def test_read_10x_h5():
sc.read_10x_h5(os.path.join(ROOT, '1.2.0',
'filtered_gene_bc_matrices_h5.h5'),
genome='hg19_chr21')
sc.read_10x_h5(os.path.join(ROOT, '3.0.0',
'filtered_feature_bc_matrix.h5'),
genome='GRCh38_chr21')
def tsne_cluster(path, njobs):
adata = sc.read_10x_h5(path)
sc.pp.recipe_zheng17(adata)
sc.tl.tsne(adata, n_jobs=n_jobs)
sc.tl.louvain(adata)
sc.pl.tsne(adata, color='louvain_groups', save=True, right_margin=2)
adata.write('one_million.h5ad')
def read_raw_file(self):
"""
Reads the raw data file and turns it into a dense matrix, stored as
an attribute.
Returns
-------
"""
print("reading single cell data from {}".format(self.raw_file))
file_format = self.raw_file.split('.')[-1]
if file_format == 'h5':
andata = sc.read_10x_h5(self.raw_file)
elif file_format == 'h5ad':
andata = sc.read(self.raw_file)
else:
raise ValueError('Reading [ %s ] failed, the inferred file '
'format [ %s ] is not supported. Please convert '
'your file to either h5 or h5ad format.' %
(self.raw_file, file_format))
# appends -1 -2... to the name of genes that already exist
andata.var_names_make_unique()
if sp_sparse.issparse(andata.X):
andata.X = andata.X.toarray()
self.sc_raw = andata
def convert_from_10xh5(path, genome):
adata = sc.read_10x_h5(path, genome)
adata.var_names_make_unique()
df = pandas.DataFrame(adata.X.todense(),
index=adata.obs_names,
columns=adata.var_names)
yield df
def adataToTrainState(filename, refname, num_genes=8000):
adata = sc.read_10x_h5(filename, refname)
adata.name = filename.split(os.sep)[-3]
sc.tl.addCleanObsNames(adata)
sc.pp.filter_cells(adata, min_genes=10)
mito_genes = [
name for name in adata.var_names if name in [
'ND1', 'ND2', 'ND4L', 'ND4', 'ND5', 'ND6', 'ATP6', 'ATP8', 'CYTB',
'COX1', 'COX2', 'COX3'
]
]
#mito_genes = [name for name in adata.var_names if name.startswith(('MTND','MTCO','MTATP','MTCYB','MTRNR','MTTR',))]
ribo_genes = [
name for name in adata.var_names
if name.startswith('RPS') or name.startswith('RPL')
]
adata.obs['percent_mito'] = np.sum(adata[:, mito_genes].X,
axis=1).A1 / np.sum(adata.X, axis=1).A1
adata.obs['percent_ribo'] = np.sum(adata[:, ribo_genes].X,
axis=1).A1 / np.sum(adata.X, axis=1).A1
# add the total counts per cell as observations-annotation to adata
adata.obs['n_counts'] = adata.X.sum(axis=1).A1
scanpy.api.pp.filter_genes_dispersion(
adata, n_top_genes=min(np.sum(np.sum(adata.X, axis=0) > 0), num_genes))
return (adata)
def adataToTrainStateAggregate(fileList, refname, num_genes=3000):
adatas = []
batch_categories = []
for filename in fileList:
a = sc.read_10x_h5(filename, refname)
sc.pp.filter_cells(a, min_genes=30)
a.name = filename.split(os.sep)[-3]
a.obs['dataset'] = a.name
batch_categories.append(a.name)
adatas.append(a)
adata = sc.AnnData.concatenate(*adatas, batch_categories=batch_categories)
mito_genes = [
name for name in adata.var_names if name in [
'ND1', 'ND2', 'ND4L', 'ND4', 'ND5', 'ND6', 'ATP6', 'ATP8', 'CYTB',
'COX1', 'COX2', 'COX3'
]
]
#mito_genes = [name for name in adata.var_names if name.startswith(('MTND','MTCO','MTATP','MTCYB','MTRNR','MTTR',))]
ribo_genes = [
name for name in adata.var_names
if name.startswith('RPS') or name.startswith('RPL')
]
adata.obs['percent_mito'] = np.sum(adata[:, mito_genes].X,
axis=1).A1 / np.sum(adata.X, axis=1).A1
adata.obs['percent_ribo'] = np.sum(adata[:, ribo_genes].X,
axis=1).A1 / np.sum(adata.X, axis=1).A1
# add the total counts per cell as observations-annotation to adata
adata.obs['n_counts'] = adata.X.sum(axis=1).A1
scanpy.api.pp.filter_genes_dispersion(
adata, n_top_genes=min(np.sum(np.sum(adata.X, axis=0) > 0), num_genes))
return (adata[adata.obs.n_counts.argsort()[::-1], :])
def load_10x_1_3mil():
"""
https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons
"""
filename_data='/data/martin/single_cell/10x_1.3mil_mice_brain/' +\
'1M_neurons_filtered_gene_bc_matrices_h5.h5'
data = sc.read_10x_h5(filename_data)
return data
def convert(
input,
output,
chunk_size=16 * 1024 * 1024,
genome=None,
overwrite=False
):
input_path, input_ext = splitext(input)
output_path, output_ext = splitext(output)
print('converting: %s to %s' % (input, output))
if input_ext == '.h5' or input_ext == '.loom':
if output_ext == '.zarr':
# Convert 10x (HDF5) to Zarr
source = h5py.File(input)
zarr.tree(source)
store = zarr.DirectoryStore(output)
dest = zarr.group(store=store, overwrite=overwrite)
# following fails if without_attrs=False (the default), possibly related to https://github.com/h5py/h5py/issues/973
zarr.copy_all(source, dest, log=sys.stdout, without_attrs=True)
zarr.tree(dest)
elif output_ext == '.h5ad':
if not genome:
keys = list(h5py.File(input).keys())
if len(keys) == 1:
genome = keys[0]
else:
raise Exception(
'Set --genome flag when converting from 10x HDF5 (.h5) to Anndata HDF5 (.h5ad); top-level groups in file %s: %s'
% (input, ','.join(keys))
)
adata = read_10x_h5(input, genome=genome)
# TODO: respect overwrite flag
adata.write(output)
elif input_ext == '.h5ad':
adata = read_h5ad(input, backed='r')
(r, c) = adata.shape
chunks = (getsize(input) - 1) / chunk_size + 1
chunk_size = (r - 1) / chunks + 1
if output_ext == '.zarr':
print('converting %s (%dx%d) to %s in %d chunks (%d rows each)' % (input, r, c, output, chunks, chunk_size))
# TODO: respect overwrite flag
adata.write_zarr(
make_store(output),
chunks=(chunk_size, c)
)
else:
raise Exception('Unrecognized output extension: %s' % output_ext)
else:
raise Exception('Unrecognized input extension: %s' % input_ext)
def basic_analysis(filename):
adata = sc.read_10x_h5(filename)
sc.pp.subsample(adata, fraction=0.1)
sc.pp.recipe_zheng17(adata)
sc.pp.neighbors(adata, method='rapids')
sc.tl.louvain(adata)
sc.tl.umap(adata)
sc.tl.rank_genes_groups(adata, 'louvain')
adata.write('./write/result_130K_gpu_neighbors.h5ad')
# plotting
sc.pl.umap(adata, color='louvain', save='_130K_gpu_neighbors.png')
sc.pl.rank_genes_groups(adata, save='_130K_gpu_neighbors.pdf')
def basic_analysis(filename):
adata = sc.read_10x_h5(filename)
sc.pp.subsample(adata, fraction=0.1)
sc.pp.recipe_zheng17(adata)
sc.pp.neighbors(adata, method="rapids")
sc.tl.louvain(adata)
sc.tl.umap(adata)
sc.tl.rank_genes_groups(adata, "louvain")
adata.write("./write/result_130K_gpu_neighbors.h5ad")
# plotting
sc.pl.umap(adata, color="louvain", save="_130K_gpu_neighbors.png")
sc.pl.rank_genes_groups(adata, save="_130K_gpu_neighbors.pdf")
def basic_analysis(filename):
adata = sc.read_10x_h5(filename)
sc.pp.recipe_zheng17(adata)
sc.pp.neighbors(adata)
sc.tl.louvain(adata)
sc.tl.paga(adata)
sc.tl.umap(adata)
sc.tl.rank_genes_groups(adata, "louvain")
adata.write("./write/result.h5ad")
# plotting
sc.pl.paga(adata)
sc.pl.umap(adata, color="louvain")
sc.pl.rank_genes_groups(adata, save=".pdf")
def get_genes(self, sce):
_transcripts = sce.rowData["hgnc_symbol"]
try:
adata = sc.read_10x_h5(self.filtered_h5(), genome=config.build)
except Exception:
adata = sc.read_10x_mtx(self.filtered_matrices())
transcripts = []
for symbol in transcripts:
if symbol not in adata.var.index:
symbol = symbol.replace(".", "-")
if symbol not in adata.var.index:
symbol = symbol.split("-")
symbol = "-".join(symbol[:-1]) + ".{}".format(symbol[-1])
if symbol not in adata.var.index:
symbol = symbol.split(".")[0]
transcripts.append(symbol)
return _transcripts
def read_anndata(input, genome=None):
_, input_ext = splitext(input)
if input_ext == ".h5":
if not genome:
keys = list(File(input, "r").keys())
if len(keys) == 1:
genome = keys[0]
else:
raise Exception(
"Set --genome flag when converting from 10x HDF5 (.h5) to Anndata HDF5 (.h5ad); top-level groups in file %s: %s"
% (input, ",".join(keys)))
return read_10x_h5(input, genome=genome)
elif input_ext == ".h5ad":
return read_h5ad(input)
elif input_ext == ".loom":
# reads the whole dataset in memory!
return read_loom(input)
else:
raise Exception("Unrecognized input extension: %s" % input_ext)
def gene_map(self, sce, original=False):
_transcripts = sce.rowData["Symbol"]
try:
adata = sc.read_10x_h5(self.filtered_h5(), genome=config.build)
except Exception:
adata = sc.read_10x_mtx(self.filtered_matrices())
transcripts = {}
for symbol in _transcripts:
original = symbol
if symbol not in adata.var.index:
symbol = symbol.replace(".", "-")
if symbol not in adata.var.index:
symbol = symbol.split("-")
symbol = "-".join(symbol[:-1]) + ".{}".format(symbol[-1])
if symbol not in adata.var.index:
symbol = symbol.split(".")[0]
if original:
transcripts[original] = symbol
else:
transcripts[symbol] = original
return transcripts
#!/usr/bin/env python3
import scanpy.api as sc
# Read 10x dataset
adata = sc.read_10x_h5("neuron_10k_v3_filtered_feature_bc_matrix.h5")
# Make variable names (in this case the genes) unique
adata.var_names_make_unique()
sc.tl.pca(adata)
sc.pl.pca(adata)
sc.pp.filter_genes(adata, min_counts=1)
sc.pp.normalize_per_cell(adata, key_n_counts='n_counts_all')
filter_result = sc.pp.filter_genes_dispersion(adata.X,
flavor='cell_ranger',
n_top_genes=1000,
log=False)
adata = adata[:, filter_result.gene_subset]
sc.pp.normalize_per_cell(adata)
sc.pp.log1p(adata)
sc.pp.scale(adata)
sc.tl.pca(adata, n_comps=50)
sc.pl.pca(adata)
def getAnnData_10x_h5(input_file): adata = sc.read_10x_h5(input_file,"GRCh38") return adata
# coding: utf-8
# In[1]:
import pandas as pd
import scanpy.api as sc
# In[ ]:
ica_cord_blood = sc.read_10x_h5(
glob.glob(
'/projects/sysbio/users/cellAtlas/data/human/immune_census/ica_cord_blood_h5.h5'
)[0], "GRCh38")
ica_cord_blood.write(
'/projects/sysbio/users/cellAtlas/scanpyObjects/ica_cord_blood.h5ad')
# In[ ]:
ica_bone_marrow = sc.read_10x_h5(
glob.glob(
'/projects/sysbio/users/cellAtlas/data/human/immune_census/ica_bone_marrow_h5.h5'
)[0], "GRCh38")
ica_bone_marrow.write(
'/projects/sysbio/users/cellAtlas/scanpyObjects/ica_bone_marrow.h5ad')
import numpy as np
import pickle
import sys
sys.path.append('/gpfs01/berens/user/dkobak/FIt-SNE')
from fast_tsne import fast_tsne
# LOAD AND PREPROCESS THE DATA
import scanpy.api as sc
sc.settings.verbosity = 2
# Data file is from here
# https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons
adata = sc.read_10x_h5('big-data/10x/1M_neurons_filtered_gene_bc_matrices_h5.h5')
sc.pp.recipe_zheng17(adata)
X = np.copy(adata.X)
X = X - X.mean(axis=0)
U, s, V = np.linalg.svd(X, full_matrices=False)
U[:, np.sum(V,axis=1)<0] *= -1
X = np.dot(U, np.diag(s))
X = X[:, np.argsort(s)[::-1]][:,:50]
pickle.dump(X, open('big-pickles/10x-pca.pickle', 'wb'))
# load cluster labels
# https://github.com/theislab/scanpy_usage/blob/master/170522_visualizing_one_million_cells/results/louvain.csv.gz
clusters = pd.read_csv('data/10x-1mln-scanpy-louvain.csv.gz', header=None).values[:,1].astype(int)
import matplotlib
matplotlib.use('agg') # plotting backend compatible with screen
import scanpy.api as sc
import pandas as pd
import numpy as np
import logging
import os
import velocyto as vcy
logging.basicConfig(level=logging.INFO)
logging.info("Start")
sc.settings.verbosity = 2
sc.settings.autosave = True # save figures, do not show them
sc.settings.set_figure_params(dpi=300) # set sufficiently high resolution for saving
inputfile = os.path.expanduser('/ye/yelabstore2/mtschmitz/seq/AlignedOrangutanOrganoid/Exonic/orangutanorganoid_Out/outs')
# Run louvain clustering on true gene expression values
velocityFile = os.path.expanduser('/ye/yelabstore2/mtschmitz/seq/AlignedOrangutanOrganoid/Exonic/orangutanorganoid_Out/velocyto/orangutanorganoid_Out.loom')
adata = sc.read_10x_h5('/home/mt/code/data/AlignedOrangutanOrganoid/Exonic/orangutanorganoid_Out/outs/filtered_gene_bc_matrices_h5.h5','refdata-celranger-Pabe2-toplevel')
adata=sc.tl.rna_velocity(adata,os.path.expanduser('~/code/data/AlignedOrangutanOrganoid/Exonic/orangutanorganoid_Out/velocyto/orangutanorganoid_Out.loom'))
import pandas as pd
import h5py
import scanpy.api as sc
import collections
import scipy.sparse as sp_sparse
import numpy as np
import tables
# In[169]:
# pick one data set here, but run code through for both
pick_one_dataset = "ica_cord_blood"
pick_one_dataset = "ica_bone_marrow"
scanpy_object = sc.read_10x_h5(
glob.glob(
'/projects/sysbio/users/cellAtlas/data/primary/human/immune_census/' +
pick_one_dataset + '_h5.h5')[0], "GRCh38")
print(scanpy_object.shape)
# # Explore the sample IDs
# In[170]:
# Donor ID is encoded in sample IDs
donor_id = [
y.split("Manton")[1]
for y in [x.split("_")[0] for x in scanpy_object.obs.index]
]
#print(set(donor_id))
import numpy as np
import pickle
import sys
sys.path.append('/gpfs01/berens/user/dkobak/FIt-SNE')
from fast_tsne import fast_tsne
# LOAD AND PREPROCESS THE DATA
import scanpy.api as sc
sc.settings.verbosity = 2
adata = sc.read_10x_h5('1M_neurons_filtered_gene_bc_matrices_h5.h5')
sc.pp.recipe_zheng17(adata)
X = np.copy(adata.X)
X = X - X.mean(axis=0)
U, s, V = np.linalg.svd(X, full_matrices=False)
U[:, np.sum(V,axis=1)<0] *= -1
X = np.dot(U, np.diag(s))
X = X[:, np.argsort(s)[::-1]][:,:50]
pickle.dump(X, open('pca-scanpy.pickle', 'wb'))
# CLUSTERING
import scanpy.api as sc
sc.settings.verbosity = 2
adata = sc.read_10x_h5('1M_neurons_filtered_gene_bc_matrices_h5.h5')
sc.pp.recipe_zheng17(adata)
#!/Users/cmdb/miniconda3/bin/python import scanpy.api as sc import sys import matplotlib sc.settings.autoshow = False adata = sc.read_10x_h5(sys.argv[1]) adata.var_names_make_unique() sc.tl.pca(adata, svd_solver='auto') sc.pl.pca(adata, save="PCA_adata.png", title="PCA Before Zheng Pre-processing") # sc.tl.pca(adata, n_comps=50) # var_names1 = adata.var_names # for i in var_names1: # print(adata.var_names[i]) # if adata.var_names[str(var_names1[i])]: # print(adata.var_names[i]) # sc.pl.pca(adata, save="PCA_adata.png") # sc.tl.tsne(adata) # sc.pl.tsne(adata, save="tSNE_adata.png") # sc.pp.neighbors(adata) # sc.tl.umap(adata) # sc.pl.umap(adata, save="uMAP_adata.png")
filename = "home/1M_neurons_matrix_subsampled_25K.h5"
elif len(sys.argv) > 1:
# default output file
filename = sys.argv[1]
if len(sys.argv) == 3:
verbose = int(sys.argv[2])
start = datetime.datetime.now()
sc.settings.verbosity = verbose # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_versions()
print("Reading, making names unique and filtering:")
now = datetime.datetime.now()
adata = sc.read_10x_h5(filename)
adata.var_names_make_unique() # this is unnecessary if using 'gene_ids'
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
print("Reading, making names unique and filtering Time:", datetime.datetime.now() - now)
print("Log Normalizing Data")
now = datetime.datetime.now()
sc.pp.normalize_per_cell(adata, counts_per_cell_after=1e3)
sc.pp.log1p(adata)
print("Log Normalizing Data Time:", datetime.datetime.now() - now)
adata.raw = adata
print("Finding Variable Features")
from scio import concatenate, check_obs, check_var
DATASET = "vanderburg_01"
OBS_DATA = "tables/{}_samples.csv".format(DATASET)
OUTPUT_DIR = "./"
obs = pd.read_csv(OBS_DATA)
dataset_samples = obs["samples"].values
filenames = [
"data/cellranger/{}_GEX/outs/raw_feature_bc_matrix.h5".format(sample[1:])
for sample in dataset_samples
]
adatas = [sc.read_10x_h5(filename, genome="GRCh38") for filename in filenames]
adatas2 = []
for adata, sample in zip(adatas, dataset_samples):
duplicated = adata.var_names.duplicated()
print("Removing {} gene symbols because they are duplicated".format(
sum(duplicated)))
adata = adata[:, ~duplicated].copy()
adata.obs['samples'] = sample
adatas2.append(adata)
adata = concatenate(adatas2, merge_var_cols=["gene_ids"])
adata.obs = adata.obs.join(obs.set_index("samples"), on="samples", how="left")
adata.obs["dataset"] = DATASET
