Nick Waters nickp60

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Monday 09 April, Morning

Pre-Conference session: Teaching microbiology in higher education

This half-day symposium will present several interactive sessions which aim to bring delegates together to discuss and share innovative ways of teaching.

	make_results_plot <- function(path, DEBUG = FALSE, reorder=FALSE, qval_thresh=.1){
	alpha_levels <- c(".0001 or less", ".001", ".01", ".05", ".05 or above")
	alpha_breaks <- rev(c(.09, .5, .62, .7, .85))
	nonscaled <- read.csv(path, sep="\t")
	if(nrow(nonscaled) == 0){
	warning("Error running maaslin, results file empty")
	return(NULL)
	}
	any_sig <- nonscaled %>% group_by(feature) %>% filter(any(qval < qval_thresh)) %>% pull(feature) %>% unique() #changed any qval <.5 from .05
	if (length(any_sig) == 0){

	library(tidyverse)
	if (!"speedyseq" %in% installed.packages()) remotes::install_github("mikemc/speedyseq")
	library(speedyseq)

	download.file("https://figshare.com/ndownloader/files/33076496", "samples.tsv")
	#this file has duplicate rows, and has multiple rows per pool
	samples <- read.csv("samples.tsv") %>% distinct() %>%
	select(-Pool, -Run, -ShotgunBatchID) %>% distinct()
	#download.file("https://figshare.com/ndownloader/files/26770931", "metadata.tsv")
	#metadata <- read.csv("metadata.tsv")

	# Source this for a handy discrete color palette and various helper functions for microbiome analyses.
	# not complex enough for an actual package. For now... #featurecreep
	mycolors=c(
	"#511087", "#4ecdc4", "#ffd447", "#13c01f", "#b78900", "#e66d17",
	"#8e3400", "#729b79", "#bf0000", "#907ad6", "#3f6356", "#ffbfb7",
	"#92bccc", "#c0e8ab", "#890466", "#382438", "#dad2bc", "#0048ff",
	"#f5f749", "#146d25", "#c08552", "#fc60a8", "#89fc00", "#3df8ff",
	"#ef2917", "#101f75", "#bfcccc"
	)

	searchres <- "~/Downloads/SearchResults.csv"
	outdir <- "~/Downloads/springercovid/"
	dir.create(outdir)

	resdf <- read.csv(searchres, stringsAsFactors = FALSE)

	resdf$dloadurl <- paste0("http://link.springer.com/content/pdf/",
	gsub("\\/", "%2F", fixed=TRUE, resdf$Item.DOI), ".pdf")
	resdf$local_file <- paste0(gsub(" +", "_", gsub("[^[:alnum:][:space:]]", "", resdf$Item.Title)), ".pdf")

	# I made some slighlty bigger test data to make sure we had some we weren't interested in
	# here are all the dates in the two years between dec 2011 and 2013
	all_dates <- seq(as.Date("2011-12-15"), as.Date("2013-12-15"), "day")

	# like you did, randomly fill in value, and randomly sample for the available nodes
	bigtest <- data.frame(
	time = all_dates,
	node = sample(c(206,211,301,108), size = length(all_dates), replace=T),
	value = rnorm(length(all_dates), 21, 2)
	)

	#!/usr/bin/env python
	"""
	Gist of how to make similar plots to the entropy plots in riboSeed from an MSA.
	This assumes you want to annotate rDNAs, but you probably don't
	care about those. Adjust as needed.

	"""
	import os
	import datetime
	import time

	# The purpose of this script it to make working from the terminal a bit more reprodicible.
	# It is very simple
	# A boolean value (KPTRK_ON) is set to true when kptrk is invoked, and the current directory is recorded.
	# when kptrk is invoked again, logging is turned off.

	# it is built on top of https://github.com/rcaloras/bash-preexec

	# Installation
	# - install bash-pre-exec, see instructions here https://github.com/rcaloras/bash-preexec, or, in short:
	# -- Pull down our file from GitHub and write it to our home directory as a hidden file.

	#!/usr/bin/env python3
	#-- coding: utf-8 --
	import math
	import random
	import sys
	from bisect import bisect

	def printPlot(data, line=None, y=30, x=60, tick=.2, title="test", logger=None):
	yax = "\|"
	xax = "_"