In ccb-hms/SpatialData: Collection of spatially resolved omics datasets

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    crop = NULL ## Related to https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html
)

Setup

library(cytomapper)
library(vroom)
library(HDF5Array)

Start with downloading from and unzipping the dataset from: https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.jm63xsjb2

data.dir <- "data_release_baysor_merfish_gut"
raw.dir <- file.path(data.dir, "raw_data")
proc.dir <- file.path(data.dir, "data_analysis")
baysor.dir <- file.path(proc.dir, "baysor") 
cellpose.dir <- file.path(proc.dir, "cellpose") 
baysor.cellpose.dir <- file.path(proc.dir, "baysor_membrane_prior") 

Data

Def. ileum: the final and longest segment of the small intestine.

Raw data

What's there:

list.files(raw.dir)

mRNA molecule data: 820k observations for 241 genes

mol.file <- file.path(raw.dir, "molecules.csv")
mol.dat <- vroom::vroom(mol.file)
mol.dat <- data.frame(mol.dat)
dim(mol.dat)
head(mol.dat)
length(unique(mol.dat$gene))

Image data:

1. DAPI stain signal:

dapi.file <- file.path(raw.dir, "dapi_stack.tif")
dapi.img <- EBImage::readImage(dapi.file)
dapi.img

1. Membrane Na+/K+ - ATPase immunofluorescence signal:

mem.file <- file.path(raw.dir, "membrane_stack.tif")
mem.img <- EBImage::readImage(mem.file)
mem.img

Store in a CytoImageList object:

cil <- cytomapper::CytoImageList(dapi = dapi.img, membrane = mem.img)
cil
channelNames

Visualization

Plot pixels with images load in memory, takes relatively long:

system.time(
    cytomapper::plotPixels(cil)
)

This plots the first z-layer/channel, but we can select a specific z-layer/channel of the 9 different channels in the z-stack tiffs:

cytomapper::channelNames(cil)
cytomapper::plotPixels(cil, colour_by = "2")
cytomapper::plotPixels(cil, colour_by = "3")

On disk storage of images

cur_dir <- HDF5Array::getHDF5DumpDir()

Write to HDF5, takes very long:

system.time(
    cli.hdf5 <- cytomapper::loadImages(raw.dir, 
                                       pattern = ".tif$",
                                       on_disk = TRUE,
                                       h5FilesPath = cur_dir)
)
cli.hdf5

Plot pixels using hdf5 backend:

system.time(
    cytomapper::plotPixels(cli.hdf5)
)

ccb-hms/SpatialData documentation built on May 6, 2022, 4:53 a.m.