AHBA_gradients

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Exploring intrinsic gradients of the AHBA

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# AHBA components
Code and data accompanying the article [_Three components of human brain gene expression reflect normative developmental programmes with specific links to neurodevelopmental disorders_](https://www.biorxiv.org/content/10.1101/2022.10.05.510582v2.full).

Python is used for analysis, R is used for plotting.

## Key outputs
Processing of the AHBA draws heavily on prior work by [Aurina Arnatkevičiūtė](https://www.sciencedirect.com/science/article/pii/S1053811919300114?via%3Dihub) and [Ross Markello](https://elifesciences.org/articles/72129).
- [processing.py](code/processing.py): These functions make two changes to the standard _abagen_ pipeline:
    1. Samples are filtered for left-hemisphere, cortical-only samples *before* all other steps (e.g. probe selection). This was found to increase generalisability of cortical-only components relative to including subcortical data when selecting probes. This step requires patching (overwriting) one function within the abagen package.
    2. Filtering for more stable genes and more consistently-sampled brain regions.

For convenience we provide the AHBA gene-by-region expression matrix filtered for 50% most stable genes and 3+ donor regions:
- [hcp_3d_ds5.csv](outputs/expression/hcp_3d_ds5.csv)

We also provide the region scores and gene weights of the top three AHBA components from DME:
- [ahba_dme_hcp_top8kgenes_scores.csv](outputs/ahba_dme_hcp_top8kgenes_scores.csv)
- [ahba_dme_hcp_top8kgenes_weights.csv](outputs/ahba_dme_hcp_top8kgenes_weights.csv)

The gene lists used for enrichment tests are all publicly available. For clarity:
- The PGC3 schizophrenia [Extended Prioritized](https://figshare.com/articles/dataset/scz2022/19426775?file=35775617) gene list is included here [data/gwas/trubetskoy2022_extended.csv](data/gwas/trubetskoy2022_extended.csv)
- The consensus lists of differentially expressed genes for ASD, MDD, and SCZ are from the studies in [data/deg](data/deg), compiled using the function [get_deg_consensus](code/disorders_data.py). For convenience the consensus list is provided here [outputs/deg_consensus_genes.csv](outputs/deg_consensus_genes.csv).

## Analysis and figures
Figures and results are presented in jupyter notebooks that walk through the logic of each analysis. 
Figure-specific code files are listed below each notebook with some other common files listed at the end.
- [fig1.ipynb](fig1.ipynb): generalisability summary & normative enrichments 
    - [triplets.py](code/triplets.py), [enrichments.py](code/enrichments.py), [enrichments_data.py](code/enrichments_data.py)
    - [fig1_plots.R](code/fig1_plots.R)
- [fig2.ipynb](fig2.ipynb): spatial associations to normative imaging
    - [maps_data.py](code/maps_data.py), [maps_analysis.py](code/maps_analysis.py), [maps_null_test.py](code/maps_null_test.py)
    - [fig2_plots.R](code/fig2_plots.R)
- [fig3.ipynb](fig3.ipynb): single-cell & developmental analyses
    - [single_cell.py](code/single_cell.py), [brainspan.py](code/brainspan.py)
    - [fig3_plots.R](code/fig3_plots.R)
- [fig4.ipynb](fig4.ipynb): disorder associations
    - [disorders_data.py](code/disorders_data.py), [disorders.py](code/disorders.py)
    - [fig4_plots.R](code/fig4_plots.R)
- [fig_extended.ipynb](fig_extended.ipynb): extended data & supplementary figures
    - [fig_extended.R](code/fig_extended.R)

Common functions: these files include functions used in all the above
- [processing.py](code/processing.py): functions for processing the AHBA with abagen
- [gradientVersion.py](code/gradientVersion.py): class definition for PCA/DME object
- [analysis_helpers.py](code/analysis_helpers.py): general helper functions

Docker - to ease with installation of package dependencies do one of:
1. Pull the docker image [richardajdear/ahba](https://hub.docker.com/repository/docker/richardajdear/ahba/general) and run these analyses in a container (the docker image will automatically start a jupyter lab instance that can be accessed through a browser or an IDE like VScode)
2. Build your own docker image using the [Dockerfile](docker/Dockerfile), or manually install the dependencies listed in [python-reqs.txt](docker/python-reqs.txt) and [r-reqs.R](docker/r-reqs.R)

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