MAGPIE directory structure

The directory from which you run the pipeline should have the following general structure:

├── Snakefile
├── magpie_shiny_app.py
├── figures  
│   ├── magpie_logo.png
├── scripts
│   ├── alter_data.py
│   ├── create_mock_spaceranger.py
│   ├── create_perbarcode_matrix.py
├── input
│   ├── (optional) exclude.txt
│   ├── (optional) selected.txt
│   ├── ... 

The exclude.txt and selected.txt files are optional and allow the user to list a number of samples to include (rather than using all sub-folders in the input directory by default) or to exclude from analysis.

The Snakefile, magpie_shiny_app.py and scripts and figures folders should all be copied from the GitHub repository.

Input folder structure

The MAGPIE pipeline automatically detects the files in your input folder and makes decisions accordingly so you must ensure your files follow the following structure:

[sample name]
├── visium                               # Spaceranger outputs
│   ├── filtered_feature_bc_matrix.h5
│   ├── spatial
│   │   ├── aligned_fiducials.jpg
│   │   ├── detected_tissue_image.jpg
│   │   ├── scalefactors_json.json
│   │   ├── tissue_hires_image.png
│   │   ├── tissue_lores_image.png
│   │   ├── tissue_positions_list.csv
├── msi                    
│   ├── MSI_intensities.csv              # Table of intensities with MSI peaks on columns and pixels on rows
│   ├── MSI_metadata.csv                 # Table of metadata about MSI pixels, including x and y coordinate columns
│   │── MSI_HE.[jpg,png,tiff]                       # (OPTIONAL) intermediate MSI image to assist with coregistration
├── landmarks_MSI2HE.csv                 # (OPTIONAL) Table of identified landmarks between MSI image and MSI H&E image (added by shiny app or identified externally)
├── landmarks_MSI2HE.csv                 # (OPTIONAL) Table of identified landmarks between MSI H&E and Visium H&E image (added by shiny app or identified externally)
└── landmarks_noHE.csv                   # (OPTIONAL) Table of identified landmarks between MSI image and Visium H&E (added by shiny app or identified externally). 
                                           Either landmarks_noHE.csv or both landmarks_MSI2HE.csv and landmarks_MSI2HE.csv are required for coregistration.

MSI data

The MSI data should be split into an intensity table and a metadata table. The intensity table should look like this, with peaks on the columns and pixels on the rows.

spot_id	mz-101.2345	mz-102.85754	mz-303.35855	mz-344.48575	mz-321.38583	mz-112.28485
pixel_0	5774.812	675.361	23.555	8444.958	777.234	20.332
pixel_1	8794.013	444.523	81.294	6775.393	899.284	10.275
pixel_2	6777.358	857.585	14.326	9468.367	747.385	24.521
	…	…	…	…	…	…

The metadata table must contains columns spot_id, x and y (and can additionally include others) and the spot_id columns must exactly match the spot_id column in the intensity table.

spot_id	x	y	Sample	Treatment	Fibrotic
pixel_0	1	1	sample_1	treatment_1	normal
pixel_1	2	1	sample_1	treatment_1	normal
pixel_2	2	2	sample_1	treatment_1	fibrotic
	…	…	…

The first stage of the snakemake pipeline will check all these criteria and save a summary table called summary.txt.