Code Snippets

Code Snippets#

Annotate your variants#

This example shows how you can annotate your variants. Running the command below will output a pandas dataframe.

import missionbio.mosaic as ms
sample = ms.load_example_dataset("3 cell mix")
filtered_variants = list(sample.dna.filter_variants())

sample.dna.get_annotations(filtered_variants[0:5])
Position Ref allele Alt allele Varsome url Variant type RefSeq transcript id Gene Protein cDNA Coding impact Function Allele Freq (gnomAD) DANN dbSNP rsids ClinVar COSMIC ids
Variant ID
chr2:25458546:C/T 25458546.0 C T https://varsome.com/variant/hg19/chr2:25458546... SNV NM_022552.5 DNMT3A DNMT3A:p.p.? c.2597+30G>A intronic 0.513776 0.551042 rs2304429 Benign
chr2:25469502:C/T 25469502.0 C T https://varsome.com/variant/hg19/chr2:25469502... SNV NM_022552.5 DNMT3A DNMT3A:p.L422= c.1266G>A synonymous coding 0.190004 0.802194 rs2276598 Benign
chr2:25470426:C/T 25470426.0 C T https://varsome.com/variant/hg19/chr2:25470426... SNV NM_022552.5 DNMT3A DNMT3A:p.p.? c.1014+34G>A intronic 0.000702 0.635212 rs142243425
chr2:25470573:G/A 25470573.0 G A https://varsome.com/variant/hg19/chr2:25470573... SNV NM_022552.5 DNMT3A DNMT3A:p.R301W c.901C>T missense coding 0.999237 rs1553414070 Conflicting Interpretations Of Pathogenicity
chr2:209113192:G/A 209113192.0 G A https://varsome.com/variant/hg19/chr2:20911319... SNV NM_005896.4 IDH1 IDH1:p.G105= c.315C>T synonymous coding 0.05057 0.680929 rs11554137 Benign

Currently we provide the following annotations:

  • Variant type

  • Gene

  • Gene function

  • RefSeq transcript ID

  • cDNA change

  • Protein change

  • Protein coding impact

  • COSMIC

  • DANN SNVs

  • ClinVar

  • gnomAD

  • dbSNP

If you need additional annotation sources, please contact us.

Multi-assay heatmap#

The following examples demonstrate how to produce a heatmap showing data from multiple assays. The first example shows how to cluster cells by protein expression, then produce a heatmap that shows the per-cluster protein expression and DNA mutation status for some select variants.

import missionbio.mosaic as ms

sample = ms.load_example_dataset("3 cell mix")

# first, cluster by protein expression
sample.protein.normalize_reads()
sample.protein.run_pca(attribute='normalized_counts', components=5)
sample.protein.run_umap(attribute='pca')
sample.protein.cluster(attribute='pca', method='graph-community', k=100)

# show only two DNA variants, and chromosomes 1 and 2 for CNV
fig = sample.heatmap(("protein", "dna", "cnv"), features=(None, sample.dna.ids()[:2], ["1", "2"]))
fig.show("jpg")
../_images/8dec005a67abd2235d0ffc58bebcf802976d2234121bbd70663a18f892a97264.jpg

If you cluster by protein, you can also quantify the percentage and mutated cells in each cluster for your mutation(s) of choice.

import missionbio.mosaic as ms

sample = ms.load_example_dataset("3 cell mix")

# first, cluster by protein expression
sample.protein.normalize_reads()
sample.protein.run_pca(attribute='normalized_counts', components=5)
sample.protein.run_umap(attribute='pca')
sample.protein.cluster(attribute='pca', method='graph-community', k=100)

# show only two DNA variants

fig = sample.signaturemap(("protein", "dna", "cnv"), features=(None, sample.dna.ids()[:2], ["1", "2"]))
fig.show("jpg")
../_images/023c0709a0488c05b6c2fe707ef0fb5793e76e351e6bc15fa96ead6ecf531a98.jpg

Fishplot to visualize clonal evolution#

Draws a fish plot and associated graphical representation of clonal phylogeny. Currently, you must provide the phylogenetic relationships between clones.

import missionbio.mosaic as ms

group = ms.load_example_dataset('Multisample PBMC')
fig = group.fishplot(labels=["Clone 1", "Clone 2"], parents=[None, None])
fig.show("jpg")
../_images/17c668ca89f91d8ac345c5dde260d272f55f9b7f6e563539012170edf101786d.jpg

Group cells by a select number of DNA variants#

Clusters cells into clones based on the provided variants and returns a dataframe of per-clone and per-variant statistics. This algorithm also takes into consideration allele dropout out (ADO) to identify potential false positive clones.

import missionbio.mosaic as ms

sample = ms.load_example_dataset('3 cell mix')
filtered_variants = sample.dna.filter_variants()
sample.dna.group_by_genotype(features=filtered_variants[0:5])
clone 1 2 3 4 5 6 7 8 9 10 Missing GT clones (40) Small subclones (28) ADO clones (0)
chr2:25458546:C/T Het (47.13%) WT (0.6%) WT (0.7%) WT (0.57%) WT (0.77%) WT (1.9%) WT (31.26%) WT (0.87%) Het (31.88%) Het (27.22%) Missing in 2.75% of cells Het (24.13%) NaN
chr2:25469502:C/T WT (0.41%) WT (0.14%) Het (47.72%) WT (0.19%) WT (0.3%) WT (2.08%) WT (0.61%) Hom (98.01%) WT (0.61%) Het (29.8%) Missing in 3.84% of cells WT (10.45%) NaN
chr2:25470426:C/T WT (0.77%) Het (61.2%) WT (0.55%) WT (10.57%) Hom (98.75%) WT (1.19%) WT (0.95%) WT (0.79%) WT (1.84%) WT (0.52%) Missing in 7.71% of cells WT (22.67%) NaN
chr2:25470573:G/A Het (45.5%) WT (0.98%) WT (0.82%) WT (0.96%) WT (1.2%) WT (1.18%) Het (40.29%) WT (0.54%) WT (3.54%) Het (29.29%) Missing in 2.75% of cells Het (31.98%) NaN
chr2:209113192:G/A WT (0.74%) Het (50.96%) WT (0.67%) Het (51.42%) Het (50.82%) WT (0.84%) WT (0.75%) WT (1.12%) WT (1.24%) WT (0.72%) Missing in 0.32% of cells WT (18.41%) NaN
Total Cell Number 542 (21.89%) 436 (17.61%) 348 (14.05%) 205 (8.28%) 159 (6.42%) 114 (4.6%) 79 (3.19%) 56 (2.26%) 46 (1.86%) 31 (1.25%) 302 (12.2%) 158 (6.38%) NaN
3 cell mix Cell Number 542 (21.89%) 436 (17.61%) 348 (14.05%) 205 (8.28%) 159 (6.42%) 114 (4.6%) 79 (3.19%) 56 (2.26%) 46 (1.86%) 31 (1.25%) 302 (12.2%) 158 (6.38%) NaN
Parents NaN NaN NaN [small, 2, small] [2] [small, 3, 4, 7] [1] [3] [1] NaN NaN NaN NaN
Sisters NaN NaN NaN [small, 5, small] [4] [small, 8, small, small] [small] [6] [small] NaN NaN NaN NaN
ADO score 0 0 0 0.679728 0.833091 0.886213 0.369933 0.994591 0.978414 0 NaN NaN NaN
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