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Singular Enrichment Analysis

Project description

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pbsea: Pandas Based Singular Enrichment Analysis

Installation

The package works on Python 3.

Using pip

pip install pbsea

Using GitHub

pip install -e git+https://github.com/ArnaudBelcour/pbsea.git@master#egg=pbsea

Singular Enrichment Analysis

Analysis

An enrichment analysis compares the occurrence of an entity in a list of interest to the occurence in a reference (for example the number of cat in one country compared to the number of cat in the world).

Two files are used : pbsea.py (Pandas Based Singular Enrichment Analysis), preprocessing.py.

In pbsea.py, three class are present:

  1. PandasBasedEnrichmentAnalysis: to peform a Singular Enrichment Analysis on a pandas dataframe.
  2. AnnotationEnrichmentAnalysis: to perform a Singular Enrichment Analysis on Annotation terms.
  3. EnrichmentAnalysisExperimental: to perform a Singular Enrichment Analysis on everything with SgoF multiple testing correction.

In preprocessing.py, four functions are present:

  1. preprocessing_files: create a pandas dataframe from two files.
  2. go_translation_dictionary_creation: create a dictionary containing GO number as key and GO label as value.
  3. ec_translation_dictionary_creation: create a dictionary containing EC number as key and EC name as value.
  4. interpro_translation_dictionary_creation: create a dictionary containing InterPro id as key and InterPro name as value.

The analysis take two input files (with preprocessing) or a pandas dataframe, with two columns (one for the interest values, one for the reference values and in index the object to analyze (e.g. GO terms)).

The script performs a Singular Enrichment Analysis. This analysis takes a list of genes (for example differentially expressed genes) and compute an enrichment term for each annotation term in this list. For a better definition, read the article writed by Huang et al. (2009).

The first class (“PandasBasedEnrichmentAnalysis”) is the basic method on pandas dataframe, which computes an hypergeometric test for variables and calculates different multiple tests corrections (Bonferroni, Holm, Sidak, Benjamini & Hochberg and SGoF).

The second class (“AnnotationEnrichmentAnalysis”) inherits from “PandasBasedEnrichmentAnalysis” and overrides a function to add label for three annotations (GO terms, EC and InterPro domains) to the results.

The third class (“EnrichmentAnalysis”) peforms an analysis and add the SGoF multiple testing correction.

Tests used :

  • Hypergeometric test to compare the distribution of GO terms in your list and in the complete organism.
  • Normal approximation when using big numbers.

Multiple Testing Correction

  • Bonferroni Correction Dunn, Olive Jean. “Multiple Comparisons Among Means.” Journal of the American Statistical Association, vol. 56, no. 293, 1961, pp. 52–64.
  • Sidak Sidak, Zbynek. “Rectangular Confidence Regions for the Means of Multivariate Normal Distributions.” Journal of the American Statistical Association, vol. 62, no. 318, 1967, pp. 626–633.
  • Holm Holm, Sture. “A Simple Sequentially Rejective Multiple Test Procedure.” Scandinavian Journal of Statistics, vol. 6, no. 2, 1979, pp. 65–70.
  • Benjamini & Hochberg Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society. Series B (Methodological), vol. 57, no. 1, 1995, pp. 289–300.
  • Benjamini & Yekutieli Benjamini, Yoav, and Daniel Yekutieli. “The Control of the False Discovery Rate in Multiple Testing under Dependency.” The Annals of Statistics, vol. 29, no. 4, 2001, pp. 1165–1188.
  • SGoF Carvajal-Rodríguez, Antonio, Jacobo de Uña-Alvarez, and Emilio Rolán-Alvarez. “A New Multitest Correction (SGoF) That Increases Its Statistical Power When Increasing the Number of Tests.” BMC Bioinformatics 10 (2009): 209.

Use

The input of the analysis is a pandas dataframe with two columns (occurrence in interest and occurrence in reference) and an index (the object to analyse) like this one:

index Occurrence in interest Occurrence in reference
GO:00000001 2 4
GO:00000002 10 20

For GO terms, EC and InterPro id you can use one of the function of translation dictionary creation to obtain a dictionary allowing you to translate the id into name.

from pbsea import PandasBasedEnrichmentAnalysis

number_gene_interest = 5
number_gene_reference = 6700
alpha = 0.05
normal_approximation_threshold = 100000

analysis = PandasBasedEnrichmentAnalysis(dataframe, 'Occurrence in interest',
                        'Occurrence in reference', number_gene_interest, number_gene_reference,
                        alpha, normal_approximation_threshold)
result_dataframe = analysis.enrichment_analysis()

The result will be a pandas dataframe.

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