Disease-related transcriptomics¶

This page contains descriptions and examples to extract GWAS-implicated gene expression data and project them to cortical and subcortical surfaces. In the following tutorial, we will use epilepsy-related genes (more specifically, genes related to hippocampal sclerosis) as an example, but feel free to replace epilepsy with any other disorder listed below.

Extract disease-related genes¶

As part of the ENIGMA TOOLBOX, users can query pre-defined lists of disease-related genes (obtained from several recently published GWAS), including gene sets for:
↪ attention deficit/hyperactivity disorder
↪ autism spectrum disorder
↪ bipolar disorder
↪ depression
↪ common epilepsies 📸
↪ schizophrenia
↪ tourette’s syndrome

* 📸 indicates disease-related genes used in the code snippets.

Caution ⚠️

Pre-defined gene sets are obtained from individual studies and are liable to be changed. We welcome any suggestions you may have on defining proper disease-related gene sets and are happy to expand this function to include other interesting disorders. Get in touch with us here.

Prerequisites
↪ Fetch gene expression data

>>> from enigmatoolbox.datasets import risk_genes

>>> # Get the names of epilepsy-related genes (Focal HS phenotype)
>>> epilepsy_genes = risk_genes('epilepsy')['focalhs']

>>> # Extract gene expression data for these Focal HS genes
>>> epilepsy_gene_data = genes[genes.columns.intersection(epilepsy_genes)]

% Get the names of epilepsy-related genes (Focal HS phenotype)
epilepsy_genes = risk_genes('epilepsy');
epilepsy_genes = epilepsy_genes.focalhs;

% Extract gene expression data for these Focal HS genes
epilepsy_gene_data = genes(:, ismember(genes.Properties.VariableNames, ...
                           epilepsy_genes));

Visualize disease-related gene expression maps¶

Following up on the above example, we provide a brief example to project gene expression maps to the surface. Once again, we use genes related to hippocampal sclerosis as an example.

Prerequisites
↪ Fetch gene expression data
↪ Extract disease-related gene data

>>> import numpy as np
>>> from enigmatoolbox.utils.parcellation import parcel_to_surface
>>> from enigmatoolbox.plotting import plot_cortical, plot_subcortical

>>> # Compute the mean co-expression across all Focal HS genes
>>> mean_epilepsy_genes = np.mean(epilepsy_gene_data, axis=1)

>>> # Separate cortical (ctx) from subcortical (sctx) regions
>>> mean_epilepsy_genes_ctx = mean_epilepsy_genes[:68]
>>> mean_epilepsy_genes_sctx = mean_epilepsy_genes[68:]

>>> # Map the parcellated gene expression data to our surface template (cortical values only)
>>> mean_epilepsy_genes_ctx_fsa5 = parcel_to_surface(mean_epilepsy_genes_ctx, 'aparc_fsa5')

>>> # Project the results on the surface brain
>>> plot_cortical(array_name=mean_epilepsy_genes_ctx_fsa5, surface_name="fsa5", size=(800, 400), nan_color=(1, 1, 1, 1),
...               cmap='Greys', color_bar=True, color_range=(0.4, 0.6))

>>> plot_subcortical(array_name=mean_epilepsy_genes_sctx, ventricles=False, size=(800, 400),
...                 cmap='Greys', color_bar=True, color_range=(0.4, 0.6))

% Compute the mean co-expression across all Focal HS genes
mean_epilepsy_genes = mean(epilepsy_gene_data{:, :}, 2);

% Separate cortical (ctx) from subcortical (sctx) regions
mean_epilepsy_genes_ctx  = mean_epilepsy_genes(1:68);
mean_epilepsy_genes_sctx = mean_epilepsy_genes(69:end);

% Map the parcellated gene expression data to our surface template (cortical values only)
mean_epilepsy_genes_ctx_fsa5 = parcel_to_surface(mean_epilepsy_genes_ctx, 'aparc_fsa5');

% Project the results on the surface brain
f = figure,
    plot_cortical(mean_epilepsy_genes_ctx_fsa5, 'color_range', ...
                  [0.4 0.6], 'cmap', 'Greys')

f = figure,
    plot_subcortical(mean_epilepsy_genes_sctx, 'ventricles', 'False', ...
                     'color_range', [0.4 0.6], 'cmap', 'Greys')