def load_palantir_data(smoothed=False):
fn = '../../data/external/Palantir/human_cd34_bm_rep1.h5ad'
an = anndata.read_h5ad(fn)
genes = an.var_names
cells = an.obs_names
if not smoothed:
counts = singlet.CountsTable(
data=an.raw.X.todense().T,
index=genes,
columns=cells,
)
else:
counts = singlet.CountsTable(
data=an.obsm['MAGIC_imputed_data'].T,
index=genes,
columns=cells,
)
ss = singlet.SampleSheet(an.obs)
ss['tsne_1'] = an.obsm['tsne'][:, 0]
ss['tsne_2'] = an.obsm['tsne'][:, 1]
ss['clusters'] = ss['clusters'].astype(str)
ds = singlet.Dataset(
counts_table=counts,
samplesheet=ss,
)
return ds
def load_palantir_data(smoothed=False):
fn = '../../data/external/Palantir/human_cd34_bm_rep1.h5ad'
an = anndata.read_h5ad(fn)
genes = an.var_names
cells = an.obs_names
if not smoothed:
counts = singlet.CountsTable(
data=an.raw.X.todense().T,
index=genes,
columns=cells,
)
else:
counts = singlet.CountsTable(
data=an.obsm['MAGIC_imputed_data'].T,
index=genes,
columns=cells,
)
ss = singlet.SampleSheet(an.obs)
ss['tsne_1'] = an.obsm['tsne'][:, 0]
ss['tsne_2'] = an.obsm['tsne'][:, 1]
ss['clusters'] = ss['clusters'].astype(str)
ds = singlet.Dataset(
counts_table=counts,
samplesheet=ss,
)
ds.samplesheet['Cell Subtype'] = ds.samplesheet['clusters'].replace({
'0':
'HSC',
'1':
'HSC',
'2':
'Ery-precursor',
'3':
'Mono',
'4':
'Mono-precursor',
'5':
'CLP',
'6':
'Mono',
'7':
'pDC',
'8':
'Ery',
'9':
'Mega',
})
return ds
import anndata
if __name__ == '__main__':
fn = '../../data/external/Palantir/human_cd34_bm_rep1.h5ad'
an = anndata.read_h5ad(fn)
genes = an.var_names
cells = an.obs_names
counts = singlet.CountsTable(
data=an.X.T,
index=genes,
columns=cells,
)
ss = singlet.SampleSheet(an.obs)
ss['tsne_1'] = an.obsm['tsne'][:, 0]
ss['tsne_2'] = an.obsm['tsne'][:, 1]
ss['clusters'] = ss['clusters'].astype(str)
ds = singlet.Dataset(
counts_table=counts,
samplesheet=ss,
)
print('Get MAGIC smoothed data')
counts = singlet.CountsTable(
data=an.obsm['MAGIC_imputed_data'].T,
index=genes,
columns=cells,
)