💻

Florian Wuennemann FloWuenne

💻

Bioinformatics engineer at Seqera

FloWuenne / gist:3b28db88e6d0a86b49d13fb0da10ec22

Created July 10, 2023 17:19

Perform sliding window analysis on count data in a 2d array

	## This code was entirely generated using OpenAIs Code Interpreter and prompting for the type of analysis that was desired

	import pandas as pd

	# Load the data
	data = pd.read_csv('/mnt/data/example_rs_fish.csv')

	# Calculate various quantiles
	quantiles = data['intensity'].quantile([0.10, 0.25, 0.50, 0.75])
	quantiles

FloWuenne / build_kbref_Gencode_release26_GRCm39

Last active February 25, 2021 23:34

Build kb-python reference for GENCODE release M26 GRCm39

	## This is only on compute canada clusters where modules are available!
	module load python/3.7.4

	## Create virtual environment using python
	python3 -m venv ./kb_python_env

	## activate environment
	source ./kb_python_env/bin/activate

	## Install kb-python inside environment

FloWuenne / build_custom_kb_ref.sh

Last active February 25, 2021 23:31

Building a custom reference for kb-python in a virtual environment

	## This is only on compute canada clusters where modules are available!
	module load python/3.7.4

	## Create virtual environment using python
	python3 -m venv ./kb_python_env

	## activate environment
	source ./kb_python_env/bin/activate

	## Install kb-python inside environment

FloWuenne / gist:83b8fea1b7fd970075a921ea5a91ef09

Created June 30, 2020 20:54

Make a Featureplot style plot using schex from a Seurat object

	feature_schex <- function(seurat_object,
	gene_ids,
	label = FALSE,
	nbins = 150,
	reduction = "UMAP",
	assay = "SCT",
	slot = "data",
	action = "mean"){

	require(Seurat)

FloWuenne / gist:04cf19f8a3e666f0633aed1d7b147887

Created February 13, 2020 17:49

Get list of installed R packages

	ip <- as.data.frame(installed.packages()[,c(1,3:4)])
	rownames(ip) <- NULL
	ip <- ip[is.na(ip$Priority),1:2,drop=FALSE]
	print(ip, row.names=FALSE)

FloWuenne / gist:5d457456e239e75c3eb61ea210dea6b8

Last active August 22, 2019 20:27

Setup Gist for new Rshiny server VMs.

	## Includes most packages commonly used in my workflows
	install.packages("roxygen2")
	install.packages("Rcpp")
	install.packages("promises")
	install.packages("httpuv")
	install.packages("htmltools")
	install.packages("xml2")
	install.packages("digest")
	install.packages("devtools")
	library(devtools)

FloWuenne / gist:fca174d38ffd81c513fbd979e34f7062

Created November 9, 2018 16:30

Quick testing of the Jensen-Shannon implementation in the philentropy package

	## Testing of JSD function in philentrop package
	## Documentation at:
	## https://www.rdocumentation.org/packages/philentropy/versions/0.2.0/topics/JSD

	## Load library
	library(philentropy)

	## We will test the Jensen-Shannon divergence with the aplpication of scoring gene regulatory networks as described in this \
	## paper: https://www.cell.com/cell-reports/fulltext/S2211-1247(18)31634-6?dgcid=raven_jbs_etoc_email#secsectitle0070

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)

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: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)]