daskelly

library(Seurat)
library(harmony)

# x is a Seurat object not yet processed but we have chosen num_pc
x <- FindNeighbors(x, reduction = 'pca', dims = 1:num_pc, verbose = FALSE) %>%
  FindClusters(verbose = FALSE, resolution = 0.2) %>%
  RunUMAP(reduction = 'pca', dims = 1:num_pc, verbose = FALSE)
x$noharmony_clusters <- x$seurat_clusters
x@misc$noharmony_umap <- x@reductions$umap

daskelly

library(DT)
#
# make a table of results
# ...
#
datatable(signif_results, filter = 'top', rownames = FALSE,
    options=list(pageLength = 10, autoWidth = TRUE))

daskelly

library(ensembldb)
library(EnsDb.Mmusculus.v79)
ensembl_gene_ids <- c('ENSMUSG00000000120', 'ENSMUSG00000000159', 'ENSMUSG00000000563', 'ENSMUSG00000000600')  # some random IDs
ensembldb::select(EnsDb.Mmusculus.v79, keys=ensembl_gene_ids, keytype = "GENEID", columns = c("SYMBOL","GENEID"))

daskelly

# List all open tabs in front Chrome window
# From https://superuser.com/questions/276321/how-to-export-opened-tabs-in-chrome
alias listtabs="osascript -e{'set o to\"\"','tell app\"google chrome\"','repeat with t in tabs of window 1','set o to o&url of t&\" \"&title of\
 t&linefeed',end,end}|sed \$d"

daskelly

wilcox_unequal_var <- function(x, y, ...) {
  # The Wilcoxon rank sum test (Mann Whitney U test) does not perform
  # that well on data where the variances are unequal. Some
  # investigators have recommended as an alternative performing the
  # unequal variance t-test on ranked values. See
  # https://academic.oup.com/beheco/article/17/4/688/215960
  # https://psycnet.apa.org/doiLanding?doi=10.1037%2Fh0078850
  rr <- rank(c(x, y))
  nx <- length(x)
  t.test(rr[1:nx], rr[(nx+1):length(rr)], var.equal=FALSE, ...)

daskelly

read10x <- function(dir) {
    names <- c("barcodes.tsv", "features.tsv", "matrix.mtx")
    for (i in 1:length(names)) {
        R.utils::gunzip(paste0(dir, "/", names[i], ".gz"))
    }
    file.copy(paste0(dir, "/features.tsv"), paste0(dir, "/genes.tsv"))
    mat <- Seurat::Read10X(dir)
    file.remove(paste0(dir, "/genes.tsv"))
    for (i in 1:length(names)) {
        R.utils::gzip(paste0(dir, "/", names[i]))

daskelly

library(tidyverse)
library(Seurat)
library(doParallel)
# Find specific markers for each cell type

n.cores <- 10
registerDoParallel(cores=n.cores)

celltypes <-   # list of cell types (unique ident labels of Seurat object)
obj <-         # Seurat object

daskelly

library(tidyverse)
library(biomaRt)

genes <- # FILL ME IN ... Should be a vector of gene names
markers <- # FILL ME IN ... Should be a data.frame with at least one column called mgi_symbol

ensembl <- useMart("ensembl", dataset="mmusculus_gene_ensembl")  # Mouse
# GO:0009986 is cell surface
go_cellsurf <- "GO:0009986"
gene.data <- getBM(attributes=c("mgi_symbol", "ensembl_gene_id", "go_id"),

daskelly

# Intalling leafcutter on a CentOS 6.5 machine
# This machine has glibc 2.12 and leafcutter needs glibc 2.14
module purge
module load gcc/4.9.2

# Install glibc 2.14
mkdir $HOME/apps/glibc_install
cd $HOME/apps/glibc_install 
wget http://ftp.gnu.org/gnu/glibc/glibc-2.14.tar.gz
tar zxvf glibc-2.14.tar.gz

daskelly

#!/bin/sh

# works on a machine running CentOS 7.7
# no error checking and this requires manual intervention. This script just gives 
# an overview of the process.
#
# https://github.com/rust-lang/cargo/issues/713 was helpful...

mkdir -p apps/mosh
cd apps/mosh