dfjenkins3

library(singleCellTK)

createSCTKE_from_Rsubread_and_multiqc <- function(matdir, r1multi, r2multi){
  r1annot <- read.table(r1multi, sep="\t", row.names = 1, header=TRUE)
  colnames(r1annot) <- paste0(colnames(r1annot), "_R1")
  rownames(r1annot) <- gsub("_R1$", "", rownames(r1annot))
  r2annot <- read.table(r2multi, sep="\t", row.names = 1, header=TRUE)
  colnames(r2annot) <- paste0(colnames(r2annot), "_R2")
  rownames(r2annot) <- gsub("_R2$", "", rownames(r2annot))
  if(any(rownames(r1annot)!=rownames(r2annot))){

dfjenkins3

#' Convert Cell Ranger outs Directory to a SingleCelltkExperiment
#' 
#' This function creates a SingleCelltkExperiment object from a Cell Ranger
#' output directory. The filtered count matrix, pca, tsne, and clustering
#' results are stored in the object. This function requires the cellrangerRkit
#' package which is not on CRAN or Bioconductor. Install it using the directions
#' available here:
#' 
#' https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/rkit
#'

dfjenkins3

library(limma)
library(edgeR)

# function to return the toptable from limma
# indata: raw count matrix for all kras comparisons
# subsetcols: column indices for columns that you want to subset
# firstannot: the name of the first annotation
# secondannot: the name of the second annotation
get_toptable <- function(indata, subsetcols, firstannot, secondannot){
  subset_gfp_wt <- indata[,subsetcols]

dfjenkins3

#credit to @myajima

library(cellrangerRkit) #install using https://s3-us-west-2.amazonaws.com/10x.files/supp/cell-exp/rkit-install-1.1.0.R

pipestance_path <-getwd()
download_sample(sample_name="pbmc3k",
                sample_dir=pipestance_path,
                host="https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/")

gbm <- load_cellranger_matrix(pipestance_path)

dfjenkins3

create_complexheatmap <- function(indata){
  set.seed(123)
  
  sample.annotation <- data.frame(SampleType=sample(c("groupA","groupB"),
                                                    size = ncol(indata),
                                                    replace = TRUE))
  
  topha <- HeatmapAnnotation(df = sample.annotation,
                             col = list(SampleType = c("groupA" =  "coral3",
                                                      "groupB" = "aquamarine4")),

dfjenkins3

library(shiny)

hist_widget <- function() { 
  shinyApp(
    ui = fluidPage(
      sidebarLayout(
        sidebarPanel(sliderInput("n", "Bins", 5, 100, 20)),
        mainPanel(plotOutput("hist"))
      )
    ), 

dfjenkins3

Keybase proof

I hereby claim:

• I am dfjenkins3 on github.
• I am dfjenkins3 (https://keybase.io/dfjenkins3) on keybase.
• I have a public key whose fingerprint is 596F 88F4 BA92 A8AE BB47 7D84 8916 F2F3 B5CA 1207

To claim this, I am signing this object:

dfjenkins3

#run PCA
pca.results <- prcomp(t(expression))

#vector of colors
cols <- as.character(indata$Annotation)
cols[cols=="K"] <- 'red'
cols[cols=="LG"] <- 'lightgreen'
cols[cols=="G"] <- 'darkgreen'

#plot it

dfjenkins3

Comparing GenTox with Bacterial.Mutagenesis

library(Biobase)

setwd(file.path("C:","Users","djenk","OneDrive","grad_school","05-2015_fall",
                "BS830_microarray","20151218-Final_Project"))

#TG-GATEs Data (Discovery Set)

dfjenkins3

# Comparing GenTox with Bacterial.Mutagenesis

```{r}
library(Biobase)

setwd(file.path("C:","Users","djenk","OneDrive","grad_school","05-2015_fall",
                "BS830_microarray","20151218-Final_Project"))

#TG-GATEs Data (Discovery Set)
load("Rat.Liver.in_vivo.repeat.annotated.RData")