Sample

class missionbio.mosaic.sample.Sample(name: Optional[str] = None, dna: Optional[missionbio.mosaic.dna.Dna] = None, cnv: Optional[missionbio.mosaic.cnv.Cnv] = None, protein: Optional[missionbio.mosaic.protein.Protein] = None, cnv_raw: Optional[missionbio.mosaic.cnv.Cnv] = None, protein_raw: Optional[missionbio.mosaic.protein.Protein] = None, rna: Optional[missionbio.mosaic.rna.Rna] = None, rna_raw: Optional[missionbio.mosaic.rna.Rna] = None)

Container for multiple assays.

Just like the missionbio.mosaic.assay._Assay object, Sample can also be filtered using Python’s slice notation.

It accepts only one key - a list of barcodes, a list of the position of the barcodes, or a Boolean list.

Load the sample.

>>> import missionbio.mosaic.io as mio
>>> sample = mio.load('/path/to/h5')

Selecting the first 100 cells (these aren’t necessarily cells with the highest reads, they’re arbitrary cells).

>>> select_bars = sample.dna.barcodes()[:100]

Slice all assays in the sample.

>>> filtered_sample = sample[select_bars]

Once the analysis is complete, it can be saved and shared using:

>>> mio.save(sample)

Methods

reset([assay])	Resets to the original state.
assays()	Assays present in the sample
raw_counts()	Raw reads present in the sample
split([row])	Split the sample by the given row attribute.

Multi-assay visualizations

heatmap(clusterby[, sortby, drop, flatten, ...])	Multi-assay heatmap.
clone_vs_analyte([analyte, plot_width, ...])	Set of summary plots to give a run overview.
umaps()	Plots umap arrays from assay objects.

Raw count visualizations

assay_scatter([ax, title, highlight])	Plots DNA reads vs Protein reads.
raw_heatmaps()	Protein and DNA read distribution.
read_data()	Plot read statistics of each barcode.