Spatial

Spatial transcriptome cellular niche analysis using 10x xenium data go to https://www.10xgenomics.com/resources/datasets There is a lung cancer and a breast cancer dataset. Let’s work on the lung cancer one. https://www.10xgenomics.com/resources/datasets/xenium-human-lung-preview-data-1-standard 37G zipped file! wget https://s3-us-west-2.amazonaws.com/10x.files/samples/xenium/1.3.0/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip unzip Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip sudo tar xvzf cell_feature_matrix.tar.gz cell_feature_matrix/ cell_feature_matrix/barcodes.tsv.gz cell_feature_matrix/features.tsv.gz cell_feature_matrix/matrix.mtx.gz read in the data with Seurat We really only care about the cell by gene count matrix which is inside the cell_feature_matrix folder, and the cell location x,y coordinates: cells.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter In a previous blog post, I showed you how to make a Seurat spatial object from Vizgen spatial transcriptome data. In this post, I am going to show you how to identify clusters of neighborhood or cellular niches where specific cell types tend to co-localize. read in the data and pre-process library(Seurat) library(here) library(ggplot2) library(dplyr) # the LoadVizgen function requires the raw segmentation files which is too big.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter Single-cell spatial transcriptome data is a new and advanced technology that combines the study of individual cells’ genes and their location in a tissue to understand the complex cellular and molecular differences within it. This allows scientists to investigate how genes are expressed and how cells interact with each other with much greater detail than before.