| metaphlanToPhyloseq <- function( | |
| metaphlandir, | |
| metadat=NULL, | |
| simplify=TRUE){ | |
| ## tax is a matrix or data.frame with the table of taxonomic abundances, rows are taxa, columns are samples | |
| ## metadat is an optional data.frame of specimen metadata, rows are samples, columns are variables | |
| ## if simplify=TRUE, use only the most detailed level of taxa names in the final object | |
| ## metaphlanToPhyloseq("~/Downloads/metaphlan_bugs_list") | |
| .getMetaphlanTree <- function(removeGCF=TRUE, simplify=TRUE){ | |
| if (!requireNamespace("ape")) { |
| #' Read bismark coverage file as a methylKit object | |
| #' | |
| #' Bismark aligner can output methylation information per base in | |
| #' multiple different formats. This function reads coverage files, | |
| #' which have chr,start,end, number of cytosines (methylated bases) | |
| #' and number of thymines (unmethylated bases). | |
| #' | |
| #' @param location a list or vector of file paths to coverage files | |
| #' | |
| #' @param sample.id a list or vector of sample ids |
| ## RNA-seq analysis with DESeq2 | |
| ## Stephen Turner, @genetics_blog | |
| # RNA-seq data from GSE52202 | |
| # http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse52202. All patients with | |
| # ALS, 4 with C9 expansion ("exp"), 4 controls without expansion ("ctl") | |
| # Import & pre-process ---------------------------------------------------- | |
| # Import data from featureCounts |
| # two scales in one diag (not always recommended, but I WANT that now) | |
| # some reading up: https://stackoverflow.com/questions/3099219/plot-with-2-y-axes-one-y-axis-on-the-left-and-another-y-axis-on-the-right/3101876# | |
| library(ggplot2) | |
| library(lubridate) | |
| dt <- data.frame(when=ymd(c(20180320,20180321, 20180323, 20180324, 20180325)), numinter=c(1, 5, 4, 3, 4), prod=c(0.95, 0.5, 0.7, 0.75, 0.6)) # pure fiction | |
| ggplot() + | |
| geom_bar(mapping = aes(x = dt$when, y = dt$numinter), stat = "identity", fill = "grey") + | |
| geom_line(mapping = aes(x = dt$when, y = dt$prod*5), size = 2, color = "blue") + | |
| scale_x_date(name = "Day", labels = NULL) + | |
| scale_y_continuous(name = "Interruptions/day", sec.axis = sec_axis(~./5, name = "Productivity % of best", labels = function(b) { paste0(round(b * 100, 0), "%")})) + |
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:
| #!/usr/bin/python | |
| import os | |
| import sys | |
| import boto3 | |
| # get an access token, local (from) directory, and S3 (to) directory | |
| # from the command-line | |
| local_directory, bucket, destination = sys.argv[1:4] |
| FILE SPACING: | |
| # double space a file | |
| sed G | |
| # double space a file which already has blank lines in it. Output file | |
| # should contain no more than one blank line between lines of text. | |
| sed '/^$/d;G' |
| #!/usr/bin/env bash | |
| # memusg -- Measure memory usage of processes | |
| # Usage: memusg COMMAND [ARGS]... | |
| # | |
| # Author: Jaeho Shin <netj@sparcs.org> | |
| # Created: 2010-08-16 | |
| ############################################################################ | |
| # Copyright 2010 Jaeho Shin. # | |
| # # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); # |
To install a custom package or modify an existing docker image we need to
