Liang-Bo Wang ccwang002

Gene	Cancer	Tumor suppressor or oncogene prediction (by 20/20+)	Decision	Tissue Frequency	Pancan Frequency	Consensus Score	Correlation adusted score	Novel	Rescue Notes	Note about previous publication
ARID1A	CHOL		official	11.76%	6.69%	2.5	1.80	0		Found in 28297679
BAP1	CHOL	tsg	official	17.65%	2.14%	3.5	2.80	0		Found in 28297679
EPHA2	CHOL	tsg	official	11.76%	1.58%	2.5	2.50	0		0
IDH1	CHOL	oncogene	official	14.71%	5.56%	4.5	3.80	0		Found in 28297679
PBRM1	CHOL	tsg	official	17.65%	3.73%	3.5	2.32	0		0

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[TOC]

Lab Guide for Coding Beginners

亮亮（@ccwang002）| Mar, 2015 | CC 3.0 BY license

如果內容有誤，你可以用任何管道發訊息轟炸我，或用底下的 gist comment 留言。

學習方式

每個檔案都會是一個主題，主題底下會列出一些資源。資源的最後會有一個學習目標，方便讓你評估自己學到什麼程度。學習目標會給一個明確的任務，我盡量讓它能跟（宅宅的）日常生活結合。通常只要完成前一、二個目標就行了，這也不是功課所以不一定要給我看。如果你不介意給我看，我會分享我主觀的建議，但大部份的任務是沒有絕對的正確答案。只要能解決問題都是好方法。

	"""Find the possible Ensembl releases of the given IDs.

	The script uses Ensembl Tark APIs to subset the possible Ensembl releases
	that cover all the given Ensembl IDs. Usually it can pinpoint the right release
	using less than 30 IDs. Feeding more IDs may exceed the API call rate limit.

	Known issues:
	* The API doesn't handle ENSGR (chrY PAR genes)
	"""
	import argparse

	from itertools import combinations, product


	def gen_pos_sets_to_sub(barcode, max_sub=1):
	"""
	Generate all the possible position combinations (sets) within
	the given maximal number of substitutions.

	Examples:

	from pathlib import Path
	from snakemake.remote.GS import RemoteProvider as GSRemoteProvider
	GS = GSRemoteProvider()


	GS_PREFIX = "lbwang-playground/snakemake_rnaseq"
	GENOME_FA = GS.remote(f"{GS_PREFIX}/griffithlab_brain_vs_uhr/GRCh38_Ens87_chr22_ERCC/chr22_ERCC92.fa")
	GENOME_GTF = GS.remote(f"{GS_PREFIX}/griffithlab_brain_vs_uhr/GRCh38_Ens87_chr22_ERCC/genes_chr22_ERCC92.gtf")
	HISAT2_INDEX_PREFIX = "hisat2_index/chr22_ERCC92"
	FULL_HISAT2_INDEX_PREFIX = "dinglab/lbwang/snakemake_demo/hisat2_index/chr22_ERCC92"

	# The Snakefile that loads raw data and genome reference locally
	GENOME_FA = "griffithlab_brain_vs_uhr/GRCh38_Ens87_chr22_ERCC/chr22_ERCC92.fa"
	GENOME_GTF = "griffithlab_brain_vs_uhr/GRCh38_Ens87_chr22_ERCC/genes_chr22_ERCC92.gtf"
	HISAT2_INDEX_PREFIX = "hisat2_index/chr22_ERCC92"

	SAMPLES, *_ = glob_wildcards('griffithlab_brain_vs_uhr/HBR_UHR_ERCC_ds_10pc/{sample}.read1.fastq.gz')

	from pathlib import Path

	import pandas as pd
	import os
	from pathlib import Path

	# Export Zotero library as CSV
	ZOTERO_LIBRARY_PTH = '/Users/liang/Desktop/My Library.csv'
	REFERENCES_ROOT = Path('/Users/liang/Dropbox/References/')


	df = pd.read_csv(ZOTERO_LIBRARY_PTH)

	from datetime import datetime
	from pytz import timezone # pip install pytz


	# Setup remote time
	remote_tz = timezone('US/Pacific') # PST for example
	remote_dt = remote_tz.localize(datetime(2015, 5, 1, 14, 0)) # May 1, 2015 PM2:00 PST

	# Setup Taipei local time
	tpe = timezone('Asia/Taipei')

	import numpy as np
	rs = np.random.RandomState(seed=5566)
	n_conditions = 10

	# Here we simulate a complex computation, for example, analogy of the magnitude
	# of gradient decent which expects to be strictly positive. But from the result
	# we find that it seems to be sometimes negative, we wish to find out when and
	# what condition our program produces bogus ouput.
	#
	# This is the case to use pdb and condition break point