singlet.dataset.correlations

class singlet.dataset.correlations.Correlation(dataset)

Bases: singlet.dataset.plugins.Plugin

Correlate gene expression and phenotype in single cells

correlate_features_features(features='all', features2=None, method='spearman')

Correlate feature expression with one or more phenotypes.

Parameters:

• features (list or string) – list of features to correlate. Use a string for a single feature. The special string ‘all’ (default) uses all features.
• features2 (list or string) – list of features to correlate with. Use a string for a single feature. The special string ‘all’ uses all features. None (default) takes the same list as features, returning a square matrix.
• method (string) – type of correlation. Must be one of ‘pearson’ or ‘spearman’.

Returns:

pandas.DataFrame with the correlation coefficients. If either

features or features2 is a single string, the function returns a pandas.Series. If both are a string, it returns a single correlation coefficient.

correlate_features_phenotypes(phenotypes, features='all', method='spearman', fillna=None)

Correlate feature expression with one or more phenotypes.

Parameters:

• phenotypes (list of string) – list of phenotypes, i.e. columns of the samplesheet. Use a string for a single phenotype.
• features (list or string) – list of features to correlate. Use a string for a single feature. The special string ‘all’ (default) uses all features.
• method (string) – type of correlation. Must be one of ‘pearson’ or ‘spearman’.
• fillna (dict, int, or None) – a dictionary with phenotypes as keys and numbers to fill for NaNs as values. None will do nothing.

Returns:

pandas.DataFrame with the correlation coefficients. If either

phenotypes or features is a single string, the function returns a pandas.Series. If both are a string, it returns a single correlation coefficient.

correlate_phenotypes_phenotypes(phenotypes, phenotypes2=None, method='spearman', fillna=None, fillna2=None)

Correlate feature expression with one or more phenotypes.

Parameters:

• phenotypes (list of string) – list of phenotypes, i.e. columns of the samplesheet. Use a string for a single phenotype.
• phenotypes2 (list of string) – list of phenotypes, i.e. columns of the samplesheet. Use a string for a single phenotype. None (default) uses the same as phenotypes.
• method (string) – type of correlation. Must be one of ‘pearson’ or ‘spearman’.
• fillna (dict, int, or None) – a dictionary with phenotypes as keys and numbers to fill for NaNs as values. None will do nothing, potentially yielding NaN as correlation coefficients.
• fillna2 (dict, int, or None) – as fillna, but for phenotypes2.

Returns:

pandas.DataFrame with the correlation coefficients. If either

phenotypes or features is a single string, the function returns a pandas.Series. If both are a string, it returns a single correlation coefficient.

correlate_samples(samples='all', samples2=None, phenotypes=None, method='spearman')

Correlate feature expression with one or more phenotypes.

Parameters:

• samples (list or string) – list of samples to correlate. Use a string for a single sample. The special string ‘all’ (default) uses all samples.
• samples2 (list or string) – list of samples to correlate with. Use a string for a single sample. The special string ‘all’ uses all samples. None (default) takes the same list as samples, returning a square matrix.
• method (string) – type of correlation. Must be one of ‘pearson’ or ‘spearman’.
• phenotypes (list) – phenotypes to include as additional features in the correlation calculation. None (default) means only feature counts are used.

Returns:

pandas.DataFrame with the correlation coefficients. If either

samples or samples2 is a single string, the function returns a pandas.Series. If both are a string, it returns a single correlation coefficient.