First, you need to clone this repository to use CanDI.

git clone https://github.com/GilbertLabUCSF/CanDI.git

We suggest to use Conda as a package manager and environment management system. You can create a fresh conda environment with all CanDI’s requirements using bellow command:

conda env create -f CanDI/candi.yml -n candi

The python command from CanDI will automatically download and modify datasets.

python CanDI/CanDI/setup/install.py

Downloaded and formatted datasets would organize this way:

.
├── config.ini # modified after Installation
├── depmap
│   ├── CCLE_expression.csv
│   ├── CCLE_fusions.csv
│   ├── CCLE_gene_cn.csv
│   ├── CCLE_mutations.csv
│   ├── CCLE_RNAseq_reads.csv
│   ├── CRISPR_gene_dependency.csv
│   ├── CRISPR_gene_effect.csv
│   └── sample_info.csv
├── genes
│   └── gene_info.csv
└── locations
    └── merged_locations.csv

To import CanDI, your active directory in python must be same as the cloned folder.

from CanDI import candi

OR, you can add path to the CanDI directory if you want to use it from other directories.

import sys
sys.path.append("path-to-candi-directory")

from CanDI import candi

• data : Container for all candi datasets. All access to datasets go through data object.
• Gene : Provides cross dataset indexing from the gene perspective.
• CellLine : Provides cross dataset indexing from the cell line perspective.
• Cancer : Provides cross dataset indexing by a group of cell lines that are all the same tissue.
• Organelle: Provides cross dataset indexing for a group of genes whose proteins localize to the same organelle.
• CellLineCluster : Provides cross dataset indexing for a group of user defined cell lines.
• GeneCluster : Provides cross dataset indexing for a group of user defined genes.