mipathway 0.0.3

MIPath pathway analysis method

Mipath documentation

Installation

pip install mipathway

Package usage

You can find an example of a simple analysis in the vignette.

Main functions

The main function of this package is:

mipath(data, metadata, gene_sets_df, factors, n_neighbors=25)

• data: a pandas DataFrame containing the gene expression data. Rows are samples and columns are genes. Indexes are sample IDs and column names are gene IDs. This should be normalized and preprocessed before input.
• metadata: a pandas DataFrame containing the phenotype annotations against witch the pathways will be scored. Indexes must be the same as data.
• gene_sets_df: a pandas DataFrame containing the pathway annotation. This should be obtained using the parse_gmt function in conjunction with .gmt file. The gene IDs must be the same as data column names. In case of reactome this can also be downloaded directly using the get_reactome helper function.
• factors: a string or list of strings. Each string must be the same as a metadata column name. These are the variables that will be scored against the pathways.
• n_neighbors: an integer parameter that determines the number of neighbors used to construct the graph. Default is 25.

This will return a pandas DataFrame containing the results of the pathway analysis. Indexes are pathway IDs taken from gene_sets_df and column names are factors.

The mipath function contains two steps, which can be run separately for convenience. Their separate functions are:

decompose_pathways(data, gene_sets_df, n_neighbors=25)

which finds the sample modules for each pathway and returns a DataFrame containing the module assignment for each sample and pathway. Indexes are sample IDs and column names are pathway IDs. This is the most resource intensive part of the analysis.

The second step provides the final scores using:

score_factors(decomposed_df, metadata, factors)

where decomposed_df is the result of the decompose_pathways function.

Helper functions

parse_gmt(gmt_path, gene_id_dict_path = None)

• gmt_path: the path to the `.gmt' file to be parsed.
• gene_id_dict_path: the path to a tab separated file. If not None this file will be used to convert gene IDs. It contains two columns, the fist is a list of gene IDs in the original format, and the second the corresponding gene IDs in the desired format.

get_reactome(organism = 'HSA', gene_anot = 'Ensembl')

• organism: a string describing which organism to download the pathways for. Default is HSA for Homo Sapiens
• gene_annot: a string describing which gene annotation scheme to use. Accepts all Reactome schemes and defaults to Ensembl.