In celsomilne/scSpatial: Single-Cell Spatial Data

library(BiocStyle)
knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE)

Downloading the Data

First, we download the data from Zonendo.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask=FALSE)
data.url <- "http://lincs.hms.harvard.edu/data/HMS_Dataset_20267.zip"

data.path <- bfcrpath(bfc, data.url)

Now read it into our session.

library(tidyverse)

temp <- file.path("temp/")
dir.create(temp, recursive = TRUE)
unzip(data.path, exdir=temp)

csv_file <- file.path(temp, "HMS_Dataset_20267", "HMS_Dataset_20267_data_file.csv")
data <- read_csv(csv_file)

Now, we want to clean the data up a bit.

data <- data %>% 
  dplyr::rename(x="X", y="Y", 
                shape_area="Area", 
                shape_perimiter="Perim", 
                shape_circularity="Circ", 
                cellID="Cell_No",
                imageID="Image_link") %>%
  select(-Well, -Treatment, -Dose, -`Dose unit`, -`Duration (hr)`, -Row, 
         -Column, -Field)

intensity_columns <- colnames(data)[grep("[A-Za-z]+[0-9]*_[0-9]+|IntDen_DAPI", colnames(data))]

# Rename the intensity columns (for easier processing in Segmented cells)
data <- data %>% 
  rename_at(intensity_columns, function(x){paste0("intensity_", x)})

Save to ExperimentHub Storage

Prepare all the files for storage on the ExperimentHub.

path <- file.path("scSpatial", "Hafner-BreastCancer", "1.0")
dir.create(path, showWarnings=FALSE, recursive=TRUE)

saveRDS(data, file=file.path(path, "spatialCellData.rds"))

And get rid of the temporary folder and the cache.

unlink(temp, recursive = TRUE, force = TRUE)
unlink("raw_data", recursive = TRUE, force = TRUE)

celsomilne/scSpatial documentation built on Aug. 11, 2020, 9:51 a.m.