| package main | |
| import ( | |
| "fmt" | |
| "log" | |
| "os" | |
| "bufio" | |
| "strings" | |
| "compress/gzip" | |
| ) |
| input = "1+2+3++4+++5++++6+++7+++9+8+++10" | |
| t = sum(map(int, (filter(bool, input.split("+"))))) | |
| import math | |
| def fsum(f): | |
| def apply(a, b): |
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:
| write.table( res, file="/Analysis/deseq/Dox_vs_DMSO.differentialExpression.csv",row.names = TRUE,col.names = TRUE,append = FALSE, quote = FALSE, sep = "\t",eol = "\n") | |
| write.table(res, "E_F.results.tsv", row.names=T, col.names = NA) | |
| write.csv(as.data.frame(res),file='sim_condition_treated_results_deseq2.csv') |
| args = commandArgs(trailingOnly=TRUE) | |
| if (length(args) != 2){ | |
| print("1 vs 2 DEG analysis for RSem output") | |
| stop("USAGE :: Rscript this.R [1.genes.results] [2.genes.results]") | |
| } | |
| # assign file names | |
| f1 <- args[1] | |
| f2 <- args[2] |
| 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 |
| import biomart | |
| server = biomart.BiomartServer( "http://useast.ensembl.org/biomart" ) | |
| server.is_alive |
| --- | |
| layout: page | |
| title: dplyr tutorial | |
| --- | |
| ```{r options, echo=FALSE} | |
| library(knitr) | |
| opts_chunk$set(fig.path=paste0("figure/", sub("(.*).Rmd","\\1",basename(knitr:::knit_concord$get('infile'))), "-")) | |
| ``` |
| library(ggplot2) | |
| library(RColorBrewer) | |
| library(reshape2) | |
| data.frame to melt | |
| # chnage columns names! | |
| names(ac.df) <- c("A", "C") | |
| colnames(ac.mat) <- c("A", "C") |
| import time | |
| def benchmark(func): | |
| def wrapper(*args, **kwargs): | |
| t = time.clock() | |
| res = func(*args, **kwargs) | |
| print("\t%s" % func.__name__, time.clock() - t) | |
| return res | |
| return wrapper |