RCTD [19]: we first convert spatial transcriptomics data to a SpatialRNA object and scRNA-seq reference to a reference object following the RCTD tutorial on GitHub:https://github.com/dmcable/spacexr. To compare with STASCAN, we performed RCTD with the hyperparameters of “doublet_mode =...
Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individua
For visualization data processing and downstream analysis, we used the Scanpy python package (version 1.4.4) [37] following a standard processing pipeline according to the tutorial on spatial transcriptomics data analysis. In short, data were normalized using a scaling factor of 1000, log-transformed...
Spatial transcriptomics in neuroscience Article Open access 02 October 2023 Introduction The adult brain of vertebrate animals has extensive capabilities due to its astonishing cell type diversity1,2 and precise arrangement of regional structures,3 especially in the cerebral cortex as it is the most ...
Spatial Transcriptomics count matrix Annotated signature gene sets (seeexample) (Optional): paired H&E image Features: Deconvolving cell types & discovering novel, unannotated cell states Integrating with histology images and multi-sample integration ...
tutorial LICENSE README.md README MIT license METI METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics METI (Morphology-Enhanced Spatial Transcriptome Analysis Integrator) is an novel analytic framework that systematically analyzes cancer cells and cells of th...
However, for this analysis, we further incorporated three normal, human dorsolateral prefrontal cortex (DLPFC) 10x Genomics Visium spatial transcriptomics datasets from spatialLIBD as a normal reference [23]. This approach was helpful to delineate ‘higher tumour content’ spots. Moreover, we ...
analysis. Spatial transcriptomic profiling Tissue optimization was performed to determine the optimal permeabilization time for OSCC tissue for the downstream gene expression protocol. Spatial transcriptomics was performed on OSCC cryosections using the Visium Spatial Gene Expression Slide & Reagent Kit, 16 ...
Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST ...