| import sys | |
| import openai | |
| import json | |
| import pandas as pd | |
| from Bio import Entrez | |
| from datetime import datetime | |
| from PyQt6 import QtWidgets, QtGui | |
| from PyQt6.QtCore import Qt | |
| # Replace with your own ChatGPT API key |
| # HGEN 473 - Genomics | |
| # Spring 2017 | |
| # Tuesday, May 9 & Thursday, May 11 | |
| # | |
| # RNA-seq analysis with R/Bioconductor | |
| # | |
| # John Blischak | |
| # Introduction ------------------------------------------------------- |
The IPython Notebook has two different keyboard input modes. Edit mode allows you to type code/text into a cell and is indicated by a green cell border. Command mode binds the keyboard to notebook level actions and is indicated by a grey cell border.
| R to python useful data wrangling snippets | |
| The dplyr package in R makes data wrangling significantly easier. | |
| The beauty of dplyr is that, by design, the options available are limited. | |
| Specifically, a set of key verbs form the core of the package. | |
| Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. | |
| Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. | |
| The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package). | |
| dplyr is organised around six key verbs |
| library(devtools) | |
| library(sleuth) | |
| library(dplyr) | |
| library("biomaRt") | |
| #load annotation from ensembl | |
| ensembl = useMart("ENSEMBL_MART_ENSEMBL",dataset="mmusculus_gene_ensembl", host="www.ensembl.org") | |
| t2g <- biomaRt::getBM(attributes = c("ensembl_transcript_id", "ensembl_gene_id", "external_gene_name"), mart = ensembl) | |
| t2g <- dplyr::rename(t2g, target_id = ensembl_transcript_id, ens_gene = ensembl_gene_id, ext_gene = external_gene_name) | |
| #change this directory as your directory |
This is a tutorial I have presented for the class Genomics and Systems Biology at the University of Chicago. In this course the students learn about study design, normalization, and statistical testing for genomic studies. This is meant to introduce them to how these ideas are implemented in practice. The specific example is a differential expression analysis with edgeR starting with a table of counts and ending with a list of differentially expressed genes.
Past versions:
| /* | |
| * ------------------------------------------------------------ | |
| * "THE BEERWARE LICENSE" (Revision 42): | |
| * <author> wrote this code. As long as you retain this | |
| * notice, you can do whatever you want with this stuff. If we | |
| * meet someday, and you think this stuff is worth it, you can | |
| * buy me a beer in return. | |
| * ------------------------------------------------------------ | |
| */ |
| "=============================================================================== | |
| "FILE: myvimrc | |
| "CREATED BY: sooop | |
| "LAST UPDATEd: 2014. 04. 23. | |
| "DESCRIPTION: | |
| " my vimrc settting | |
| " github repository : https://github.com/sooop/myvimrc.git | |
| "=============================================================================== | |
| class ParseFastQ(object): | |
| """Returns a read-by-read fastQ parser analogous to file.readline()""" | |
| def __init__(self,filePath,headerSymbols=['@','+']): | |
| """Returns a read-by-read fastQ parser analogous to file.readline(). | |
| Exmpl: parser.next() | |
| -OR- | |
| Its an iterator so you can do: | |
| for rec in parser: | |
| ... do something with rec ... |