AllenBrainPy

Python package to analyze Allen Human Brain genes expression

Installation

Navigate to directory where you want to save this package and clone this repository: git clone https://github.com/roshchupkin/AllenBrainPy.git

Download Allen Human Brain Atlas data

Make two directories in your project folder, one for MRI data and for expression data. In config.py script change DATA_DIR to path to expression directory and MRI_DIR to path to MRI directory.

Expression Data

You can download data from Allen Brain Institute or from this dropbox link. Save all csv table in separate folder for every donor in your expression directory.

MRI data

Original MRI T1 images were transformed to template MNI space. You can download find all transformed image and template from this link. Save all of them in your MRI directory.

Update

You can update to the newest version using git. Navigate to your folder (where you cloned git repository):
git pull

Usage

Python script to analyze VBM results and Allen Human Brain Atlas of gene
expression

optional arguments:
  -h, --help            show this help message and exit
  -o O                  path to save result folder
  -model {cluster_expression,correlation}
                        Analysis models
  -i I                  path input nifti image of VBM result map
  -d {all,caucasian}    choose all donors or only caucasian
                        (choices=['all','caucasian'])
  -donor DONOR [DONOR ...]
                        choose donor(s)
  -threshold THRESHOLD  value threshold to form clusters from VBM result map
  -cl_size_threshold CL_SIZE_THRESHOLD
                        cluster size threshold to form clusters from VBM
                        result map
  -dist_threshold DIST_THRESHOLD
                        threshold for distance in voxels to link sample to
                        clusters
  -map_type {p-value,t-stat}
                        Type of VBM result map. For p-value clusters will be
                        formed for voxels < threshold,for t-stat clusters will
                        be formed for voxels > threshold, therefore first
                        split your image to negative and positive t-stat,save
                        as two maps with abs values and then run analysis
                        separately for both images.You can easily extrapolate
                        these two map types to any other result map.
  -result_name RESULT_NAME
                        name for saving results
  -probe_mode {all,best,mean}
                        gene name for expression analysis
  -plot                 plot boxplot of gene expression
  -gene_names GENE_NAMES [GENE_NAMES ...]
                        gene name for expression analysis

Citation

G.Roshchupkin et al. Fine-mapping the effects of Alzheimer's disease risk loci on brain morphology.