André F. Rendeiro afrendeiro

## make_validate_pdfa_muw.sh
#!/usr/bin/bash

# This script details an example of how to create an validate a PDF/A
# compatible PDF file for submission as a thesi at the Medical University
# of Vienna

# It assumes in the working directory there are ${PAPER}.initial.pdf files
# that will make up your cumulative thesis

# The first cause of failure for PDF/A compatibility is the PDFs from your

## chipseq_pipeline.py
#!/usr/bin/env python

"""
ChIP-seq pipeline
"""

from argparse import ArgumentParser
import os
import sys
import logging

## distributions_gini_lorenz_curves.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


def gini(x):
    # Mean absolute difference
    mad = np.abs(np.subtract.outer(x, x)).mean()
    # Relative mean absolute difference
    rmad = mad / np.mean(x)

## 10Xfastq_to_bam.py
#! /usr/bin/env python


"""
Convert 10X FASTQ files to single end BAM file with barcodes annotated as tags
"""


from argparse import ArgumentParser
from glob import glob

## keybase.md

      
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                afrendeiro
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              Created Mar 12, 2019
            
              
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## add_tracking_to_scanpy.py
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):

## simple_score_within_signature.py
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

## fasta.fa
>chr1
ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGC
ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGC

## plot_homer_consensus.py

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,

## process_figures_for_paper.sh
#!/usr/bin/env bash

# Dependencies:
# 1. inkscape
# 2. pdfunite
# 3. (optinal) minify (https://github.com/tdewolff/minify/tree/master/cmd/minify)
#    `sudo apt install minify`

# Assumptions:
# 1. figures are made in SVG format
	#!/usr/bin/bash

	# This script details an example of how to create an validate a PDF/A
	# compatible PDF file for submission as a thesi at the Medical University
	# of Vienna

	# It assumes in the working directory there are ${PAPER}.initial.pdf files
	# that will make up your cumulative thesis

	# The first cause of failure for PDF/A compatibility is the PDFs from your
	#!/usr/bin/env python

	"""
	ChIP-seq pipeline
	"""

	from argparse import ArgumentParser
	import os
	import sys
	import logging
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt


	def gini(x):
	# Mean absolute difference
	mad = np.abs(np.subtract.outer(x, x)).mean()
	# Relative mean absolute difference
	rmad = mad / np.mean(x)
	#! /usr/bin/env python


	"""
	Convert 10X FASTQ files to single end BAM file with barcodes annotated as tags
	"""


	from argparse import ArgumentParser
	from glob import glob
	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):
	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
	>chr1
	ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGC
	ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGC

	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,
	#!/usr/bin/env bash

	# Dependencies:
	# 1. inkscape
	# 2. pdfunite
	# 3. (optinal) minify (https://github.com/tdewolff/minify/tree/master/cmd/minify)
	# `sudo apt install minify`

	# Assumptions:
	# 1. figures are made in SVG format