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.
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 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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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.
An introduction to SC-MEB | Learn how to use DR-SC | Learn how to use SpatailAnno |
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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.
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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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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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.
An overview of the major visualization functionality within SRTpipeline. | XX examples | XX examples |
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XX examples | Convert data between formats for different analysis tools. | Speed up compute-intensive functions with parallelization. |
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