API reference¶

I/O module¶

`limix.io.bgen.read`(filepath[, …])	Read a given BGEN file.
`limix.io.bimbam.read_phenotype`(filepath[, …])	Read a BIMBAM phenotype file.
`limix.io.csv.read`(filename[, sep, header, …])	Read a CSV file.
`limix.io.gen.read`(prefix[, verbose])	Read GEN files into Pandas data frames.
`limix.io.hdf5.read_limix`(filepath)	Read the HDF5 limix file format.
`limix.io.npy.read`(filepath[, verbose])	Read NumPy arrays saved in a file.
`limix.io.plink.read`(prefix[, verbose])	Read PLINK files into Pandas data frames.

`limix.qc.boxcox`(x)	Box-Cox transformation for normality conformance.
`limix.qc.compute_maf`(X)	Compute minor allele frequencies.
`limix.qc.count_missingness`(X)	Count the number of missing values per column.
`limix.qc.indep_pairwise`(X, window_size, …)	Determine pair-wise independent variants.
`limix.qc.mean_impute`(X[, axis, inplace])	Impute `NaN` values.
`limix.qc.mean_standardize`(X[, axis, inplace])	Zero-mean and one-deviation normalisation.
`limix.qc.normalise_covariance`(K[, out])	Variance rescaling of covariance matrix 𝙺.
`limix.qc.quantile_gaussianize`(X[, axis, inplace])	Normalize a sequence of values via rank and Normal c.d.f.
`limix.qc.remove_dependent_cols`(X[, tol])	Remove dependent columns.
`limix.qc.unique_variants`(X)	Filters out variants with the same genetic profile.

`limix.stats.Chi2Mixture`([scale_min, …])	Mixture of 𝜒² distributions.
`limix.stats.allele_expectation`(p, nalleles, …)	Allele expectation.
`limix.stats.allele_frequency`(X)	Compute allele frequency from its expectation.
`limix.stats.compute_dosage`(X[, alt])	Compute dosage from allele expectation.
`limix.stats.confusion_matrix`(df[, wsize])	Provide a couple of scores based on the idea of windows around genetic markers.
`limix.stats.empirical_pvalues`(xt, x0)	Function to compute empirical p-values.
`limix.stats.linear_kinship`(G[, out, verbose])	Estimate Kinship matrix via linear kernel.
`limix.stats.lrt_pvalues`(null_lml, alt_lmls)	Compute p-values from likelihood ratios.
`limix.stats.multipletests`(pvals[, alpha, …])	Test results and p-value correction for multiple tests.
`limix.stats.multivariate_normal`(random, …)	Draw random samples from a multivariate normal distribution.
`limix.stats.pca`(X, ncomp)	Principal component analysis.

limix.her.estimate(y, lik, K[, M, verbose])

Estimate the so-called narrow-sense heritability.

Variance decompositon through GLMMs.

`limix.qtl.scan`(G, Y[, lik, K, M, idx, A, …])	Multi-trait association and interaction testing via linear mixed models.
`limix.qtl.iscan`(G, y[, lik, K, M, idx, E0, …])	Single-trait association with interaction test via generalized linear mixed models.

`limix.plot.box_aspect`([ax])	Change to box aspect considering the plotted points.
`limix.plot.ConsensusCurve`()	Consolidate multiple curves in a single one.
`limix.plot.image`(file[, ax])	Show an image.
`limix.plot.kinship`(K[, nclusters, img_kws, ax])	Plot heatmap of a kinship matrix.
`limix.plot.load_dataset`(name)	Example datasets.
`limix.plot.manhattan`(data[, colora, colorb, …])	Produce a manhattan plot.
`limix.plot.normal`(x[, bins, nstd, ax])	Plot a fit of a normal distribution to the data in x.
`limix.plot.pca`(X[, pts_kws, ax])	Plot the first two principal components of a design matrix.
`limix.plot.power`(pv[, label, alphas, …])	Plot number of hits across significance levels.
`limix.plot.qqplot`(a[, label, alpha, cutoff, …])	Quantile-Quantile plot of observed p-values versus theoretical ones.
`limix.plot.image`(file[, ax])	Show an image.
`limix.plot.get_pyplot`()	Get `matplotlib.pyplot`.
`limix.plot.show`()

`limix.sh.filehash`(filepath)	Compute sha256 from a given file.
`limix.sh.download`(url[, dest, verbose])	Download file.
`limix.sh.extract`(filepath[, verbose])	Extract a compressed file.
`limix.sh.remove`(filepath)	Remove file.