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...
Bioinformatic data analysis (single cell transcriptomics): J.F.Q., P.C., E.M.B., T.D.O. Bioinformatic data analysis (spatial transcriptomics): J.F.Q. Flow cytometry: J.F.Q., M.C.S. Imaging: J.F.Q., P.C., R.H., C.B.A., C.L. The single-cell atlas was created by ...
Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of t
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
Celltype deconvolution data was added to Seurat object metadata for downstream analysis. The ‘numbat’ R package (Version 1.1.0)82 was used to conduct haplotype-aware CNV inference on all 12 spatial transcriptomics objects from raw ST BAM files. CNV inference was conducted in accordance with ...
Spatial Transcriptomics count matrix Annotated signature gene sets (seeexample) (Optional): paired H&E image Deconvolving cell types & discovering novel, unannotated cell states Integrating with histology images and multi-sample integration Downstream analysis: spatial hub identification, cell-type colocal...
文章在Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope(NC,IF 16.6) 看一眼教程 import numpy as np import pandas as pd import pathlib importmatplotlib.pyplot as plt import matplotlib as mpl ...
Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to...
CoSTA: Unsupervised Convolutional Neural Network Learning for Spatial Transcriptomics Analysis CoSTA is an analysis approach for understanding spatial gene relationship of spatial transcriptomics. Usage To reproduce results in Manuscript, please follow instruction in notebook tutorial under the foder of "CoSTA...