singlet.dataset.correlations

class singlet.dataset.correlations.Correlation(dataset)

Bases: object

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.
• features – 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.

mutual_information(xs, ys)

Mutual information between feature counts and/or phenotypes

Parameters:

• xs (list or string) – Features and/or phenotypes to use as abscissa (independent variable). The string ‘total’ means all features including spikeins and other, ‘mapped’ means all features excluding spikeins and other, ‘spikeins’ means only spikeins, and ‘other’ means only ‘other’ features.
• ys (list or string) – Features and/or phenotypes to use as ordinate (dependent variable). The string ‘total’ means all features including spikeins and other, ‘mapped’ means all features excluding spikeins and other, ‘spikeins’ means only spikeins, and ‘other’ means only ‘other’ features.

NOTE: Mutual information is defined only for discrete or categorical variables and require a decent coverage of all bins or categories because it has p(x)p(y) in the denominator. Feature counts and quantitative phenotypes require binning prior to calculating Mutual information. See CountsTable.bin and SampleSheet.bin for options. This function uses all unique values in the counts and phenotyes as separate bins.