')

PED <- read.table("data/all_phase3.king.psam", header = TRUE, skip = 0, sep = '\t')
PED <- PED[which(PED$IID %in% rownames(eigenvec)), ]
PED <- PED[match(rownames(eigenvec), PED$IID),]

PED$Population <- factor(PED$Population, levels=c("ACB","ASW","ESN","GWD","LWK","MSL","YRI","CLM","MXL","PEL","PUR","CDX","CHB","CHS","JPT","KHV","CEU","FIN","GBR","IBS","TSI","BEB","GIH","ITU","PJL","STU"))

col <- colorRampPalette(c("yellow","yellow","yellow","yellow","yellow","yellow","yellow","forestgreen","forestgreen","forestgreen","forestgreen","grey","grey","grey","grey","grey",
"royalblue","royalblue","royalblue","royalblue","royalblue","black","black","black","black","black"))(length(unique(PED$Population)))[factor(PED$Population)]

project.pca <- eigenvec
par(mar = c(5,5,5,5), cex = 2.0,cex.main = 7, cex.axis = 2.75, cex.lab = 2.75, mfrow = c(1,2))

plot(project.pca[,1], project.pca[,2],
     type = 'n',
     main = 'A',
     adj = 0.5,
     xlab = 'First component',
     ylab = 'Second component',
     font = 2,
     font.lab = 2)
points(project.pca[,1], project.pca[,2], col = col, pch = 20, cex = 2.25)
legend('bottomright',
       bty = 'n',
       cex = 3.0,
       title = '',
       c('AFR', 'AMR', 'EAS',
         'EUR', 'SAS'),
       fill = c('yellow', 'forestgreen', 'grey', 'royalblue', 'black'))

plot(project.pca[,1], project.pca[,3],
     type="n",
     main="B",
     adj=0.5,
     xlab="First component",
     ylab="Third component",
     font=2,
     font.lab=2)
points(project.pca[,1], project.pca[,3], col=col, pch=20, cex=2.25)

❓QUESTIONS:

Back to table of contents

Basic Biostats

To be added

Questions?

• To be added Back to table of contents

Linux Primer

To be added

Questions?

• To be added Back to table of contents

ML/LLM Training

To be added

Questions?

• To be added Back to table of contents