PeteHaitch

Combining SummarizedExperiment objects

Peter Hickey
20 October 2015

Motivation

I often find myself with multiple SE objects (I'm using SE as a shorthand for the SummarizedExperiment0 and RangedSummarizedExeriment classes), each with potentially non-distinct samples and potentially non-overlapping features/ranges. Currently, it is difficult to combine these objects; rbind() can only combine objects with the same samples but distinct features/ranges and cbind() can only combine objects with the same features/ranges but distinct samples. I think it would be useful to have a "combine" method for SE objects that handles the most general situation where each object has potentially non-distinct samples and potentially non-overlapping features/ranges.

jeroen

# Globals
repos <- 'https://cloud.r-project.org'
pkgdata <- available.packages(repos = repos)
pkgs <- row.names(pkgdata)

# On success
make_callback <- function(i, url){
  function(res){
    if(res$status == 200){
      buf <- rawConnection(res$content)

dpseidel

Default ggplot2 aesthetics by geom.

geom	alpha	angle	colour	family	fill	fontface	height	hjust	lineheight	linetype	shape	size	stroke	vjust	weight	width	xmax	xmin	ymax	ymin
GeomAbline	NA		black							1		0.5
GeomAnnotationMap	NA		NA		grey20					1		0.5
GeomArea	NA		NA		grey20					1		0.5
GeomBar	NA		NA		grey35					1		0.

ajstewartlang

# Usimg @micahgallen's nice raincloud plot code 
# https://wellcomeopenresearch.org/articles/4-63
# and fork of benmarwick/geom_flat_violin.R code
# gganimate by Thomas Lin Pedersen - @thomasp85

devtools::install_github('thomasp85/gganimate')

library(tidyverse)
library(gganimate)
library(RColorBrewer)

jxtx

Conference info: https://bioc2018.bioconductor.org/

My first Bioconductor meeting, and I'm not a BioC or R expert so these notes are probably going to be naïve!

Contents

• Developer Day
  • ⚡️talks II
  • BoFs
• ⚡️ III

juliasilge

## this analysis assumes a dataframe `post_views` with columns:
##     PostId
##     CreationDate
##     Tag
##     AnswerCount
##     ViewCount

library(tidyverse)

post_views %>%

emitanaka

<script>
(function() {
  var divHTML = document.querySelectorAll(".details-open");
  divHTML.forEach(function (el) {
    var preNodes = el.getElementsByTagName("pre");
    var outputNode = preNodes[1];
    outputNode.outerHTML = "<details open class='output'><summary>Output</summary>" + outputNode.outerHTML + "</details>";
  })
})();
(function() {

alexpghayes

``` r
library(Matrix)

alpha = 0.05

A <- rsparsematrix(1000, 1000, 0.1)

s <- as.data.frame(summary(A))
p <- abs(s$x) / sum(abs(s$x))   # probabilities proportional to abs(entry)

rcastelo

## this file contains 4 functions using several R and Bioconductor
## packages to help identifying scarcely used R package dependencies
## representing a large installation burden. it has been created
## upon the following discussion thread on the bioc-devel mailing list:
## https://stat.ethz.ch/pipermail/bioc-devel/2020-February/016146.html
##
## to use these functions you need to build a database of package
## information using the following lines:
##
## repos <- BiocManager::repositories()[c("BioCsoft", "CRAN")]

dieghernan

# Setup----

library(rtweet)

source("~/R/Projects/environment_vars.R") # secrets
api_key <- Sys.getenv("TWITTER_API_KEY")
api_secret_key <- Sys.getenv("TWITTER_API_SECRET")
access_token <- Sys.getenv("TWITTER_ACCESS_TOKEN")
access_token_secret <- Sys.getenv("TWITTER_ACCESS_TOKEN_SECRET")