def test_read_write_h5ad(self):
adata = pg.read_input(
"tests/pegasus-test-data/input/hgmm_1k_v3_filtered_feature_bc_matrix/"
)
pg.write_output(adata, "test.h5ad")
adata2 = pg.read_input("test.h5ad")
assert_adata_equal(self, adata, adata2)
def plot_umap(adata, Z_torch, Z_py, Z_R, prefix, batch_key):
if adata is not None:
adata.obsm['X_torch'] = Z_torch
adata.obsm['X_py'] = Z_py
adata.obsm['X_harmony'] = Z_R
pg.neighbors(adata, rep = 'torch')
pg.umap(adata, rep = 'torch', out_basis = 'umap_torch')
pg.neighbors(adata, rep = 'py')
pg.umap(adata, rep = 'py', out_basis = 'umap_py')
pg.neighbors(adata, rep = 'harmony')
pg.umap(adata, rep = 'harmony', out_basis = 'umap_harmony')
pg.write_output(adata, "./result/{}_result".format(prefix))
else:
print("Use precalculated AnnData result.")
if os.system("pegasus plot scatter --basis umap --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.before.umap.pdf".format(name = prefix, attr = batch_key)):
sys.exit(1)
if os.system("pegasus plot scatter --basis umap_torch --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.torch.umap.pdf".format(name = prefix, attr = batch_key)):
sys.exit(1)
if os.system("pegasus plot scatter --basis umap_py --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.py.umap.pdf".format(name = prefix, attr = batch_key)):
sys.exit(1)
if os.system("pegasus plot scatter --basis umap_harmony --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.harmony.umap.pdf".format(name = prefix, attr = batch_key)):
sys.exit(1)
def plot_umap(adata, Z_cpu, Z_gpu, Z_R, prefix, batch_key):
if adata is not None:
adata.obsm['X_cpu'] = Z_cpu
adata.obsm['X_gpu'] = Z_gpu
adata.obsm['X_harmony'] = Z_R
pg.neighbors(adata, rep = 'cpu')
pg.umap(adata, rep = 'cpu', out_basis = 'umap_cpu')
pg.neighbors(adata, rep = 'gpu')
pg.umap(adata, rep = 'gpu', out_basis = 'umap_gpu')
pg.neighbors(adata, rep = 'harmony')
pg.umap(adata, rep = 'harmony', out_basis = 'umap_harmony')
pg.write_output(adata, "./result/{}_result".format(prefix))
else:
print("Use precalculated AnnData result.")
if os.system("pegasus plot scatter --basis umap --attributes {attr} --alpha 0.5 ./result/{prefix}_result.h5ad ./plots/{prefix}.before.umap.pdf".format(attr = batch_key, prefix = prefix)):
sys.exit(1)
if os.system("pegasus plot scatter --basis umap_cpu --attributes {attr} --alpha 0.5 ./result/{prefix}_result.h5ad ./plots/{prefix}.cpu.umap.pdf".format(attr = batch_key, prefix = prefix)):
sys.exit(1)
if os.system("pegasus plot scatter --basis umap_gpu --attributes {attr} --alpha 0.5 ./result/{prefix}_result.h5ad ./plots/{prefix}.gpu.umap.pdf".format(attr = batch_key, prefix = prefix)):
sys.exit(1)
if os.system("pegasus plot scatter --basis umap_harmony --attributes {attr} --alpha 0.5 ./result/{prefix}_result.h5ad ./plots/{prefix}.harmony.umap.pdf".format(attr = batch_key, prefix = prefix)):
sys.exit(1)
def test_read_write_old_5ad_backed_whitelist(self):
shutil.copy(
"tests/pegasus-test-data/input/test_obsm_compound.h5ad",
"test_obsm_compound.h5ad",
)
adata = pg.read_input("test_obsm_compound.h5ad", h5ad_mode="r+")
pg.write_output(adata, "test_obsm_compound.h5ad", whitelist=["obs"])
adata2 = pg.read_input("test_obsm_compound.h5ad")
assert_adata_equal(self, adata, adata2)
def write_dataset(ds, path, output_format='txt'):
path = str(path)
if not path.lower().endswith('.' + output_format):
path += '.' + output_format
if output_format == 'txt':
x = ds.X.toarray() if scipy.sparse.isspmatrix(ds.X) else ds.X
pd.DataFrame(x, index=ds.obs.index,
columns=ds.var.index).to_csv(path,
index_label='id',
sep='\t',
doublequote=False)
else:
pg.write_output(ds, path)
def test_write_mtx(self):
adata = pg.read_input(
"tests/pegasus-test-data/input/heart_1k_v3/filtered_feature_bc_matrix.h5"
)
adata.var['test'] = 1.0
adata.obs['test'] = 1.0
output_dir = 'test_mtx/mm10'
pg.write_output(adata, os.path.join(output_dir, 'matrix.mtx.gz'))
adata2 = pg.read_input(output_dir)
del adata2.obs['Channel'] # get channel from csv
adata2.obs = adata2.obs.join(
pd.read_csv(os.path.join(output_dir, 'obs.csv.gz'), index_col=0))
adata2.var = adata2.var.join(
pd.read_csv(os.path.join(output_dir, 'var.csv.gz'), index_col=0))
del adata2.var['featuretype']
assert_adata_equal(self, adata, adata2, obs_blacklist=['Channel'])
if __name__ == "__main__":
import pandas as pd
import pegasus as pg
import argparse
parser = argparse.ArgumentParser(
description='Update the X_pca with the results of harmony')
parser.add_argument('h5ad_filename', type=str)
parser.add_argument('harmony_csv', type=str)
parser.add_argument('output', type=str)
args = parser.parse_args()
args = args.__dict__
pca = pd.read_csv(args["harmony_csv"])
pca = pca.values.T[1:] # remove the id pf the pc
adata = pg.read_input(args["h5ad_filename"])
adata.obsm["X_pca"] = pca
pg.write_output(adata, args["output"])
def main():
args = my_args()
out = args.output
command = "pegasus aggregate_matrix %s %s" % (args.input_csv, out)
os.system(command)
zarr_file = "%s.zarr.zip" % (out)
data = pg.read_input(zarr_file)
if args.citeseq:
data.select_data("%s-rna" % (data.uns['genome']))
pg.qc_metrics(data,
percent_mito=args.MT_percent,
mito_prefix=args.MT_prefix,
max_genes=args.max_genes)
df_qc = pg.get_filter_stats(data)
df_qc.to_csv("%s_qc_get_filter_stats.csv" % (out))
pg.qcviolin(data, plot_type='gene')
plt.savefig("%s_qcviolin_gene.pdf" % (out), bbox_inches='tight')
pg.qcviolin(data, plot_type='count')
plt.savefig("%s_qcviolin_UMI_count.pdf" % (out), bbox_inches='tight')
pg.qcviolin(data, plot_type='mito')
plt.savefig("%s_qcviolin_UMI_mito.pdf" % (out), bbox_inches='tight')
# filtering
pg.filter_data(data)
pg.identify_robust_genes(data, percent_cells=0.05)
pg.log_norm(data)
print(data.obs['Channel'].value_counts())
# save log norm data, rna
df = pd.DataFrame.sparse.from_spmatrix(data.X)
df.index = data.obs.index.tolist()
df.columns = data.var.index.tolist()
df.to_pickle("%s.rna.log_norm.pkl" % (out))
if args.citeseq:
data.select_data("%s-citeseq" % (data.uns['genome']))
df = pd.DataFrame.sparse.from_spmatrix(data.X)
df.index = data.obs.index.tolist()
df.columns = data.var.index.tolist()
df.to_pickle("%s.antibody.log_norm.pkl" % (out))
data.select_data("%s-rna" % (data.uns['genome']))
# without batch correction
data_baseline = data.copy()
pg.highly_variable_features(data_baseline,
consider_batch=False,
n_top=4000)
data_baseline.var.loc[
data_baseline.var['highly_variable_features']].sort_values(
by='hvf_rank')
pg.hvfplot(data_baseline)
plt.savefig("%s_hvfplot_noBC.pdf" % (out), bbox_inches='tight')
pg.pca(data_baseline, n_components=200)
pg.neighbors(data_baseline, K=200)
pg.louvain(data_baseline, resolution=2)
pg.umap(data_baseline, n_neighbors=10, min_dist=0.4)
pg.scatter(data_baseline,
attrs=['louvain_labels', 'Channel'],
basis='umap')
plt.savefig("%s_without_BC.pdf" % (out), bbox_inches='tight')
# with batch correction
pg.highly_variable_features(data, consider_batch=True, n_top=4000)
data.var.loc[data.var['highly_variable_features']].sort_values(
by='hvf_rank')
pg.hvfplot(data)
plt.savefig("%s_hvfplot_noBC.pdf" % (out), bbox_inches='tight')
data_harmony = data.copy()
pg.pca(data_harmony, n_components=200)
harmony_key = pg.run_harmony(data_harmony)
pg.neighbors(data_harmony, rep=harmony_key, K=200)
pg.louvain(data_harmony, rep=harmony_key, resolution=2)
pg.umap(data_harmony, rep=harmony_key, n_neighbors=10, min_dist=0.4)
pg.scatter(data_harmony, attrs=['louvain_labels', 'Channel'], basis='umap')
plt.savefig("%s_Harmony_BC.pdf" % (out), bbox_inches='tight')
pg.write_output(data_harmony, "%s_harmony.zarr" % (out))
ddf = pd.DataFrame.sparse.from_spmatrix(data_harmony.X)
ddf.index = data_harmony.obs.index.tolist()
ddf.columns = data_harmony.var.index.tolist()
data_harmony.select_data("%s-citeseq" % (data_harmony.uns['genome']))
ddf2 = pd.DataFrame.sparse.from_spmatrix(data_harmony.X)
ddf2.index = data_harmony.obs.index.tolist()
ddf2.columns = data_harmony.var.index.tolist()
df_all = pd.concat([ddf, ddf2], axis=1)
df_all = df_all.sparse.to_dense()
df_all = df_all.round(3)
df_all.to_csv("%s.Harmony_correction.data.csv" % (out))
### original harmony UMAP data
out = data_harmony.obs.copy()
out['UMAP1'] = data_harmony.obsm['X_umap'][:, 0]
out['UMAP2'] = data_harmony.obsm['X_umap'][:, 1]
from anndata import AnnData
ann = AnnData(X=out[['UMAP1', 'UMAP2']],
obs=out[['Channel', 'louvain_labels']])
import scanpy as sc
from matplotlib import rcParams
sc.pl.scatter(ann,
x="UMAP1",
y="UMAP2",
color='louvain_labels',
legend_loc='on data',
legend_fontsize=12,
legend_fontoutline=2,
frameon=False,
title='clustering of cells')
plt.savefig("%s_Scapy_UMAP.png" % (args.output), bbox_inches='tight')
out.to_csv("%s_Harmony_UMAP.csv" % (args.output))
def test_read_write_old_5ad(self):
adata = pg.read_input(
"tests/pegasus-test-data/input/test_obsm_compound.h5ad")
pg.write_output(adata, "test.h5ad")
adata2 = pg.read_input("test.h5ad")
assert_adata_equal(self, adata, adata2)
