# BioFusion
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
A tool for multimodal biological data integration and analysis with the
help of multilayer networks.
This repository contains code developed during collaboration between
Fujitsu Research of Europe and Barcelona Supercomputing Center.
## Organisation
The directory structure is as follows:
.
|-- data
| |-- GeneCelltypes
| | |-- gene_celltypes_all_common.txt
| | |-- gene_celltypes_all_common_cnv.txt
| | |-- gene_celltypes_all_common_rna.txt
| | |-- gene_celltypes_all_unique.txt
| | |-- gene_celltypes_all_unique_cnv.txt
| | `-- gene_celltypes_all_unique_rna.txt
| |-- MultilayerCommunities
| | |-- <BSC-community-trajectories.tsv>
| | `-- <BSC-distance-matrix.tsv>
| |-- MultilayerGraphs
| | |-- <BSC-MLN-layer-1.json>
| | |-- :
| | `-- <BSC-MLN-layer-5.json>
| |-- TCGA_BRCA_Dic_Hover_files
| | `-- TCGA-E2-A1B6-01A-03-TSC.f0917d61-c963-42cf-86c7-48b1e70c662d.pt
| |-- TopGenesWSI
| | |-- common_genes
| | | |-- box_level
| | | | `-- TCGA-E2-A1B6-01A-03-TSC.f0917d61-c963-42cf-86c7-48b1e70c662d
| | | | `-- stats.csv
| | | `-- wsi_level
| | `-- unique_genes
| | |-- box_level
| | `-- wsi_level
| |-- cnv.csv
| `-- rna.csv
|-- outputs
| |-- TCGA_BRCA_spatial
| |-- TCGA_Gene_Graphs
| `-- TopGenesMLN
|-- scripts
| |-- create_gene_graph.py
| |-- create_gene_list.py
| |-- get_WSI_celltype_weights.py
| `-- get_WSI_gene_info.py
|-- README.md
`-- requirements.txt
## Usage
The Python scripts can be run from the `/scripts` directory after
installing all necessary Python modules as listed in `requirements.txt`.
The following scripts are provided:
`create_gene_list.py` - Description: This script finds the set of genes
that are common between the MLN and the genomic data (CNV or RNA). Files
in the folder that have suffix “\_cnv” and “\_rna” are generated using
this script. - Input: /data/GeneCelltypes, /data/cnv.csv - Output:
/data/GeneCelltypes
`get_WSI_gene_info.py` - This script/module reads top genes from WSI
patches and retrieves gene associations and significant neighbourhood
communities from multilayer network. - Input: /data/TopGenesWSI -
Output: /outputs/TopGenesMLN
`get_WSI_celltype_weights.py` - This script takes WSI Graphs (where
patches correspond to groups of nodes), gene celltype associations, and
bulk-RNA data, and produces heatmaps of approximated spatial gene
expression. - Input: /data/TCGA_BRCA_Dic_Hover_files,
/data/GeneCelltypes, /data/rna.csv - Output: /outputs/TCGA_BRCA_spatial
`create_gene_graph.py` - Description: This script takes the genomic data
(CNV or RNA) and MLN graphs (along with computes Louvain community based
Hamming distance matrix) and generates a hierarchical clustering based
similarity matrix for the genes and a gene graph with edge attributes
reflecting the gene-gene similarities. - Input: /data/cnv.csv,
/data/MultilayerGraphs, /dataa/MultilayerCommunities - Output:
/outputs/TCGA_Gene_Graphs
To run notebooks, please install the package in the editable mode:
pip install -e .
from the package roor directory.
Raw data
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"description": "# BioFusion\n\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\nA tool for multimodal biological data integration and analysis with the\nhelp of multilayer networks.\n\nThis repository contains code developed during collaboration between\nFujitsu Research of Europe and Barcelona Supercomputing Center.\n\n## Organisation\n\nThe directory structure is as follows:\n\n .\n |-- data\n | |-- GeneCelltypes\n | | |-- gene_celltypes_all_common.txt\n | | |-- gene_celltypes_all_common_cnv.txt\n | | |-- gene_celltypes_all_common_rna.txt\n | | |-- gene_celltypes_all_unique.txt\n | | |-- gene_celltypes_all_unique_cnv.txt\n | | `-- gene_celltypes_all_unique_rna.txt\n | |-- MultilayerCommunities\n | | |-- <BSC-community-trajectories.tsv>\n | | `-- <BSC-distance-matrix.tsv>\n | |-- MultilayerGraphs\n | | |-- <BSC-MLN-layer-1.json>\n | | |-- :\n | | `-- <BSC-MLN-layer-5.json>\n | |-- TCGA_BRCA_Dic_Hover_files\n | | `-- TCGA-E2-A1B6-01A-03-TSC.f0917d61-c963-42cf-86c7-48b1e70c662d.pt\n | |-- TopGenesWSI\n | | |-- common_genes\n | | | |-- box_level\n | | | | `-- TCGA-E2-A1B6-01A-03-TSC.f0917d61-c963-42cf-86c7-48b1e70c662d\n | | | | `-- stats.csv\n | | | `-- wsi_level\n | | `-- unique_genes\n | | |-- box_level\n | | `-- wsi_level\n | |-- cnv.csv\n | `-- rna.csv\n |-- outputs\n | |-- TCGA_BRCA_spatial\n | |-- TCGA_Gene_Graphs\n | `-- TopGenesMLN\n |-- scripts\n | |-- create_gene_graph.py\n | |-- create_gene_list.py\n | |-- get_WSI_celltype_weights.py\n | `-- get_WSI_gene_info.py\n |-- README.md\n `-- requirements.txt\n\n## Usage\n\nThe Python scripts can be run from the `/scripts` directory after\ninstalling all necessary Python modules as listed in `requirements.txt`.\n\nThe following scripts are provided:\n\n`create_gene_list.py` - Description: This script finds the set of genes\nthat are common between the MLN and the genomic data (CNV or RNA). Files\nin the folder that have suffix \u201c\\_cnv\u201d and \u201c\\_rna\u201d are generated using\nthis script. - Input: /data/GeneCelltypes, /data/cnv.csv - Output:\n/data/GeneCelltypes\n\n`get_WSI_gene_info.py` - This script/module reads top genes from WSI\npatches and retrieves gene associations and significant neighbourhood\ncommunities from multilayer network. - Input: /data/TopGenesWSI -\nOutput: /outputs/TopGenesMLN\n\n`get_WSI_celltype_weights.py` - This script takes WSI Graphs (where\npatches correspond to groups of nodes), gene celltype associations, and\nbulk-RNA data, and produces heatmaps of approximated spatial gene\nexpression. - Input: /data/TCGA_BRCA_Dic_Hover_files,\n/data/GeneCelltypes, /data/rna.csv - Output: /outputs/TCGA_BRCA_spatial\n\n`create_gene_graph.py` - Description: This script takes the genomic data\n(CNV or RNA) and MLN graphs (along with computes Louvain community based\nHamming distance matrix) and generates a hierarchical clustering based\nsimilarity matrix for the genes and a gene graph with edge attributes\nreflecting the gene-gene similarities. - Input: /data/cnv.csv,\n/data/MultilayerGraphs, /dataa/MultilayerCommunities - Output:\n/outputs/TCGA_Gene_Graphs\n\nTo run notebooks, please install the package in the editable mode:\n\n pip install -e .\n\nfrom the package roor directory.\n",
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