Skip to content

This repository provides an R-based pipeline for analyzing spatial RNA-seq data using Seurat, STDeconvolve, and Giotto. Designed to process and analyze spatial transcriptomics data through a sequence of well-defined steps, enabling in-depth exploration and interpretation of spatially resolved gene expression profiles.

License

Notifications You must be signed in to change notification settings

utdal/visium10x-spatial-transcriptomics-pipeline

Repository files navigation

Analysis Pipeline for Visium 10x Data [visium10x-spatial-transcriptomics-pipeline]

This repository provides an R-based pipeline for analyzing spatial RNA-seq data using Seurat, STDeconvolve, and Giotto. Designed to process and analyze spatial transcriptomics data through a sequence of well-defined steps, enabling in-depth exploration and interpretation of spatially resolved gene expression profiles.

Note: Make sure to have older version of Giotto (1.1.2).

Tools used:

Tool Functions
Seurat automated_seurat_analysis_func, automated_seurat_spatial_analysis_func
STDeconvolve decolvolve_spatial_transcriptomics_analysis
Giotto giotto_spatial_transcriptomics_analysis

Files:

File Name Description
run_analysis.R Main script to run the entire analysis pipeline.
package_installer.R Script to install necessary R packages.
seurat_analysis.R Functions for Seurat-based analysis.
cell_deconvolution_analysis.R Functions for cell deconvolution analysis.
giotto_gene_expr_analysis.R Functions for Giotto-based analysis.

Installation:

git clone https://github.com/utdal/visium10x-spatial-transcriptomics-pipeline.git
cd visium10x-spatial-transcriptomics-pipeline

How to run the pipeline:

Note: Prior to running the pipeline, one needs to install R and RStudio/VSCode for running the R-based pipeline.

  1. Open run_analysis.R in Rstudio/VSCode.
  2. Modify the directory paths, per sample.
  3. Additionally, for Giotto analysis, there is a chance that the barcodes may not overlap with the tissue, in that case; xmax_adj, xmin_adj, ymax_adj and ymin_adj need to be adjusted accordingly.
  4. Outputs generated are saved to the directory path defined in run_analysis.R file.

Output

The script will generate various output files which include:

  1. Plots (e.g., UMAP, PCA, heatmaps)
  2. CSV files with marker genes
  3. RDS files with processed data
Plots generated:

NaN

Plots preview:
Plot Plot Plot
Plot Plot Plot
Plot Plot Plot
Plot Plot Plot
Plot Plot Plot
Plot Plot Plot
Plot Plot Plot
Plot Plot Plot

Troubleshooting

  1. Missing Files: Ensure all required files are present in the specified paths.
  2. Package Issues: Install any missing R packages using package_installer.R.
  3. Path Errors: Double-check file paths and directory names in the run_analysis.R script.

Known Issues: STDeconvolve and Giotto need the input files to be in the following format:

-- sample
   -- outs
     -- filtered_feature_bc_matrix
        -- barcodes.tsv.gz
        -- features.tsv.gz
        -- matrix.mtx.gz
     -- raw_feature_bc_matrix
        -- barcodes.tsv.gz
        -- features.tsv.gz
        -- matrix.mtx.gz
     -- spatial
        -- tissue_positions_list.csv
        -- scalefactors_json.json
     -- filtered_feature_bc_matrix.h5
     -- raw_feature_bc_matrix.h5
     -- etc.

Contant: For questions or issues, please reach out Dr. Tavares Ferreira, Diana or Nikhil Nageshwar Inturi.

About

This repository provides an R-based pipeline for analyzing spatial RNA-seq data using Seurat, STDeconvolve, and Giotto. Designed to process and analyze spatial transcriptomics data through a sequence of well-defined steps, enabling in-depth exploration and interpretation of spatially resolved gene expression profiles.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages