def scrublet_predictions(self, vlm, input_dir, doublet_rate=0.06):
import scrublet as scr
import scipy.io
print('Loading counts matrix {}/matrix.mtx'.format(input_dir),
file=sys.stderr)
counts_matrix = scipy.io.mmread(input_dir + '/matrix.mtx').T.tocsc()
print("Loading barcodes {}/barcodes.tsv".format(input_dir),
file=sys.stderr)
barcodes = np.array(
scr.load_genes(input_dir + 'barcodes.tsv', delimiter='t',
column=0))
print("Initializing scrublet object", file=sys.stderr)
scrub = scr.Scrublet(
counts_matrix,
expected_doublet_rate=doublet_rate) #whole counts matrix
print("Computing doublet predictions", file=sys.stderr)
doublet_scores, predicted_doublets = scrub.scrub_doublets(
min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=30)
#collapse barcodes, scores, and predictions into a dict
doublet_dict = {
barcode: [doublet_scores[i], predicted_doublets[i]]
for i, barcode in enumerate(barcodes)
}
#add doublet score and doublet prediction as column attributes:
vlm.ca["doublet_scores"] = np.array(
[doublet_dict[barcode][0] for barcode in vlm.ca['CellID']])
vlm.ca["doublet_predictions"] = np.array(
[doublet_dict[barcode][1] for barcode in vlm.ca['CellID']])
return vlm
def annotate_doublets(mtx_fpath, feature_fpath, expected_doublet_rate2=0.06):
if False:
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Arial'
plt.rc('font', size=14)
plt.rcParams['pdf.fonttype'] = 42
counts_matrix = scipy.io.mmread(mtx_fpath).T.tocsc()
genes = np.array(scr.load_genes(feature_fpath, delimiter='\t', column=1))
print('Counts matrix shape: {} rows, {} columns'.format(
counts_matrix.shape[0], counts_matrix.shape[1]))
print('Number of genes in gene list: {}'.format(len(genes)))
scrub = scr.Scrublet(counts_matrix,
expected_doublet_rate=expected_doublet_rate2)
doublet_scores, predicted_doublets = scrub.scrub_doublets(
min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=30)
if False:
scrub.plot_histogram()
print('Running UMAP...')
scrub.set_embedding(
'UMAP', scr.get_umap(scrub.manifold_obs_, 10, min_dist=0.3))
print('Done.')
scrub.plot_embedding('UMAP', order_points=True)
return ([doublet_scores, predicted_doublets])
def run_scrublet_rna(input_dir):
counts_matrix = scipy.io.mmread(input_dir + 'matrix.mtx').T.tocsc()
genes = np.array(scr.load_genes(input_dir + 'features.tsv', delimiter='\t', column=1))
print('Counts matrix shape: {} rows, {} columns'.format(counts_matrix.shape[0], counts_matrix.shape[1]))
print('Number of genes in gene list: {}'.format(len(genes)))
scrub = scr.Scrublet(counts_matrix, expected_doublet_rate=0.05)
doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=30)
np.savetxt(input_dir + 'predicted_doublet_mask.txt', scrub.predicted_doublets_, fmt='%s')
np.savetxt(input_dir + 'doublet_scores.txt', scrub.doublet_scores_obs_, fmt='%.4f')
def scrublet_c(sample, inDir, outDir, expected_doublet_rate, sim_doublet_ratio,
ratio_df, out_df):
print(sample, "start scrublet")
counts_matrix = scipy.io.mmread(os.path.join(inDir,
'matrix.mtx')).T.tocsc()
genes = np.array(
scr.load_genes(os.path.join(inDir, 'genes.tsv'),
delimiter='\t',
column=1))
scrub = scr.Scrublet(counts_matrix,
expected_doublet_rate=expected_doublet_rate,
sim_doublet_ratio=sim_doublet_ratio)
doublet_scores, predicted_doublets = scrub.scrub_doublets(
min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=30)
scrub.plot_histogram()
plt.savefig(
os.path.join(
outDir, "{0}_scrublet_doublet_score_histogram.pdf".format(sample)))
print(sample, 'Running scrublet UMAP...')
scrub.set_embedding('UMAP',
scr.get_umap(scrub.manifold_obs_, 10, min_dist=0.3))
print(sample, 'scrublet Done.')
scrub.plot_embedding('UMAP', order_points=True)
plt.savefig(os.path.join(outDir, "{0}_scrublet_UMAP.pdf".format(sample)))
print(sample, "Done scrublet")
ratio_df.loc['scrublet', sample] = scrub.detected_doublet_rate_
out_df['scrublet_doublet_scores'] = doublet_scores
out_df['scrublet_doublets'] = predicted_doublets
return ratio_df, out_df
import sys
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', help='raw 10X file directory for input', type=str)
parser.add_argument('-o', '--output', help='output directory', type=str, default="./")
parser.add_argument('-n', '--name', help='name of output files', type=str, default="name")
parser.add_argument('-r', '--doublet', help='expected doublet rate, default=0.06', type=float, default=0.06)
parser.add_argument('-e', '--embed', help='plot UMAP and TSNE. True or False.', type=bool, default=False)
args = parser.parse_args()
#load counts matrix, genes, barcodes
print("Loading counts matrix %s" % args.input + '/matrix.mtx', file=sys.stderr)
counts_matrix = scipy.io.mmread(args.input + '/matrix.mtx').T.tocsc()
print("Loading barcodes %s" % args.input + '/barcodes.tsv', file=sys.stderr)
barcodes = np.array(scr.load_genes(args.input + 'barcodes.tsv', delimiter='t', column=0))
#initialize scrublet object
print("Initializing scrublet object", file=sys.stderr)
scrub = scr.Scrublet(counts_matrix, expected_doublet_rate=args.doublet) #whole counts matrix
print("Computing doublet predictions", file=sys.stderr)
doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=30)
#write scrublet output to file:
print("Writing doublet predictions to %s" % args.output + "/" + args.name + "_predicted_doublets.tsv", file=sys.stderr)
with open(args.output + "/" + args.name + "_predicted_doublets.tsv", 'w') as outfile:
outfile.write("\t".join(["barcode", "doublet_score", "doublet_prediction"])+"\n")
import scrublet as scr
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import os
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Arial'
plt.rc('font', size=14)
plt.rcParams['pdf.fonttype'] = 42
#filtered
#input_dir = '/share/ScratchGeneral/briglo/scRNA/venchi/data/hg19/VENCHI_SampleBCITE/outs/filtered_feature_bc_matrix/'
input_dir = '/share/ScratchGeneral/briglo/scRNA/venchi/outputs/seurat/'
counts_matrix = scipy.io.mmread(input_dir + 'combined.human.mtx').T.tocsc()
genes = np.array(scr.load_genes(input_dir + 'genes.tsv', delimiter='\t', column=0))
print('Counts matrix shape: {} rows, {} columns'.format(counts_matrix.shape[0], counts_matrix.shape[1]))
print('Number of genes in gene list: {}'.format(len(genes)))
#Counts matrix shape: 12865 rows, 32738 columns
#Number of genes in gene list: 32738
scrub = scr.Scrublet(counts_matrix, expected_doublet_rate=0.06)
doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=30,
get_doublet_neighbor_parents=True)
import scrublet as rc import matplotlib.pyplot as plt import scipy.io from scipy.sparse import csc_matrix import numpy as np wd = "/restricted/projectnb/camplab/home/syyang/contamination/data/pbmc/4k/" counts = scipy.io.mmread( wd + 'data/matrix.mtx' ) geneIndex = ((counts > 2).sum( axis = 1 ) > 2 ) counts_filter = counts.toarray()[ np.array(geneIndex).reshape(-1), : ] print( counts_filter.shape) counts_csc = csc_matrix( counts_filter.T ) genes = np.array( rc.load_genes( wd + 'data/genes.tsv', delimiter='\t', column=1)) [ np.array(geneIndex).reshape(-1) ] scrub = rc.Scrublet(counts_csc, expected_doublet_rate=0.06) doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=2, min_cells=3, min_gene_variability_pctl=85, n_prin_comps=30) np.save( "doublet_scores.npy", doublet_scores ) np.save( "predicted_doublets.npy", predicted_doublets * 1 )
print('Arguments:', len(sys.argv))
print('List:', str(sys.argv))
var_number = float(sys.argv[3])
print(var_number)
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Arial'
plt.rc('font', size=14)
plt.rcParams['pdf.fonttype'] = 42
## Basic run with scrublet
input_dir = os.path.join(sys.argv[1])
counts_matrix = scipy.io.mmread(input_dir + 'matrix.mtx').T.tocsc()
genes = np.array(
scr.load_genes(input_dir + 'genes.tsv', delimiter='\t',
column=1)) # Use with the raw data
print('Counts matrix shape: {} rows, {} columns'.format(
counts_matrix.shape[0], counts_matrix.shape[1]))
print('Number of genes in gene list: {}'.format(len(genes)))
scrub = scr.Scrublet(counts_matrix,
expected_doublet_rate=0.15,
sim_doublet_ratio=2)
doublet_scores, predicted_doublets = scrub.scrub_doublets(
min_counts=2,
min_cells=150,
min_gene_variability_pctl=var_number,
n_prin_comps=30)
scrub.call_doublets(threshold=0.40)
outdir = sys.argv[2]
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import os
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Arial'
plt.rc('font', size=14)
plt.rcParams['pdf.fonttype'] = 42
tag = 'FFT4G_10x'
output_dir = '/home/jovyan/snSeq_QCandAnalysis/scrublet'
input_dir = '/home/jovyan/data/snQCandAnalysis/FFT4G_10x/filtered_feature_bc_matrix'
counts_matrix = scipy.io.mmread(input_dir + '/matrix.mtx.gz').T.tocsc()
genes = np.array(
scr.load_genes(input_dir + '/features.tsv', delimiter='\t', column=1))
print('Counts matrix shape: {} rows, {} columns'.format(
counts_matrix.shape[0], counts_matrix.shape[1]))
print('Number of genes in gene list: {}'.format(len(genes)))
scrub = scr.Scrublet(counts_matrix, expected_doublet_rate=0.06)
doublet_scores, predicted_doublets = scrub.scrub_doublets(
min_counts=2, min_cells=3, min_gene_variability_pctl=85, n_prin_comps=30)
predicted_doublets = predicted_doublets * 1
predicted_doublets = predicted_doublets.astype(int)
detected_doublets_rate = round(scrub.detected_doublet_rate_, 4)
overall_doublets_rate = round(scrub.overall_doublet_rate_, 4)
np.savetxt(output_dir + '/' + tag + '_' + 'doublets_scores.txt',
if len(sys.argv) == 1:
print('input file prefix: python scrublet_doublet.py FEL011_S')
exit()
else:
prefix = sys.argv[1]
print(prefix)
#plt.rcParams['font.family'] = 'sans-serif'
#plt.rcParams['font.sans-serif'] = 'Arial'
plt.rc('font', size=14)
plt.rcParams['pdf.fonttype'] = 42
counts_matrix = scipy.io.mmread(prefix + '.matrix.mtx').T.tocsc()
genes = np.array(
scr.load_genes(prefix + '.genes.tsv', delimiter='\t', column=0))
cells = pd.read_table(prefix + '.barcodes.tsv', header=None)
cells.columns = ["Cell.ID"]
print('Counts matrix shape: {} rows, {} columns'.format(
counts_matrix.shape[0], counts_matrix.shape[1]))
print('Number of genes in gene list: {}'.format(len(genes)))
#indexnames = list(counts_matrix.index)
#columnnames = list(counts_matrix.columns)
#print('10 index values: {}'.format(indexnames[1:10]))
#print('10 column values: {}'.format(counts_matrix[1:3,1:3]))
scrub = scr.Scrublet(counts_matrix, expected_doublet_rate=0.06)
def main():
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog='author: {0} mail: {1}'.format(__author__, __mail__))
parser.add_argument('-m',
'--mtx',
help='cellranger分析结果中的matrix.mtx',
dest='mtx',
required=True)
parser.add_argument('-f',
'--feature',
help='cellranger分析结果中的feature.csv或genes.csv',
dest='feature',
required=True)
parser.add_argument('-o',
'--outdir',
help='结果输出目录',
dest='outdir',
required=True)
parser.add_argument('-s',
'--sampleName',
help='样本名',
dest='sampleName',
required=True)
parser.add_argument('-e',
'--expectedDoubletRate',
help='细胞结团率',
dest='expectedDoubletRate',
type=float,
required=True)
parser.add_argument('-p',
'--pc',
help='PC值',
dest='pc',
type=int,
default=30)
args = parser.parse_args()
logging.basicConfig(
level=logging.DEBUG,
format=
"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s"
)
logging.info("开始分析")
# plt.rcParams['font.family'] = 'sans-serif'
# plt.rcParams['font.sans-serif'] = 'Arial'
# plt.rc('font', size=14)
# plt.rcParams['pdf.fonttype'] = 42
pc = args.pc
expectedDoubletRate = args.expectedDoubletRate
sampleName = args.sampleName
#Load counts matrix and gene list
counts_matrix = scipy.io.mmread(args.mtx).T.tocsc()
genes = np.array(scr.load_genes(args.feature, delimiter='\t', column=1))
scrub = scr.Scrublet(counts_matrix,
expected_doublet_rate=expectedDoubletRate)
#Run the default pipeline
doublet_scores, predicted_doublets = scrub.scrub_doublets(
min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=pc)
#Plot doublet score histograms for observed transcriptomes and simulated doublets
scrub.call_doublets(threshold=0.1)
scrub.plot_histogram()
plt.savefig(args.outdir + "/" + sampleName + '_Scrublet_Histogram.png')
scrub.set_embedding('UMAP',
scr.get_umap(scrub.manifold_obs_, 10, min_dist=0.3))
scrub.plot_embedding('UMAP', order_points=True)
plt.savefig(args.outdir + "/" + sampleName + '_Scrublet_UMAP.png')
#output the log file
#expected doublet rate、 detected doublet rate、 doublet threshold、overall doublet rate
logging.info(
"patientID\texpected_doublet_rate\tdetected_doublet_rate\toverall_doublet_rate\tthreshold\tPC\n"
)
logging.info(
"%s\t%.4f\t%.4f\t%.4f\t%.4f\t%s\n" %
(sampleName, scrub.expected_doublet_rate, scrub.detected_doublet_rate_,
scrub.overall_doublet_rate_, scrub.threshold_, pc))
#output the doublet status of every single cell
with open(args.outdir + "/" + sampleName + ".predictDoublet_scrublet.txt",
"w+") as fo:
for i in scrub.predicted_doublets_:
fo.write("%s\n" % (i))
import os
import time
import sys
input_dir = sys.argv[1] + "/"
# The raw counts matrix (E) should be a scipy sparse CSC matrix
# with cells as rows and genes as columns
if os.path.isfile(input_dir + '/gene_count.npz'):
E = scipy.sparse.load_npz(input_dir + '/gene_count.npz')
else:
E = scipy.io.mmread(input_dir + '/gene_count.mtx').T.tocsc()
scipy.sparse.save_npz(input_dir + '/gene_count.npz', E, compressed=True)
genes = np.array(scr.load_genes(input_dir + 'df_gene.tsv', delimiter='\t', column=1))
print('Expression matrix shape: {} rows, {} columns'.format(E.shape[0], E.shape[1]))
print('Number of genes in gene list: {}'.format(len(genes)))
scrub = scr.Scrublet(E, expected_doublet_rate=0.05)
doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=2,
min_cells=3,
min_gene_variability_pctl=85,
n_prin_comps=30)
scrub.call_doublets(threshold=0.22)
scrub.plot_histogram()
plt.savefig(input_dir + "/hist1.png")
print('Running UMAP...')
scrub.set_embedding('UMAP', scr.get_umap(scrub.manifold_obs_, 10, min_dist=0.3))
plt.savefig(input_dir + "/umap.png")