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import scanpy as sc
import pandas as pd
import GEOparse

def gse_to_adata(gse):
    X = gse.pivot_samples("VALUE")
    key = list(gse.gpls.keys())[0]
    annotated = X.index.to_frame(index=False).merge(
        gse.gpls[key].table[["ID", "Gene Symbol"]], 
        left_on="ID_REF", right_on="ID"

wflynny

Introduction

This process has gotten simpler with 10X Genomics Loupe Browser 5.0.0+. I try to keep a cache of all 10X downloads internal to JAX here (must be on the VPN):

http://singlecell-software.s3-far.jax.org/list.html

where you can download the latest version of Loupe Browser for your OS. Otherwise, head over to the 10X site:

https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest#loupe

wflynny

###################
# chord diagram
import matplotlib.pyplot as plt
from matplotlib.path import Path as mplPath
import matplotlib.patches as patches

import numpy as np
import pandas as pd

LW = 0.3

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from matplotlib.text import Annotation
from matplotlib.legend_handler import HandlerBase
from matplotlib import patheffects
from matplotlib.colors import CSS4_COLORS, hex2color

class AnnotationHandler(HandlerBase):
    def create_artists(self, legend, artist, xdescent, ydescent,
                       width, height, fontsize, trans):
        a = Annotation(
            artist.get_text(), [width/2, height/2],

wflynny

import re
from pathlib import Path
from xml.etree import ElementTree as ET

import numpy as np
from skimage import exposure
from skimage.io import imread, imsave, imshow
from skimage.filters import threshold_otsu, gaussian

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import re
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-i", "--infile", required=True)
parser.add_argument("-o", "--outfile", required=True)
args = parser.parse_args()

gene_matcher = re.compile('\tgene\t.*gene_id (".*?");.*Name (".*?");')
parent_matcher = re.compile('gene_id (".*?");.*Parent (".*?");')

wflynny

#!/usr/bin/env bash

TEMP=$(getopt -o hsg --long help,snapshot,gpu -n 'susuage' -- "$@")

if [ $? != 0 ] ; then echo "Terminating..." >&2 ; exit 1 ; fi

# Note the quotes around `$TEMP': they are essential!
eval set -- "$TEMP"

SNAPSHOT=false

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from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import scanpy as sc

def load_hto_matrix(mtx_dir):
    raw_htos = sc.read_mtx(mtx_dir + "/matrix.mtx.gz").T
    raw_htos.var = pd.read_csv(mtx_dir + "/features.tsv.gz", header=None, index_col=0)
    raw_htos.obs = pd.read_csv(mtx_dir + "/barcodes.tsv.gz", header=None, index_col=0)
    raw_htos = raw_htos[:, ~raw_htos.var_names.isin(["unmapped"])]

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_grab_ip() {
  jobid=$1
  port=$2
  hostname=$(qstat -f ${jobid} | grep -oP "exec_host = (\K[a-z0-9]+)")
  echo "http://${hostname}:${port}"
}

_submit_job() {
  queue=$1
  port=$2

wflynny

#!/usr/bin/env bash
#### PBS preamble

#PBS -N jupyter-server
#PBS -o /path/to/software/logs/jupyter-server.${PBS_JOBID%%.*}.out

#PBS -j oe
#PBS -m n

#PBS -l mem=128GB