API reference¶
I/O module¶
limix.io.bgen.read(filepath[, size, …]) |
Read a given BGEN file. |
limix.io.bimbam.read_phenotype(filepath) |
Read a BIMBAM phenotype file. |
limix.io.bimbam.see_phenotype(filepath) |
Shows a summary of a BIMBAM phenotype file. |
limix.io.csv.read(filename[, sep, header]) |
Read a CSV file. |
limix.io.csv.see(filepath, header[, verbose]) |
Shows a human-friendly representation of a CSV file. |
limix.io.gen.read(prefix) |
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limix.io.hdf5.fetch(fp, path) |
Fetches an array from hdf5 file. |
limix.io.hdf5.fetcher(filename) |
Fetch datasets from HDF5 files. |
limix.io.hdf5.read_limix(filepath) |
Read the HDF5 limix file format. |
limix.io.hdf5.see(f_or_filepath[, …]) |
Shows a human-friendly tree representation of the contents of a hdf5 file. |
limix.io.npy.read(filepath[, verbose]) |
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limix.io.npy.save(filepath, X[, verbose]) |
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limix.io.npy.see(filepath[, verbose]) |
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limix.io.plink.read(prefix[, verbose]) |
Read PLINK files into Pandas data frames. |
limix.io.plink.see_bed(filepath, verbose) |
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limix.io.plink.see_kinship(filepath, verbose) |
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limix.io.plink.fetch_dosage(prefix, verbose) |
Quality control¶
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) |
Column-wise impute NaN values by column mean. |
limix.qc.mean_standardize(X[, axis, out]) |
Zero-mean and one-deviation normalisation. |
limix.qc.normalise_covariance(K[, out]) |
Variance rescaling of covariance matrix K. |
limix.qc.quantile_gaussianize(x) |
Normalize a sequence of values via rank and Normal c.d.f. |
limix.qc.regress_out(Y, X[, return_b]) |
Regresses out X from Y |
limix.qc.remove_dependent_cols(X[, tol, verbose]) |
Remove dependent columns. |
limix.qc.unique_variants(X) |
Filters out variants with the same genetic profile. |
Statistics¶
limix.stats.allele_expectation(p, nalleles, …) |
Allele expectation. |
limix.stats.allele_frequency(expec) |
Compute allele frequency from its expectation. |
limix.stats.Chi2Mixture([scale_min, …]) |
A class for continuous random variable following a chi2 mixture. |
limix.stats.compute_dosage(expec[, 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.effsizes_se(effsizes, pvalues) |
Standard errors of the effect sizes. |
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.pca(X, ncomp) |
Principal component analysis. |
Heritability estimation¶
limix.her.estimate(y, lik, K[, M, verbose]) |
Estimate the so-called narrow-sense heritability. |
Quantitative trait loci¶
limix.qtl.scan(G, y, lik[, K, M, verbose]) |
Single-variant association testing via generalised linear mixed models. |
limix.qtl.QTLModel(null_lml, alt_lmls, …) |
Result of a QTL analysis. |
Plotting & Graphics¶
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() |
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limix.plot.show() |
Generalised Linear Mixed Models¶
limix.glmm.GLMMComposer.covariance_matrices |
Get the covariance matrices. |
limix.glmm.GLMMComposer.decomp() |
Get the fixed and random effects. |
limix.glmm.GLMMComposer.fit([verbose]) |
Fit the model. |
limix.glmm.GLMMComposer.fixed_effects |
Get the fixed effects. |
limix.glmm.GLMMComposer.likname |
Get likelihood name. |
limix.glmm.GLMMComposer.lml() |
Get the log of the marginal likelihood. |
limix.glmm.GLMMComposer.y |
Get the outcome array. |
Shell utilities¶
limix.sh.filehash(filepath) |
Compute sha256 from a given file. |
limix.sh.download(url[, dest, verbose, force]) |
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limix.sh.extract(filepath[, verbose]) |
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limix.sh.remove(filepath) |