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ark-analysis

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Toolbox for analyzing multiplexed imaging data.

Full documentation for the project can be found here.

Table of Contents

• Pipeline Flowchart
• Getting Started
  • Overview
    • 1. Segmentation
    • 2. Pixel clustering with Pixie
    • 3. Cell clustering with Pixie
    • 4. Post clustering tasks
    • 5. Spatial analysis
  • Installation Steps.
    • Download the Repo
    • Running on Windows
    • Using the Repository
    • Updating the repo
• External Tools
  • Mantis Viewer
    • Mantis directory structure
    • Loading image-specific files
    • Loading project-wide files
    • View cell features
  • External Hard Drives and Google File Stream
• Updating the Repository
• Example Dataset
• Questions?
• Want to contribute?
• How to Cite

Pipeline Flowchart

Getting Started

Overview

This repo contains tools for analyzing multiplexed imaging data. The assumption is that you've already performed any necessary image processing on your data (such as denoising, background subtraction, autofluorescence correction, etc), and that it is ready to be analyzed. For MIBI data, we recommend using the toffy processing pipeline.

We have recorded workshop talks which complement the repository. MIBI Workshop Playlist.

1. Segmentation

The segmentation notebook will walk you through the process of using Mesmer to segment your image data. This includes selecting the appropriate channel(s) for segmentation, running your data through the network, and then extracting single-cell statistics from the resulting segmentation mask. Workshop Talk - Session V - Part 1: Segmentation

• Note: It is assumed that the cell table uses the default column names as in ark/settings.py. Refer to the docs to get descriptions of the cell table columns, and methods to adjust them if necessary.
• If you plan to segment out non-traditional cellular structures such as protein aggregates or cytoplasmic projections often found in brain cells (e.g. microglia, astrocytes, and neuropil), try out the companion notebook ezSegmenter either as a stand-alone or in combination with the above standard cell segmentation process.

2. Pixel clustering with Pixie

The first step in the Pixie pipeline is to run the pixel clustering notebook. The notebook walks you through the process of generating pixel clusters for your data, and lets you specify what markers to use for the clustering, train a model, use it to classify your entire dataset, and generate pixel cluster overlays. The notebook includes a GUI for manual cluster adjustment and annotation. Workshop Talk - Session IV - Pixel Level Analysis

3. Cell clustering with Pixie

The second step in the Pixie pipeline is to run the cell clustering notebook. This notebook will use the pixel clusters generated in the first notebook to cluster the cells in your dataset. The notebook walks you through generating cell clusters for your data and generates cell cluster overlays. The notebook includes a GUI for manual cluster adjustment and annotation. Workshop Talk - Session V - Cell-level Analysis - Part 2: Cell Clustering

4. Post Clustering Tasks

After the Pixie Pipeline, the user can inspect and fine tune their results with the post clustering notebook. This notebook will go over cleaning up artifacts left from clustering, and working with functional markers.

5. Spatial Analysis

Workshop Talk - Session VI - Spatial Analysis - Part 1: Choosing the Right Analysis Tool.

1. Pairwise Enrichment Analysis

   The pairwise enrichment notebook allows the user to investigate the interaction between the phenotypes present in their data. In addition users can cluster based on phenotypes around a particular feature such as artery or gland. Workshop Talk - Session VI - Spatial Analysis - Part 2: Pairwise Spatial Enrichment.

2. K-means Neighborhood Analysis

   The neighborhood analysis notebook sheds light on neighborhoods made of micro-environments which consist of a collection of cell phenotypes. Workshop Talk - Session VI - Spatial Analysis - Part 3: K-means Neighborhood Analysis.

3. Spatial LDA

   The preprocessing and training / inference draws from language analysis, specifically topic modelling. Spatial LDA overlays a probability distribution on cells belonging to a any particular micro-environment. Workshop Talk - Session VI - Spatial Analysis - Part 4: Spatial LDA.

Installation Steps

Pip Installation

You can install the latest version of ark with:

pip install ark-analysis

However, the repository will still need to be cloned if you wish to use the Jupyter Notebooks.

Download the Repo

You can find all the versions available in the Releases Section. Open terminal and navigate to where you want the code stored.

If you would like to use the latest version of ark simply clone the project and create the Conda environment.

git clone -b v0.7.2 https://github.com/angelolab/ark-analysis.git
cd ark-analysis
conda env create -f environment.yml

Running on Windows

Our repo runs best on Linux-based systems (including MacOS). If you need to run on Windows, please consult our Windows guide for additional instructions.

Using the Repository

First, activate the conda environment inside the root of the ark-analysis folder:

conda activate ark_env

Once activated, notebooks can be used via this command for Windows:

start_jupyter.sh

or this command for macOS:

./start_jupyter.sh

This will automatically open your browser with a link to our Jupyter notebooks.

Be sure to keep this terminal open. Do not exit the terminal or enter control-c until you are finished with the notebooks.

You can shut down the notebooks by entering control-c in the terminal window.

REMEMBER TO DUPLICATE AND RENAME NOTEBOOKS

If you didn't change the name of the notebooks within the templates folder, they will be overwritten when you decide to update the repo. Read about updating Ark here

Updating the repo

The ark-analysis repo is constantly being updated. In order to get those changes to your version, you'll need to tell git to update with the following command:

git pull

After performing the above command, you will sometimes need to update your environment:

conda remove --name ark_env --all
conda env create -f environment.yml

To update the notebooks, run this command for Windows:

start_jupyter.sh -u

or this command for macOS:

./start_jupyter.sh -u

External Tools

Mantis Viewer

Mantis is a multiplexed image viewer developed by the Parker Institute. It has built in functionality for easily viewing multichannel images, creating overlays, and concurrently displaying image features alongisde raw channels. We have found it to be extremely useful for analying the output of our analysis pipeline. There are detailed instructions on their download page for how to install and use the tool. Below are some details specifically related to how we use it in ark. Workshop Talk - Session V - Cell-level Analysis - Part 3: Assessing Accuracy with Mantis Viewer.

Mantis directory structure

Mantis expects image data to have a specific organization in order to display it. It is quite similar to how MIBI data is already stored, with a unique folder for each FOV and all channels as individual tifs within that folder. Any notebooks that suggest using Mantis Viewer to inspect results will automatically format the data in the format shown below.

mantis
│ 
├── fov0
│   ├── cell_segmentation.tiff
│   ├── chan0.tiff
│   ├── chan1.tiff
│   ├── chan2.tiff
│   ├── ...
│   ├── population_mask.csv
│   └── population_mask.tiff
├── fov1
│   ├── cell_segmentation.tiff
│   ├── chan0.tiff
│   ├── chan1.tiff
│   ├── chan2.tiff
│   ├── ...
│   ├── population_mask.csv
│   └── population_mask.tiff
└── marker_counts.csv

Loading image-specific files

In addition to the images, there are additional files in the directory structure which can be read into mantis.

cell_segmentation: This file contains the predicted segmentation for each cell in the image, and allows mantis to identify individual cells.

population_pixel_mask: This file maps the individual pixel clusters generated by Pixie in the pixel clustering notebook to the image data.

population_cell_mask: Same as above, but for cell clusters instead of pixel clusters

These files should be specified when first initializing a project in mantis as indicated below:

Loading project-wide files

When inspecting the output of the clustering notebooks, it is often useful to add project-wide .csv files, such as marker_counts.csv. These files contain information, such as the average expression of a given marker, across all the cells in the project. Project-wide files can either be loaded at project initialization, as shown below:

Or they can be loaded into an existing project via Import -> Segment Features -> For project from CSV

View cell features

Once you have loaded the project-wide files into Mantis, you'll need to decide which of the features you want to view. Click on Show Plot Plane at the bottom right, then select the marker you want to assess. This will then allow you to view the cell expression of that marker when you mouse over the cell in Mantis.

Updating the Repository

This project is still under development, and we are making frequent changes and improvements. If you want to update the version on your computer to have the latest changes, perform the following steps. Otherwise, we recommend waiting for new releases.

First, get the latest version of the repository.

git pull

Then, run the command below to update the Jupyter notebooks to the latest version

If you have made changes to these notebooks that you would like to keep (specific file paths, settings, custom routines, etc), rename them before updating!

For example, rename your existing copy of 1_Segment_Image_Data.ipynb to 1_Segment_Image_Data_old.ipynb. Then, after running the update command, a new version of 1_Segment_Image_Data.ipynb will be created with the newest code, and your old copy will exist with the new name that you gave it.

After updating, you can copy over any important paths or modifications from the old notebooks into the new notebook.

Example Dataset

If you would like to test out the pipeline, then we have incorporated an example dataset within the notebooks. Currently the dataset contains 11 FOVs with 22 channels (CD3, CD4, CD8, CD14, CD20, CD31, CD45, CD68, CD163, CK17, Collagen1, ECAD, Fibronectin, GLUT1, H3K9ac, H3K27me3, HLADR, IDO, Ki67, PD1, SMA, Vim), and intermediate data necessary for each notebook in the pipeline.

The dataset is split into several smaller components, with each Jupyter Notebook using a combination of those components. We utilize Hugging Face for storing the dataset and using their API's for creating these configurations. You can view the dataset's repository as well.

Dataset Compartments

Image Data: This compartment stores the tiff files for each channel, for every FOV.

image_data/
├── fov0/
│  ├── CD3.tiff
│  ├── ...
│  └── Vim.tiff
├── fov1/
│  ├── CD3.tiff
│  ├── ...
│  └── Vim.tiff
├── .../

Cell Table: This compartment stores the various cell tables which get generated by Notebook 1.

segmentation/cell_table/
├── cell_table_arcsinh_transformed.csv
├── cell_table_size_normalized.csv
└── cell_table_size_normalized_cell_labels.csv

Deepcell Output: This compartment stores the segmentation images after running deepcell.

segmentation/deepcell_output/
├── fov0_whole_cell.tiff
├── fov0_nuclear.tiff
├── ...
├── fov10_whole_cell.tiff
└── fov10_nuclear.tiff

Example Pixel Output: This compartment stores feather files, csvs and pixel masks generated by pixel clustering.

segmentation/example_pixel_output_dir/
├── cell_clustering_params.json
├── channel_norm.feather
├── channel_norm_post_rowsum.feather
├── pixel_thresh.feather
├── pixel_channel_avg_meta_cluster.csv
├── pixel_channel_avg_som_cluster.csv
├── pixel_masks/
│  ├── fov0_pixel_mask.tiff
│  └── fov1_pixel_mask.tiff
├── pixel_mat_data/
│  ├── fov0.feather
│  ├── ...
│  └── fov10.feather
├── pixel_mat_subset/
│  ├── fov0.feather
│  ├── ...
│  └── fov10.feather
├── pixel_meta_cluster_mapping.csv
└── pixel_som_weights.feather

Example Cell Output: This compartment stores feather files, csvs and cell masks generated by cell clustering.

segmentation/example_cell_output_dir/
├── cell_masks/
│  ├── fov0_cell_mask.tiff
│  └── fov1_cell_mask.tiff
├── cell_meta_cluster_channel_avg.csv
├── cell_meta_cluster_count_avg.csv
├── cell_meta_cluster_mapping.csv
├── cell_som_cluster_channel_avg.csv
├── cell_som_cluster_count_avg.csv
├── cell_som_weights.feather
├── cluster_counts.feather
├── cluster_counts_size_norm.feather
└── weighted_cell_channel.csv

Dataset Configurations

• 1 - Segment Image Data:
  • Image Data
• 2 - Pixie Cluster Pixels:
  • Image Data
  • Cell Table
  • Deepcell Output
• 3 - Pixie Cluster Cells:
  • Image Data
  • Cell Table
  • Deepcell Output
  • Example Pixel Output
• 4 - Post Clustering:
  • Image Data
  • Cell Table
  • Deepcell Output
  • Example Cell Output

Questions?

If you have a general question or are having trouble with part of the repo, you can refer to our FAQ or head to the discussions tab to get help. If you've found a bug with the codebase, first make sure there's not already an open issue, and if not, you can then open an issue describing the bug.

Want to contribute?

If you would like to help make ark better, please take a look at our contributing guidelines.

How to Cite

Please directly cite the ark repo (https://github.com/angelolab/ark-analysis) if it was a part of your analysis. In addition, please cite the relevant paper(s) below where applicable to your study.

1. Greenwald, Miller et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning [2021]
2. Liu et al. Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering [2023]