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Removing unwanted variation in machine learning for personalized medicine
Published on Jul 18, 20161219 Views
Machine Learning for Personalized Medicine will inevitably build on large omics datasets. These are often collected over months or years, and sometimes involve multiple labs. Unwanted variation (UV) c
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Chapter list
Removing Unwanted Variation in Machine Learning for Personalized Medicine00:00
Apology, Motivation and Declaration of Conflict of Interest02:27
Afirma - 103:15
Afirma - 205:21
Introduction to our RUV methods05:42
The problem06:03
Artifact can overwhelm biology06:19
Some scientific goals sought using gene expression microarrays07:59
Aim for today08:15
“Our” model (brief refs later)08:36
Concrete example13:37
Our model in pictures14:48
Our goal: classification16:40
Our model, 216:57
Some ways of dealing with these and related problems with microarrays17:21
Identifiability: we don’t know the correlation of W (k=1) with X19:13
We might have genes j not affected by X - 120:34
We might have genes j not affected by X - 221:06
“Our” solution: Use control genes22:45
Using the negative controls c24:42
Introducing the two-step: RUV-226:51
Removing severe batch effects32:10
Standard analysis32:31
A microarray experiment with central retina tissue from the rd1 mouse35:03
Analysis with RUVinv35:25
Are there any questions?41:53
Introducing RUV-inv44:29
Classification44:53
What is unwanted variation? - 147:30
What is unwanted variation? - 249:36
Hypothetical example49:53
The challenge of non-stationarity54:06
An interesting point54:27
Removing unwanted variation from the test (target) set55:26
Model for the training set data55:57
Model for the test and target set56:20
Goal57:52
How to proceed?58:27
Method A start with αˆ01:00:43
Digression: some calculations01:02:15
Method B, start with ˆβ01:03:16
Comparing and contrasting Methods A and Method B01:04:18
Choice of classifier01:06:34
Advantage of P(B)01:10:47
Cleaning up the training set01:11:02
Cross Normalization01:16:01
How does it work? Simulations01:16:19
Removing Unwanted Variation makes it possible to use “simple” classifiers01:16:24
Why we might be able to stick to “simple” classifiers?01:16:39
Example01:16:41
Gender differences in the brain01:16:46
Ex: gender differences in the brain01:17:44
Principal component01:17:54
Same plot with gender indicated01:18:59
Same plot with brain regions indicated01:19:20
Ex: gender differences in the brain, 201:19:35
Estimated accuracy rates01:21:36
Making this work for personalized medicine01:26:26
Here are a couple of thoughts01:26:29