afrendeiro

import os
import pandas as pd
from argparse import ArgumentParser


# Parse command-line arguments
parser = ArgumentParser(
    prog="CSV parser",
    description="Gets some column out of CSVs."
)

afrendeiro

for GENOME in hg19 mm10 danRer10
do
    # Static files
    mkdir -p resources/${GENOME}
    cd resources/${GENOME}
    ### Genome
    wget http://hgdownload.cse.ucsc.edu/goldenPath/${GENOME}/bigZips/${GENOME}.2bit
    twoBitToFa ${GENOME}.2bit ${GENOME}.fa
    samtools faidx ${GENOME}.fa
    cd ../..

afrendeiro

# Install zimba
R
install.packages(c("R.oo"))
install.packages(c("R.utils", "quantreg","doParallel","doMC","foreach"))  # for R>3.0
# only version that works with R 3.0:
# get it from here: https://code.google.com/p/zinba/issues/detail?id=69
install.packages("zinba_2.03.1.tar.gz", repos=NULL)

# Make bed files from bams
# system("bedtools bamtobed -i /data/mapped/sample.bam > ~/zinba/reads/sample.bed")

afrendeiro

CURRENT_DATE=20181212
NUMBER_MAIN_FIGURES=5
NUMBER_SUPP_FIGURES=11
ROOT_DIR=/home/path/to/paper/figures

cd $ROOT_DIR
mkdir -p cropped_unlabeled_pngs

# Cell Press format convertion

afrendeiro

# Change this
CURRENT_DATE=20181212
NUMBER_MAIN_FIGURES=5
NUMBER_SUPP_FIGURES=11
ROOT_DIR=/home/path/to/paper/figures

# Don't change this
cd $ROOT_DIR
mkdir -p cropped_unlabeled_pngs

afrendeiro

def plot_differential_enrichment(
        enrichment_table,
        enrichment_type,
        data_type="ATAC-seq",
        direction_dependent=True,
        output_dir="results/differential_analysis_{data_type}/enrichments",
        comp_variable="comparison_name",
        output_prefix="differential_analysis",
        rasterized=True,

afrendeiro

>chr1
ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGC
ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGC

afrendeiro

import pandas as pd
from sklearn.preprocessing import StandardScaler

# get list of genes of interest (e.g. from diff exp, or a known signature)
diff = pd.Index(['geneX', 'geneY'])

# X is our numeric matrix, shape (n_features, n_samples)
X = pd.DataFrame(index=['geneA', 'geneZ'], columns=['stimul_1', 'stimul_2'] + ['unstimul_1', 'unstimul_2'])

# Standardize and center

afrendeiro

import scanpy.api as sc
import numpy as np

def add_tracker(field_name="tracker"):
    from inspect import getcallargs
    from functools import wraps

    def tracker(f):
        @wraps(f)
        def wrapper(*args, **kwds):

afrendeiro

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