Functions

Genetic-related tools for Limix..

limix_genetics.maf(X)

Compute minor allele frequencies.

It assumes that X encodes 0, 1, and 2 representing the number of alleles.

Parameters:X (array_like) – Genotype matrix.
Returns:minor allele frequencies.
Return type:array_like
limix_genetics.qqplot(df, figure=None, colors=None, show=True, tools=None, nmax_points=1000, atleast_points=0.01, significance_level=0.01, paper_settings=False, **kwargs)

Plot number of significant hits across p-value thresholds.

Parameters:df (pandas.DataFrame) – Columns label and p-value define labeled curves.

Example

from limix_genetics import qqplot
import pandas as pd
import numpy as np
random = np.random.RandomState(0)

snp_ids = np.arange(1000)

data1 = np.stack((['method1']*1000, random.rand(1000)),
                 axis=1)
df1 = pd.DataFrame(data1, columns=['label', 'p-value'],
                   index=snp_ids)

data2 = np.stack((['method2']*1000, random.rand(1000)),
                 axis=1)
df2 = pd.DataFrame(data2, columns=['label', 'p-value'],
                   index=snp_ids)

df = pd.concat([df1, df2])

qqplot(df)
limix_genetics.hitsplot(df, figure=None, colors=None, show=True, tools=None, min_threshold=1e-05, max_threshold=0.01, paper_settings=False, perc=False, **kwargs)

Plot number of significant hits across p-value thresholds.

Parameters:df (pandas.DataFrame) – Columns label and p-value define labeled curves.

Example

from limix_genetics import hitsplot
import pandas as pd
import numpy as np
random = np.random.RandomState(0)

snp_ids = np.arange(1000)

data1 = np.stack((['method1']*1000, random.rand(1000) * 0.1),
                 axis=1)
df1 = pd.DataFrame(data1, columns=['label', 'p-value'],
                   index=snp_ids)

data2 = np.stack((['method2']*1000, random.rand(1000) * 0.05),
                 axis=1)
df2 = pd.DataFrame(data2, columns=['label', 'p-value'],
                   index=snp_ids)

df = pd.concat([df1, df2])

hitsplot(df)