Last updated: 2024-04-05
Checks: 2 0
Knit directory: pop_spec_eqtl_ml/
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The aim of my master’s project was to use multi-population eQTL summary statistics to train machine learning (ML) models to predict whether a given eQTL is shared between human populations.
Our overall workflow to build and interrogate machine learning models to predict whether an eQTL is specific or shared between two populations has five major steps (detailed below, and in Figure 1). Firstly (1), we integrate summary statistics from studies performed in the same tissue, but in different populations. Then for each population pair (2), we label the eQTLs in these integrated summary statistics as shared between, or specific to a single population. Next (3), we extract the features of these eQTLs from publicly available datasets. Then, (4) we train a supervised machine learning model to predict our labels (population-specific or shared) using the supplied features. Finally (5), we assess the performance of our model on a held out test set, and investigate which features contribute to the model’s performance using the Leave-One-Feature-Out (LOFO) Importance metric.
To get a brief overview of our method decisions made during this project, or the pipeline steps from data extraction to model building and prediction, see the relevant pages below. For example which runs through the whole pipeline see, example pipeline. Otherwise, our full analysis at each step of the workflow are documented below.
Step 1: Combining eQTL Data Analysis
The first step in this pipeline and our investigation is to combine eQTL summary statistics from multiple studies in the same tissue. Hence, pages in this section contain information about investigations, decisions and final procedures used to combine single population eQTL summary statistics from relevant tissues together. Each page is for a different tissue - where a different set of studies were combined together.
Step 2: Labeling eQTLs as either population-specific or shared
The next step in our pipeline is to use the above combined summary statistics to label each eQTL in these datasets as population-specific (unique to a single population) or population-shared (found / is consistent across populations).
Firstly, we investigated different methods and software to call eQTLs population specific or shared from summary statistics (pages under 2i)). We decided upon using mashr effect sizes to decide whether an eQTL is population-specific or shared.
Step 3: Obtaining features of eQTLs
Once we have a set of eQTLs we have labelled population-specific or shared, we then extract the features of these eQTLs, and it is these features which the machine learning pipeline uses to
Pages in section 3i) contain investigations into the quirks of different features resources / datasets (mostly machine learning model predictors). These pages were used to decide how eQTL features were integrated into the machine learning pipeline.
3i) Investigations into the features of eQTLs for machine learning:
3ii) Finalised feature extraction process
Step 4: Build machine learning models
Pages in this section contain information and investigations into the performance and behaviour of built machine learning models.
For a table of summary information (performance, run details etc.) for each machine model building see ‘Summary Details for all Machine Learning Models’ This page also contains links to analyses and investigations into particular machine learning models. Furthermore, for the most recently produced (and best behaving) models we have a page dedicated to investigating the distribution model ‘specificity scores’
4i) Pre-processing steps
4ii) Model optimisation steps and investigations:
Step 5: Compare population specific and shared eQTLs
Pages in this section contain investigations, and simple comparisons of the features of population specific and shared eQTLs.