def make_spikein_per_transcript_plot(quantifications, library_id, quantification='FPKM'):
spikein_cpc = pandas.DataFrame(models.get_single_spike_cpc(), columns=['copies'])
library = quantifications[library_id]
spikes = library[library.index.isin(spikein_cpc.index)]
copies_vs_quant = pandas.concat([spikein_cpc, spikes], axis=1)
source = ColumnDataSource(
data=dict(
x = copies_vs_quant['copies'],
y = copies_vs_quant[library_id],
desc = copies_vs_quant.index,
)
)
hover = HoverTool(
tooltips=[
('index', '$index'),
('transcripts', '$x'),
('fpkms', '$y'),
('desc', '@desc'),
]
)
plot = figure(
title="Spike-in for library {}".format(library_id),
tools=["pan","wheel_zoom","reset","resize",hover]
)
plot.xaxis.axis_label = "transcripts spiked in"
plot.yaxis.axis_label = "RNA-Seq RSEM ({})".format(quantification)
plot.circle('x', 'y', source=source)
return plot
def make_spikein_variance_plot(quantifications, experiments, experiment, quantification='FPKM'):
spikein_cpc = models.get_single_spike_cpc().sort(inplace=False)
library_ids = experiments[experiment]
libraries = quantifications[library_ids]
spikes = libraries[libraries.index.isin(spikein_cpc.index)]
spikes_sorted = spikes.reindex(spikein_cpc.index)
plot = BoxPlot(
spikes_sorted.T,
title="Spike-in variance for experiment {}".format(experiment),
outliers=True,
xlabel="spike-in",
ylabel="RNA-Seq RSEM ({})".format(quantification),
width=900,
)
return plot