Obtain the MERFISH mouse ileum dataset from Petukhov et al., 2021
MouseIleumPetukhov2021( segmentation = c("baysor", "cellpose"), use.images = TRUE, use.polygons = TRUE )
character. Should be either
logical. Should DAPI and Membrane Na+/K+ - ATPase images
be loaded into memory and annotated to the
logical. Should polygon cell boundaries be annotated
Spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. Distinguishing the boundaries of individual cells in such data is challenging. Current segmentation methods typically approximate cells positions using nuclei stains.
Petukhov et al., 2021, describe Baysor, a segmentation method, which optimizes 2D or 3D cell boundaries considering joint likelihood of transcriptional composition and cell morphology. Baysor can also perform segmentation based on the detected transcripts alone.
Petukhov et al., 2021, compare the results of Baysor segmentation (mRNA-only) to the results of a deep learning-based segmentation method called Cellpose from Stringer et al., 2021. Cellpose applies a machine learning framework for the segmentation of cell bodies, membranes and nuclei from microscopy images.
The function allows to obtain segmented MERFISH mouse ileum data for both segmentation methods.
A note on storing images within a
use.images = TRUE reduces the 9-frame z-stack images
for DAPI stain and Membrane Na+/K+ - ATPase fluorecense to single-frame
images (taking the first frame). For working with the 9-frame z-stack
images it is recommended to load the images individually from ExperimentHub.
An object of class
Petukhov et al. (2021) Cell segmentation in imaging-based spatial transcriptomics. Nat Biotechnol, 40(3), 345-54.
Stringer et al. (2021) Cellpose: a generalist algorithm for cellular segmentation. Nat Methods, 18(1), 100-6.
spe <- MouseIleumPetukhov2021()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.