ProteinNetworks 0.1.3

Module for working with protein networks (gene ontology, enrichment, protein-protein interactions, etc.)

ProteinNetworks

The library contains convenient tools for rapid analysis of gene ontology, enrichment and protein-protein interaction data. Based on the stringdb library. Some features require you to install R to work (see EnrichmentAnalysis.prioretizingGO())

The module will contain 4 sets of tools:

• Enrichment Analysis
• Protein networks Analysis
• Group comparing tools
• Visualization tools

Get Started

pip install -i https://test.pypi.org/simple/ ProteinNetworks==0.1.3

Contents:

• Enrichment Analysis

  • module: ProteinNetworks.STRING_enrichment

    • class: EnrichmentAnalysis

      methods:

      • EnrichmentAnalysis.create_subframe_by_names()
      • EnrichmentAnalysis.drop_duplicated_genes()
      • EnrichmentAnalysis.get_category_terms()
      • EnrichmentAnalysis.get_enrichment()
      • EnrichmentAnalysis.get_genes_by_localization()
      • EnrichmentAnalysis.get_genes_of_term()
      • EnrichmentAnalysis.get_mapped()
      • EnrichmentAnalysis.prioretizingGO()
      • EnrichmentAnalysis.proteins_participation_in_the_category()
      • EnrichmentAnalysis.save_table()
      • EnrichmentAnalysis.show_category_terms()
      • EnrichmentAnalysis.show_enrichest_terms_in_category()
      • EnrichmentAnalysis.show_enrichment_categories()

---

Enrichment Analysis

Contains a set of functions based on the stringdb library for gene ontology analysis and enrichment analysis Look examples in Colab Notebook

ProteinNetworks.STRING_enrichment module

class ProteinNetworks.STRING_enrichment.EnrichmentAnalysis (data, enrichment=None, protein_id_type='UniProtID')

Bases: object

EnrichmentAnalysis class.

• Parameters:
  • data: Dataframe containing the protein ID for analysis. It must contain either a “Gene” or “UniProtID” column’
  • enrichment: Dataframe containing the results of previous enrichment analysis
  • protein_id_type: type of protein ID. Valid Types

static create_subframe_by_names(df, column: str, names: [<class 'list'>, <class 'tuple'>, <class 'set'>], add: str = 'first')

function finds rows in original dataset and returns sub-dataframe including input names in selected column

• Parameters:
  • df – target DataFrame
  • column – the selected column in which names will be searched
  • names – list of target names whose records need to be found in the table
  • add – [‘first’, ‘last’, ‘all’] parameter of adding found rows. ‘first’ - add only the first entry ‘last’ - add only the last entry ‘all’ - add all entries
• Returns: sub-dataframe including input names in selected column

drop_duplicated_genes(silent=False)

function for droppig dublicated genes

• Parameters:
  • subset: (list) Only consider certain columns for identifying duplicates, by default use all columns. return: df of dropped genes

get_category_terms(category: str, term_type: str = 'id')

function returns set of all terms in chosen category

• Parameters:

  • category: Name of category

  • term_type: ‘id’ or ‘description’.

    id - returns terms IDs of category (for example, GO terms)

    description - returns Description of IDs of category

• Returns: set of terms

get_enrichment()

function performs enrichment analysis. Results store in self.enrichment

• Returns: None

get_genes_by_localization(compartments: list, set_operation: str, save=False)

function for getting proteins localized in target compartments. You also can do common set operations under compartments genes

Example: get_genes_by_localization([Nucleus, Cytosol], ‘union’) - return proteins localized in Nucleus or Cytosol

• Parameters:

  • compartments: list of compartments. Will be attention:

    1. Capitalization of letters matters. Get available compartment names by calling get_components_list().

    2. Order of compartments matter if you want to get sets difference.

  • set_operation: operation between sets. This means that the operations will be applied sequentially to all sets from the compartments. [A, B, C], 'intersection' -> A and B and C

    For example:

    get_genes_by_localization([‘Nucleus’, ‘Cytosol’], ‘difference’) - return just nucleus proteins, get_genes_by_localization([‘Cytosol’, ‘Nucleus’], ‘union’) - return cytosol and nucleus proteins. get_genes_by_localization([‘all’, ‘Nucleus’], ‘difference’) - return all proteins except nucleus proteins.

get_genes_of_term(term: str)

function get genes from enrichment table by target term

• Parameters:
  • term: target GO term from column ‘term’ in enrichment table
• Returns: list of genes associated with target term

get_mapped(species=9606)

function makes gene mapping, it finds STRINGids by protein ids. It`s important for future analysis

• Parameters:
  • species: ID of organism. For example, Human species=9606
• Returns: None

prioretizingGO(terms: [<class 'list'>, <class 'set'>], organism='Human', domain='BP')

function for prioretizing GO-terms using R script with GOxploreR package (doi:10.1038/s41598-020-73326-3) See ‘RScript Prioretizing_GO.R’ work with R.4-3.x. Yoy need to add RScript in PATH

If you use this function in google-collab, you will have to install R-packages at the first launch. This may take a long time (up to 20 minutes)

• Parameters:
  • terms – list of GO-terms
  • organism – name of target organism
  • domain – name of domain in GO-graph. Available inputs: ‘BP’ - Biological Process ‘CC’ - Cellular Component “MF” - Molecular Functions
• Returns: list of Prioretized GO terms

proteins_participation_in_the_category(df, category, term_type='id', term_sep='\n')

function check terms that proteins participated and make statistics table

• Parameters:

  • df: target DataFrame

  • category: Name of category

  • term_type: ‘id’ or ‘description’.

    id - returns terms IDs of category (for example, GO terms)

    description - returns Description of IDs of category

  • term_sep: terms connected with each protein will save in one cell. Choose separator beetwen terms

• Returns: None

static save_table(table, name, saveformat='xlsx', index: bool = True)

function for saving DataFrame tables

• Parameters:
  • table: DataFrame
  • name: name of file
  • saveformat: format of saving file: ‘xlsx’ or ‘csv’
  • index: show indexes in saved table?
• Returns: None

show_category_terms(category: str, show: [<class 'int'>, <class 'str'>] = 10, sort_by='genes', save: bool = False, savename='terms', saveformat='xlsx')

function displays all terms and number of associated genes in category

• Parameters:
  • category: Name of category. You can check available category by calling ‘show_enrichment_categories’ method
  • show: “all” or integer number. Number of strings to display
  • sort_by: [“genes”, “term”] - sort by number of genes (by descending) or term names (by ascending)
  • save: Need to save? Choose True. By default, save in .xlsx format
  • savename: work with save=True, name of file
  • saveformat: format of saving file: ‘xlsx’ or ‘csv’
• Returns: None

show_enrichest_terms_in_category(category: str, count: int = 10, sort_by='fdr', save: bool = False, savename='enrichment', saveformat='xlsx')

function shows top-%count of most enriched terms in %category

• Parameters:
  • category: Name of category. You can check available category by calling ‘show_enrichment_categories’ method
  • count: count of terms you need to show
  • sort_by: you can sort target list by one of ‘fdr’, ‘p_value’, ‘number_of_genes’ parameters
  • save: Need to save? Choose True. By default, save in .xlsx format
  • savename: work with save=True, name of file
  • saveformat: format of saving file: ‘xlsx’ or ‘csv’
• Returns: None

show_enrichment_categories()

function shown available enrichment categories for current dataset

• Returns: None