💻

Florian Wuennemann FloWuenne

💻

Bioinformatics engineer at Seqera

FloWuenne / Run_clustering_module_Rds.R

Last active April 27, 2018 13:53

Run certain gists in R from GithubGist

	library(devtools)

	#### Specific datasets
	## Specific time point Rds
	source_gist("https://gist.github.com/FloWuenne/df2dca6804f562ed90db26a04508af31", filename = "Create_clustering_module_Rds.R")

	## Specific time point .Rdata
	source_gist("https://gist.github.com/FloWuenne/dfd1e00c198b2ae1adeb16720a844441",, filename = "Create_clustering_module_Rds.R")

	#### All datasets

FloWuenne / Clustering_module_shiny_ALL_Rds.R

Last active April 20, 2018 19:17

Create clustering module for shiny server for all timepoints

	library(dplyr)
	library(tidyr)

	## Define S4 object with required components
	Clustering_info <- setClass("clustering_module",slots=c(tsne="data.frame",
	metadata="data.frame",
	norm_exprs="data.frame",
	marker_list="data.frame"))

FloWuenne / gist:4a4c1532011186e1c5aae3150556b5c6

Created April 18, 2018 15:03

Make a nice ClusterTree from the initial ClusterTree plot from Seurat

	## Build the ClusterTree from Seurat object
	expression_seurat <- BuildClusterTree(expression_seurat,
	do.plot=TRUE)

	## Plot custom phylogenetic tree that is more easily interpretable
	png(filename="./with_Batch_Cycle_Correction/ClusterTree.png", width=1800, height=1700, bg = "white", res = 150)
	plot(ladderize([email protected][[1]]), adj=0,
	label.offset=50,
	edge.width=1)
	nodelabels(frame="circle",bg="grey")

FloWuenne / gist:b2c31b18f60fbe1ffa20ca72e3006f5b

Created April 20, 2018 17:51

Takes as input an Rds file for clustering module (.Rmd code)

	---
	title: "Transform_to_feather"
	author: "Florian Wuennemann"
	date: "April 20, 2018"
	output: html_document
	---

	```{r setup, include=FALSE}
	knitr::opts_chunk$set(echo = TRUE)
	```

FloWuenne / extract_shiny_data_Full_dataset.R

Last active April 26, 2018 14:31

Create an Rds file for the full heart maturation dataset

	library(dplyr)
	library(tidyr)

	## Define S4 object with required components
	Clustering_info <- setClass("clustering_module",slots=c(tsne="data.frame",
	metadata="data.frame",
	norm_exprs="data.frame",
	marker_list="data.frame"))

FloWuenne / cluster_module_shiny_ALL_renamed.R

Last active April 26, 2018 14:12

Create shiny cluster modules for renamed datasets

	library(dplyr)
	library(tidyr)

	## Define S4 object with required components
	Clustering_info <- setClass("clustering_module",slots=c(tsne="data.frame",
	metadata="data.frame",
	norm_exprs="data.frame",
	marker_list="data.frame"))

FloWuenne / extract_shiny_clustering_renamed_full.R

Last active May 2, 2018 20:28

Extract shiny clustering data for full dataset

	library(dplyr)
	library(tidyr)

	## Define S4 object with required components
	Clustering_info <- setClass("clustering_module",slots=c(tsne="data.frame",
	metadata="data.frame",
	norm_exprs="data.frame",
	marker_list="data.frame"))

FloWuenne / gist:aec5aa1f6fcf3bf1721b4794247098ec

Created May 1, 2018 15:04

Save raw UMI counts and metadata for each object

	## Create vector with timepoints

	time_points <- c("E14.5","E16.5","E18.5","P1","P4","P7")

	for(time in time_points){
	rds_object <- readRDS(paste("/home/wueflo00/project/Dropseq/Andelfingerlab_Dropseq/Heart_Maturation/Analysis/Objects/",time,".expression_seurat.renamed_clusters.Rds",sep=""))

	raw_umi <- as.matrix([email protected])
	metadata <- [email protected]
	raw_umi <- raw_umi[,rownames(metadata)]

FloWuenne / gist:098afe3bff11ed588cc6496bcf4312ac

Created June 14, 2018 20:15

Check correlation between number of genic reads and genes and transcripts detected per cell

	## Read in summary file from Drop-seq pipe
	infile =

	DGE_info <- read.table(infile,
	sep="\t",
	header=T)

	## Correlation Number of genic reads vs num of genes detected per cell
	ggplot(DGE_info,aes(NUM_GENIC_READS,NUM_GENES,col=NUM_TRANSCRIPTS)) +
	geom_point()

FloWuenne / gist:ff003a56a50d34acedaaa6512b6cb960

Last active July 20, 2018 15:35

Analyze and fix .gtf file for Drop-seq pipeline to remove warnings and skipping for genes with issues


	```{r setup, include=FALSE}
	knitr::opts_chunk$set(echo = TRUE)
	```

	# Load libraries

	```{r}
	library(refGenome)
	library(dplyr)