ivirshup

Status	SRR950078	SRR950079	SRR950080	SRR950081	SRR950082	SRR950083	SRR950084	SRR950085	SRR950086	SRR950087
1	Assigned	771600	758170	771620	782006	709176	769418	762469	775637	817422	811414
2	Unassigned_Ambiguity	0	0	0	0	0	0	0	0	0	0
3	Unassigned_MultiMapping	0	0	0	0	0	0	0	0	0	0
4	Unassigned_NoFeatures	143539	140245	137103	142209	129824	146504	131665	144008	107762	122946
5	Unassigned_Unmapped	81716	98620	88046	72146	157535	80385	102452	76928	71142	62661
6	Unassigned_MappingQuality	3145	2965	3231	3639	3465	3693	3414	3427	3674	2979
7	Unassigned_FragmentLength	0	0	0	0	0	0	0	0	0	0
8	Unassigned_Chimera	0	0	0	0	0	0	0	0	0	0
9	Unassigned_Secondary	0	0	0	0	0	0	0	0	0	0

ivirshup

from subprocess import run, PIPE
import requests
import pandas as pd
from pathlib import Path

BASE_URL = "http://imlspenticton.uzh.ch/robinson_lab/conquer/"

def curl(inpth, outpth=None):
    command = ["curl", str(inpth)]
    if outpth:

ivirshup

import matplotlib
matplotlib.use('Agg')  # plotting backend compatible with screen
import scanpy.api as sc
from argparse import ArgumentParser

sc.settings.verbosity = 4  # show logging output

def main():
    parser = ArgumentParser(
        description="Reads in 10x hdf5 file and write a h5ad file.")

ivirshup

import scanpy
import scanpy.api as sc
import numpy as np
from scipy import random, sparse
from time import sleep
import sklearn.utils.sparsefuncs as sparsefuncs

@profile
def current_dense(X):
    return scanpy.preprocessing.simple._get_mean_var(X)

ivirshup

import holoviews as hv
import numpy as np
import pandas as pd
hv.extension("bokeh")

# Generate data
base_df = pd.DataFrame({
    "x": np.random.randn(100), "y": np.random.randn(100),
    "cat1": np.random.randint(0, 5, 100), "cat2": np.random.randint(0, 5, 100),
    "con1": np.random.randn(100), "con2": np.random.randn(100)})

ivirshup

name: scanpy_scatter_ring
channels:
  - defaults
  - r
  - bioconda
  - conda-forge
dependencies:
  - anndata=0.6.10=py_0
  - igraph=0.7.1=hcc8e21d_5
  - louvain=0.6.1=py36hfc679d8_1

ivirshup

{
    "_meta": {
        "hash": {
            "sha256": "aa4e26d643aa1ae7337e67cf9da430773141ac3e9930891ae0862d948e73b6d5"
        },
        "pipfile-spec": 6,
        "requires": {
            "python_version": "3.6"
        },
        "sources": [

ivirshup

def fix_names(adata):
    adata.var_names_make_unique()
    adata.var["gene_symbols"] = adata.var.index
    adata.var.index = adata.var["gene_ids"]

ivirshup

import anndata
from scipy import sparse
import pandas as pd
import numpy as np

from pathlib import Path
from tqdm import tqdm
from urllib.request import urlretrieve
from zipfile import ZipFile

ivirshup

import anndata
from scipy import sparse
import pandas as pd
import numpy as np

from pathlib import Path
from tqdm import tqdm
from urllib.request import urlretrieve
from zipfile import ZipFile