Getting Started with SRTpipeline

We provide a series of vignettes, tutorials, and analysis walkthroughs to help users get started with SRTpipeline. You can also check out our Reference page which contains a full list of functions available to users.

Introductory Vignettes

For new users of SRTpipeline, we suggest starting with a guided walk through of a spatial transcriptomics dataset (SampleID: 151672) for human dorsolateral prefrontal cortex (DLPFC) from Github. This tutorial implements the major components of a standard unsupervised clustering workflow including QC and data filtration, calculation of high-variance genes or spatially variable genes, dimensional reduction, Markov random field-based spatial clustering, and the identification of cluster markers.

We provide additional introductory vignettes for users who are interested in analyzing multiple datasets.

A basic overview for single data analyses — taking DLPFC 151672 as an example	A basic overview for multiple data analyses — taking eight Liver ST datasets an example	Embedding and spatial clustering for single sample — DLPFC 151672
A basic overview of SRTpipeline that includes an introduction to common analytical workflows.	Learn to integrate spatially-resolved transcriptomic data with examples from 10x Visium.	An introduction to working with single SRT sample in SRTpipeline.
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Single SRT Data Analyses

Recently, we have developed computational methods ( SC-MEB, DR-SC and SpatialAnno) for analysis of slngle SRT dataset generated by different platforms. As an example, we provide a guided walk through for analyzing a mouse embryo seqFISH dataset and a mouse olfactory bulb ST dataset. The workflow consists of QC and data filtration, calculation of high-variance genes or spatially variable genes, dimension reduction, spatial clustering/annotation, the identification of cluster markers and trajectory inference.

SRT data clustering — Embryo seqFISH dataset	SRT data embedding and clustering — Embryo seqFISH dataset	SRT data embedding and annotation
An introduction to SC-MEB	Learn how to use DR-SC	Learn how to use SpatailAnno
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Mulitple SRT Data Integration

Recently, we have developed computational methods ( PRECAST and iSC.MEB) for integrated analysis of SRT datasets generated across different conditions, experimental batches, individuals or datasets with only partially shared cell/domain clusters. As an example, we provide a guided walk through for integrating and comparing hepatocellular carcinoma (HCC) Visium data generated under different conditions. There are four slides of in-house HCC data generated using the 10x Visium platform, with two slides from tumors (HCC1 and HCC2) and two from tumor-adjacent tissues (HCC3 and HCC4) from an HCC patient. We provide additional vignettes demonstrating how to leverage iSC.MEB to efficiently integrate large datasets.

Introduction to SRT integration — eight Liver ST data	SRT clustering and integration based on uncorrected PCs — three DLPFC Visium data	SRT embedding, clustering and integration based on normlized expression — four hepatocellular carcinoma Visium data
An introduction to integrating spatially resolved transcriptomics (SRT) datasets in order to identify and compare shared embeddings and cell types across experiments.	Learn how to rapidly integrate multiple SRT datasets, achieve spatial clustering and align embeddings using uncorrected PCs.	Learn how to integrate multiple SRT datasets, achieve embeddings alignment and spatial clustering using normlized gene expression.
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SRT clustering and integration based on uncorrected PCs — eight mouse liver ST data	SRT embedding, clustering and integration based on normlized expression — two human breast cancer Visium datasets	Tips for integrating large datasets
Analyze two human breast cancer data using integration method.	Annotate, visualize, and interpret an scATAC-seq experiment using scRNA-seq data from the same biological system.	Tips and examples for integrating very large scRNA-seq datasets (including >200,000 spots).
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Other

Here we provide a series of short vignettes to demonstrate a number of features that are commonly used in SRTpipeline. We’ve focused the vignettes around questions that we frequently receive from users. Click on a vignette to get started.

Visualization	XX	XX
An overview of the major visualization functionality within SRTpipeline.	XX examples	XX examples
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XX	Interoperability with Other Analysis Tools	Parallelization
XX examples	Convert data between formats for different analysis tools.	Speed up compute-intensive functions with parallelization.
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