Ingest a second file

import lamindb as ln
import lnschema_bionty as lb
import readfcs

lb.settings.species = "human"

💡 loaded instance: testuser1/test-flow (lamindb 0.54.1)

ln.track()

💡 notebook imports: anndata==0.9.2 lamindb==0.54.1 lnschema_bionty==0.31.2 pytometry==0.1.4 readfcs==1.1.6 scanpy==1.9.5

💡 Transform(id='SmQmhrhigFPLz8', name='Ingest a second file', short_name='facs1', version='0', type=notebook, updated_at=2023-09-26 10:02:36, created_by_id='DzTjkKse')

💡 Run(id='b13zNeaKscxptfaMtKWi', run_at=2023-09-26 10:02:36, transform_id='SmQmhrhigFPLz8', created_by_id='DzTjkKse')

Let us validate and register another .fcs file:

Access

filepath = ln.dev.datasets.file_fcs()

adata = readfcs.read(filepath)

adata

AnnData object with n_obs × n_vars = 65016 × 16
    var: 'n', 'channel', 'marker', '$PnB', '$PnR', '$PnG'
    uns: 'meta'

Transform: normalize

import anndata as ad
import pytometry as pm

pm.pp.split_signal(adata, var_key="channel")

pm.tl.normalize_arcsinh(adata, cofactor=150)

adata = adata[  # subset to rows that do not have nan values
    adata.to_df().isna().sum(axis=1) == 0
]

adata.to_df().describe()

	KI67	CD3	CD28	CD45RO	CD8	CD4	CD57	CD14	CCR5	CD19	CD27	CCR7	CD127
count	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000	64593.000000
mean	-7.784467	-7.958064	-7.880424	-7.849991	-7.682381	-7.695841	-7.772347	-7.827088	-7.427381	-7.693235	-8.009255	-7.514956	-7.471545
std	30.911205	30.796328	30.847746	30.776819	30.846949	30.873545	30.907915	30.640249	30.767073	30.675623	30.902098	30.668348	30.830299
min	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761	-62.628761
25%	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892	-0.009892
50%	-0.000321	-0.000322	-0.000322	-0.000322	-0.000321	-0.000322	-0.000321	-0.000322	-0.000321	-0.000322	-0.000322	-0.000321	-0.000321
75%	1.086298	1.045244	0.819897	1.050630	1.104099	0.987080	0.995414	1.041992	1.145463	0.932001	1.096484	1.150226	1.248759
max	84.386696	84.386627	84.385376	84.398567	84.405106	84.398544	84.402496	84.398567	84.337654	84.382713	84.402489	84.362930	84.374611

Validate cell markers

Let’s see how many markers validate:

validated = lb.CellMarker.validate(adata.var.index)

❗ 7 terms (53.80%) are not validated for name: KI67, CD45RO, CD4, CD14, CCR5, CD19, CCR7

Let’s standardize and re-validate:

adata.var.index = lb.CellMarker.standardize(adata.var.index)
validated = lb.CellMarker.validate(adata.var.index)

❗ found 1 synonym in Bionty: ['KI67']
   please add corresponding CellMarker records via `.from_values(['Ki67'])`

❗ 3 terms (23.10%) are not validated for name: Ki67, CD45RO, CCR5

Next, register non-validated markers from Bionty:

records = lb.CellMarker.from_values(adata.var.index[~validated])
ln.save(records)

Now they pass validation:

validated = lb.CellMarker.validate(adata.var.index)
assert all(validated)

Register

modalities = ln.Modality.lookup()
features = ln.Feature.lookup()
efs = lb.ExperimentalFactor.lookup()
species = lb.Species.lookup()
markers = lb.CellMarker.lookup()

file = ln.File.from_anndata(
    adata,
    description="Flow cytometry file 2",
    field=lb.CellMarker.name,
    modality=modalities.protein,
)

/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/anndata/_core/anndata.py:1230: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.
  df[key] = c

... storing '$PnR' as categorical

/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/anndata/_core/anndata.py:1230: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.
  df[key] = c

... storing '$PnG' as categorical

❗    3 terms (100.00%) are not validated for name: FSC-A, FSC-H, SSC-A

❗    no validated features, skip creating feature set

file.save()

file.labels.add(efs.fluorescence_activated_cell_sorting, features.assay)
file.labels.add(species.human, features.species)

file.features

Features:
  var: FeatureSet(id='FMd5YcveGonVnH6Y6e6z', n=13, type='number', registry='bionty.CellMarker', hash='cInZdHy3fspNNLGysq01', updated_at=2023-09-26 10:02:41, modality_id='8Imbc507', created_by_id='DzTjkKse')
    'CD8', 'Cd4', 'CD127', 'CD28', 'CD57', 'Cd14', 'CCR5', 'Cd19', 'Ki67', 'CD3', ...
  external: FeatureSet(id='odDTC6KjllzbqtYg1LnT', n=2, registry='core.Feature', hash='UZN25UlFEndb14m2m-P4', updated_at=2023-09-26 10:02:41, modality_id='iUJ9JARf', created_by_id='DzTjkKse')
    🔗 assay (1, bionty.ExperimentalFactor): 'fluorescence-activated cell sorting'
    🔗 species (1, bionty.Species): 'human'

View data flow:

file.view_flow()

Inspect a PCA fo QC - this dataset looks much like noise:

import scanpy as sc

sc.pp.pca(adata)
sc.pl.pca(adata, color=markers.cd14.name)