def test_scrublet_batched():
"""
Test that Scrublet run works with batched data.
Check that scrublet runs and detects some doublets.
"""
pytest.importorskip("scrublet")
adata = sc.datasets.pbmc3k()
adata.obs['batch'] = 1350 * ['a'] + 1350 * ['b']
split = [adata[adata.obs["batch"] == x].copy() for x in ("a", "b")]
sce.pp.scrublet(adata, use_approx_neighbors=False, batch_key='batch')
# replace assertions by conditions
assert "predicted_doublet" in adata.obs.columns
assert "doublet_score" in adata.obs.columns
assert adata.obs["predicted_doublet"].any(), "Expect some doublets to be identified"
assert (
'batches' in adata.uns['scrublet'].keys()
), "Expect .uns to contain batch info"
# Check that results are independent
for s in split:
sce.pp.scrublet(s, use_approx_neighbors=False)
merged = sc.concat(split)
pd.testing.assert_frame_equal(adata.obs[merged.obs.columns], merged.obs)
def normalizeMultiAd(multiAd, removeAmbiguous=True):
"""
对二代三代数据分开normalize, 最终获得的每个细胞有3e4的reads
"""
multiCountAd = multiAd[:, ~multiAd.var.index.str.contains("_")]
multiOtherAd = multiAd[:, multiAd.var.index.str.contains("_")]
sc.pp.normalize_total(multiCountAd, target_sum=1e4)
sc.pp.normalize_total(multiOtherAd, target_sum=2e4)
multiAd = sc.concat([multiCountAd, multiOtherAd], axis=1)
if removeAmbiguous:
multiAd = multiAd[:, ~(multiAd.var.index.str.contains("Ambiguous")
| multiAd.var.index.str.contains("_N_")), ]
return multiAd
def combineAdataUseScanorama(adataLs,
batchKey,
batchCateLs,
subSample=False,
subSampleCounts=0):
"""
利用Scanorama整合不同adata
adataLs:
[adata1, adata2]
batchKey:
添加的label
batchCateLs:
每个batch的名称 需要和adataLs一致
subSampleCounts:
下采样的样本数。
return:
整合后的adata
"""
import scanorama
adataLs = [x.copy() for x in adataLs]
if subSample:
sampleSize = min([x.shape[0] for x in adataLs])
if subSampleCounts:
sampleSize = min(sampleSize, subSampleCounts)
logger.info(f"sample size: {sampleSize}")
[sc.pp.subsample(x, n_obs=sampleSize) for x in adataLs]
for adata in adataLs:
sc.pp.normalize_total(adata, inplace=True)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata,
flavor="seurat",
n_top_genes=2000,
inplace=True)
print(f"↓↓↓↓↓↓↓↓↓{batchCateLs}↓↓↓↓↓↓↓↓")
combineScanoramaLs = scanorama.correct_scanpy(adataLs, return_dimred=True)
print(f"↑↑↑↑↑↑↑↑↑{batchCateLs}↑↑↑↑↑↑↑↑")
combineAdata = sc.concat(combineScanoramaLs,
label=batchKey,
index_unique="-",
keys=batchCateLs)
sc.pp.neighbors(combineAdata, n_pcs=50, use_rep="X_scanorama")
sc.tl.umap(combineAdata)
return combineAdata
def combineAdataUseScanoramaOld(
adataLs, batchKey, batchCateLs, subSample=False, subSampleCounts=0
):
"""
利用Scanorama整合不同adata
adataLs:
[adata1, adata2]
batchKey:
添加的label
batchCateLs:
每个batch的名称 需要和adataLs一致
subSampleCounts:
下采样的样本数。
return:
整合后的adata
"""
import scanorama
adataLs = [x.copy() for x in adataLs]
if subSample:
sampleSize = min([x.shape[0] for x in adataLs])
if subSampleCounts:
sampleSize = min(sampleSize, subSampleCounts)
logger.info(f"sample size: {sampleSize}")
[sc.pp.subsample(x, n_obs=sampleSize) for x in adataLs]
combineAdata = adataLs[0].concatenate(
adataLs[1:], batch_key=batchKey, batch_categories=batchCateLs
)
sc.pp.normalize_per_cell(combineAdata, counts_per_cell_after=1e4)
sc.pp.log1p(combineAdata)
sc.pp.highly_variable_genes(
combineAdata, min_mean=0.0125, max_mean=3, min_disp=1.5, batch_key=batchKey
)
sc.pl.highly_variable_genes(combineAdata)
varGenes = combineAdata.var.highly_variable
varGenes = varGenes[varGenes].keys()
varGenes = list(varGenes)
alldata = {}
for oneBatchName in combineAdata.obs[batchKey].unique():
alldata[oneBatchName] = combineAdata[
combineAdata.obs[batchKey] == oneBatchName, varGenes
]
combineAdataLs = list(alldata.values())
print(f"↓↓↓↓↓↓↓↓↓{batchCateLs}↓↓↓↓↓↓↓↓")
combineScanoramaLs = scanorama.correct_scanpy(combineAdataLs, return_dimred=True)
print(f"↑↑↑↑↑↑↑↑↑{batchCateLs}↑↑↑↑↑↑↑↑")
combineAdata = sc.concat(combineScanoramaLs)
# import pdb; pdb.set_trace()
# combineScanoramaAr = np.concatenate(combineScanoramaLs)
# combineAdata.obsm["SC"] = combineScanoramaAr
# combineAdata.raw = combineAdata
# combineAdata = combineAdata[:, varGenes]
# sc.pp.scale(combineAdata, max_value=10)
# sc.tl.pca(combineAdata, svd_solver="arpack", n_comps=50)
# sc.pl.pca(combineAdata)
sc.pp.neighbors(combineAdata, n_pcs=50, use_rep="X_scanorama")
sc.tl.umap(combineAdata)
return combineAdata