Ino de Bruijn inodb

Convert SingleCellExperiment to H5ad

This worked 2022/06/16. Run:

docker run -v ${PWD}:/pwd -it bioconductor/bioconductor_docker:RELEASE_3_15 /bin/sh

cd /pwd
R

Gene lenghts from ENSEMBL REST API

Use inside notebook (uses ! syntax):

import json
import pandas as pd

def get_gene_length(gene):
    gene_info = \
        !curl -s "http://grch37.rest.ensembl.org/xrefs/symbol/homo_sapiens/"{gene} -H 'Content-type:application/json' | \
 jq '.[0].id' | tr -d '"' | \

20161002 Frontend dev workshop

Set up cbioportal-frontend

Check out the README at https://github.com/cBioPortal/cbioportal-frontend/ for up to date info

Install with npm
Try to view new patient view at this link (make sure to change routing to patient): http://localhost:3000/?cancer_study_id=lgg_ucsf_2014&case_id=P04

Try to make a component

Make a new component that show the Patient Information on a single line, instead of in a table. We would like that line to be in the PatientHeader component. You could name it PatientInline similar to SampleInline.

	"""
	Check if studies on datahub are imported in live public portal

	Currently comparing study folder name on datahub vs live public portal studies
	endpoint. Should prolly look at actual `cancer_study_identifier: study_name` in
	meta_study.txt file, but this seems to work.
	"""
	import requests

	public_study_folders = requests.get("https://api.github.com/repos/cBioPortal/datahub/contents/public")

	<html>
	<body>
	I'm on github
	</body>
	</html>

	<!doctype HTML>
	<meta charset = 'utf-8'>
	<html>
	<head>
	<link rel='stylesheet' href='data:text/css;base64,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

	"""
	Output rows from table1 that are missing from table2 i.e. table1 - table2, use
	given columns to determine 'missing'
	"""
	import argparse
	import pandas as pd
	import sys


	def output_missing_rows(t1, t2, join_columns, delimiter, output_only_columns):