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Pelin Gundogdu

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Full license GPL-3.0-only

sigpathmatrix: SIGnaling PATHway/circuit MATRIX generator

sigpathmatrix is a Python package that collects and exports signaling pathway and circuit information. This package contains helper functions that are using to generate the biological prior knowledge detail for the SigPrimedNet [1] network. The curated matrices specify the relationships between genes and corresponding pathway/circuit they belong to.

Purpose

The primary goal of this package is to collect the complex signaling pathway data and simplify in gene × pathway/circuit format. This generated simple representation of the prior biological knowledge is a key component for SigPrimedNet [1] design which empowers the explainability of the network biologically. The information is derived from the HiPathia [2] package and exports the details each individual genes and correcponding pathway/circuit pairs.

References

[1] Gundogdu, P., Alamo, I., Nepomuceno-Chamorro, I. A., Dopazo, J., & Loucera, C. (2023). SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types. Biology, 12(4), 579. https://doi.org/10.3390/biology12040579

[2] Hidalgo, M. R., Cubuk, C., Amadoz, A., Salavert, F., Carbonell-Caballero, J., & Dopazo, J. (2017). High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. Oncotarget, 8(3), 5160–5178. https://doi.org/10.18632/oncotarget.14107

Output

The function returns a pandas.DataFrame where: - Rows: Individual genes - Columns: Represent specific pathways or circuits. - Values: A binary indicator. If a gene is part of that pathway/circuit the value is 1, otherwise it is 0.

Contributing

The open access sigpathmatrix package is shared in GitHub.

License

GPL-3.0 (for detail, please see LICENSE)

USAGE

The main function name is default_pbk_hipathia which calls the corresponding functions to collect and export signaling pathways/circuits details for the given organims. The main function requires 3 parameters which are called -sp or --species for organism detail and the other two are for disease-associated keywords and list, -dk or --disease_keyword and -dl or --disease_list, respectively.

-sp SPECIES, --species SPECIES
            organism detail, such as hsa for homo sapiens, mmu for mus musculus, etc.
-dk DISEASE_KEYWORD, --disease_keyword DISEASE_KEYWORD
            a list of string that contains a disease description, removes disease-related
            pathway if there is a partially matching
-dl DISEASE_LIST, --disease_list DISEASE_LIST
            a list of string that contains the disease name, removes disease-related
            pathway if there is an exact match

❗❗ IMPORTANT INFORMATION

The working directory in terminal should be the parent path of the user’s project folder while the execution is in process.

In addition, during the execution, the package is modify PATH information of PYTHON and R to be able to complete the data collection and exportation successfully. The updates are written into the .env file which also should be located in parent path of the user’s project folder. (please see the folder structure tree for more detail)

Updated .env file will have additional 2 lines as followed,
...
SPN_PYTHON_PATH='[PATH_of_PYTHON_ENVIRONMENT]/bin/> python'
R_HOME='[PATH_of_PYTHON_ENVIRONMENT]/lib/R'
Note, the PYTHON path for the project is defined as SPN_PYTHON_PATH to not to overwrite the user’s setting in case user is already defined theirs. The package is using this defined PYTHON path as selected environment.

This package is executable via command line in terminal. Please see the USAGE_EXAMPLE.md file for more details.

The package comes with a set of built‑in default definitions that are specifically tailored for sigPrimedNet. By design, it supports a data collection for two reference organisms - homo sapiens (hsa) and mus musculus (mmu) - while automatically excluding all disease‑associated pathways. By design, the main function only needs a species detail which can be declared in variable -sp or --species if the user desires to use embedded disease-associated lists.

📌 📌 The user can define theirs tailored experiment by redefininig the organim name and lists of the disease‑associated keyword in .env file. Please see the USAGE_EXAMPLE.md for more detail explanation.

Parameter details

Please check for more details about parameter definition and structure, + Organism Selection + Disease-associated Pathway Elimination + An overview for updated ```.env``` file with customize lists

Organism Selection

The package has an embedded dictionary for two organism (homo sapiens (hsa) and mus musclus (mmu)) which are using in sigPrimedNet analysis.

GA_DICT = {'hsa':'org.Hs.eg.db', 'mmu':'org.Mm.eg.db'}

Note, if the user wants to use different organism rather than hsa and mmu, the information should be added into .env file into GA_DICT_CUSTOM dictionary with a key and proper genome wide annotation detail. One crucial detail in this step is that the genome wide annotation naming should has same as defined in Bioconductor AnnotationData Package.
GA_DICT_CUSTOM = {'[KEY1]':'[GENOME_WIDE_ANNOTATION1]', '[KEY2]':'[GENOME_WIDE_ANNOTATION2]'}

Disease-associated Pathway Elimination

The user can define own custom list. The details of the pre-defined disease-associated list in package as followed,

DISEASE_KEYWORD = ['disease', 'cancer', 'leukemia', 'infection', 'virus'
                   ,'addiction', 'anemia', 'cell carcinoma', 'diabet', 'Hepatitis']
DISEASE_LIST = ['Long-term depression', 'Insulin resistance', 'Measles'
                , 'Amyotrophic lateral sclerosis (ALS)', 'Alcoholism'
                , 'Shigellosis', 'Pertussis', 'Legionellosis', 'Leishmaniasis'
                , 'Toxoplasmosis', 'Tuberculosis', 'Influenza A', 'Glioma', 'Melanoma']

An overview for updated `.env` file with customize lists

The user can have own custom variable definition in .env file but at the end of the execution the the file will have, + Two environment definitions for both PYTHON and R, which are called SPN_PYTHON_PATHand R_HOME respectively, + customize species-genome wide annotation pair definition in GA_DICT_CUSTOM variable, abd + list using in elimination of disease-associated pathway with desired naming. (In given example below, the naming for both lists for pathway naming and keyword are defined as CUSTOM_DISEASE_LIST and CUSTOM_DISEASE_KEYWORD, respectively. )

An example for .env,

....
     [USER'S VARIABLES IF THERE IS ANY]
....
SPN_PYTHON_PATH='[PATH_of_ACTIVE_ENVIRONMENT]/bin/python'
R_HOME='[PATH_of_ACTIVE_ENVIRONMENT]/lib/R'
# DEFINITION of {[SPECIES] : [GENOME_WIDE_ANNOTATION]} PAIRS
GA_DICT_CUSTOM={'rno':'org.Rn.eg.db'}
# DISEASE-ASSOCIATED PATHWAY ELIMINITATION
CUSTOM_DISEASE_LIST='["Long-term depression","Insulin resistance","Measles","Amyotrophic lateral sclerosis (ALS)","Alcoholism","Shigellosis","Pertussis","Legionellosis","Leishmaniasis","Toxoplasmosis","Tuberculosis","Influenza A","Glioma","Melanoma"]'
CUSTOM_DISEASE_KEYWORD='["disease","cancer","leukemia","infection","virus","addiction","anemia","cell carcinoma","diabet","Hepatitis"]'
# AN EMPTY LIST DEFINITION
# ALTERATIVE #1
CUSTOM_DISEASE_LIST_EMPTY="[]"
CUSTOM_DISEASE_KEYWORD_EMPTY="[]"
# ALTERATIVE #2
CUSTOM_DISEASE_LIST_EMPTY2=""
CUSTOM_DISEASE_KEYWORD_EMPTY2=""

FOLDER STRUCTURE (after execution completed)

USER'S PROJECT FOLDER [.] structure after execution completed, (for hsa organism),

.
├── ....
+├── data_spn_helper
+│   ├── processed
+│   │   └── hsa
+│   │       ├── entrez_and_symbol.csv
+│   │       ├── hipathia_details
+│   │       │   ├── hsa03320_gene_list.txt
+│   │       │   ├── ....... [GENE LIST for EACH 
+│   │       │               INDIVIDUAL PATHWAY]
+│   │       │   └── hsa05100_gene_list.txt
+│   │       ├── hipathia_gene_list.csv
+│   │       ├── hipathia_pathway_ids_and_names.csv
+│   │       ├── hsc_pbk_hsa.txt
+│   │       └── hsp_pbk_hsa.txt
+│   └── raw
+│       └── hsa
+│           ├── hipathia_gene_list_all.csv
+│           └── hipathia_pathway_ids_and_names.csv
+├── .env
├── ....
└── ....