MultiOmics

class openomics.MultiOmics(cohort_name, omics_data=None)

Bases: object

A data object which holds multiple -omics data for a single clinical cohort.

Methods Summary

add_clinical_data(clinical, **kwargs)	Add a ClinicalData instance to the MultiOmics instance.
add_omic(omic_data[, init_annotations])	Adds an omic object to the Multiomics such that the samples in omic matches the samples existing in the other omics.
build_samples([agg_by])	Running this function will build a dataframe for all samples across the different omics (either by a union or intersection).
get_omics_list()
load_data(omics[, target, ...])	Prepare the multiomics data in format
match_samples(omics)	Return the index of sample IDs of the intersection of samples from all modalities
print_sample_sizes()
remove_duplicate_genes()	Removes duplicate genes between any omics such that the gene index across all omics has no duplicates.

Methods Documentation

add_clinical_data(clinical, **kwargs)

Add a ClinicalData instance to the MultiOmics instance.

Parameters

• clinical (openomics.clinical.ClinicalData) –

• **kwargs –

add_omic(omic_data, init_annotations=True)

Adds an omic object to the Multiomics such that the samples in omic matches the samples existing in the other omics.

Parameters

• omic_data (Expression) – The omic to add, e.g., MessengerRNA, MicroRNA, LncRNA, etc.

• init_annotations (bool) – default True. If true, initializes the annotation dataframe in the omic object

build_samples(agg_by='union')

Running this function will build a dataframe for all samples across the different omics (either by a union or intersection). Then,

Parameters

agg_by (str) – [“union”, “intersection”]

get_omics_list()

load_data(omics, target=['pathologic_stage'], pathologic_stages=None, histological_subtypes=None, predicted_subtypes=None, tumor_normal=None, samples_barcode=None, remove_duplicates=True)

Prepare the multiomics data in format

Parameters

• omics (list) – A list of the data modalities to load. Default “all” to select all modalities

• target (list) – The clinical data fields to include in the

• pathologic_stages (list) – Only fetch samples having certain stages in their corresponding patient’s clinical data. For instance, [“Stage I”, “Stage II”] will only fetch samples from Stage I and Stage II patients. Default is [] which fetches all pathologic stages.

• histological_subtypes – A list specifying the histological subtypes to fetch. Default is [] which fetches all histological sybtypes.

• predicted_subtypes – A list specifying the histological subtypes to fetch. Default is [] which fetches all histological sybtypes.

• tumor_normal – [“Tumor”, “Normal”]. Default is [], which fetches all tumor or normal sample types.

• samples_barcode – A list of sample’s barcode. If not None, only fetch data with matching samples provided in this list.

• remove_duplicates (bool) – If True, only selects samples with non-duplicated index.

Returns

Returns (X, y), where X is a dictionary containing the multiomics data with matched samples, and y contain the :param target: labels for those samples.

Return type

Tuple[Dict[str, pd.DataFrame], pd.DataFrame]

match_samples(omics)

Return the index of sample IDs of the intersection of samples from all modalities

Parameters

omics – An array of modalities

Returns

An pandas Index list

Return type

matched_sapmles

Return type

Index

print_sample_sizes()

remove_duplicate_genes()

Removes duplicate genes between any omics such that the gene index across all omics has no duplicates.