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])

Loading, <_io.BytesIO object at 0x7fdd89306d10>

Loaded in 0.4s.
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 C T https://varsome.com/variant/hg19/chr2:25458546... SNV NM_022552.5 DNMT3A c.2597+30G>A intronic 0.513776 0.551042 rs2304429 Benign
chr2:25469502:C/T 25469502 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 C T https://varsome.com/variant/hg19/chr2:25470426... SNV NM_022552.5 DNMT3A c.1014+34G>A intronic 0.000702 0.635212 rs142243425
chr2:25470573:G/A 25470573 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 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
sample.dna = sample.dna[:, filtered_variants[0:2]]

sample.heatmap(
    clusterby='protein',
    sortby='dna',
    drop='cnv',
    flatten=False
)

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
sample.dna = sample.dna[:, filtered_variants[0:2]]

sample.heatmap(
    clusterby='protein',
    sortby='dna',
    drop='cnv',
    flatten=True,
    quantify_dna_mut=True
)

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')
group.fishplot(labels=["Clone 1", "Clone 2"], parents=[None, None])

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