Skip to content

11. From A Notebook

Goal: get exactly the arrays the app draws into Python, for statistics or custom figures, without re-implementing window resolution, track matching, haplotype remapping or group aggregation.

genomics.visualizer.client talks to a running app. It needs only the standard library and numpy, and it waits on the background jobs that the heavier requests start.

from genomics.visualizer.client import Visualizer

v = Visualizer("http://127.0.0.1:8780", dataset="1kg_high_coverage")
start, end = 247_235, 277_052                   # TYRP1 gene body (window offsets)

means = v.group_means("TYRP1", "rna_seq", field="superpopulation", tracks=[1], start=start, end=end, bins=600)
ref = v.reference_signal("TYRP1", "rna_seq", tracks=[1], start=start, end=end, bins=600)
obs = v.observed("TYRP1", "rna_seq", tracks=[1], start=start, end=end, bins=600)

means["edges"]                                  # bin edges (bins + 1,)
means["groups"][0]["group"], means["groups"][0]["mean"]   # 'AFR', array (tracks, bins)
obs["tracks"][0]["provider"], obs["tracks"][0]["mean"]    # 'ENCODE', array (bins,)

A dozen lines of matplotlib on those arrays give this figure:

Matplotlib figure: TYRP1 group means by superpopulation, reference genome, and observed ENCODE signal
Made in a notebook from group_means, reference_signal and observed. These are the numbers behind the Tracks views of step 3.

You don't have to write these calls by hand. On the Tracks page, Export → Copy as Python copies the call for the view on screen, with its gene, tracks, range, binning, haplotypes or groups and cohort filters filled in.

Method Returns
datasets, summary, genes, samples, cohort the dataset, its windows and its sample table (with region scalars and PC fields). samples() is a pandas DataFrame when pandas is installed
signal, reference_signal, observed per-haplotype predictions, the reference-window prediction, and the measured ENCODE / FANTOM5 signal
group_means mean ± SD per group of a sample field over the cohort
variant_sites, variant_effect, genotypes cohort sites, the in-silico eQTL at one site, genotype counts
scalars, pca region-scalar definitions and the genotype PCA
sessions, session saved views, to reproduce a figure's exact view
products, products_fasta, report, expression the Gene products page: transcripts and proteins per haplotype, its report, and mRNA/protein against the reference per tissue

That is the end of the tour. From here: