Baysor

Bayesian segmentation of imaging-based spatial transcriptomics data

• News ([0.5.1] — 2021-12-01)
• Overview
• Installation
  • Binary download
  • Install as a Julia package
  • Build CLI application from source
  • Docker
• Run
  • Dataset preview
  • Full run
    • Normal run
    • Using a prior segmentation
      • Segmenting stains
    • Segmenting cells with pronounced intracellular structure
    • Outputs
    • Choice of parameters
• Citation

News ([0.5.1] — 2021-12-01)

• CLI parameter no-ncv-estimation to disable estimation of NCVs
• We have MacOS binaries now!

See the changelog for more detalis.

Overview

Baysor is a tool for performing cell segmentation on imaging-based spatial transcriptomics data. It optimizes segmentation considering the likelihood of transcriptional composition, size and shape of the cell. The approach can take into account nuclear or cytoplasm staining, however can also perform segmentation based on the detected molecules alone. The details of the method are described in the paper, or pre-print (old version of the text). To reproduce analysis from the paper see BaysorAnalysis repo.

See the 16-min live-demo of Baysor for an overview of the workflow!

Installation

Binary download

The easiest way to install Baysor on Linux or MacOS is to download a binary from the release section (see Assets for your platform). There, you can use bin/baysor executable.

Install as a Julia package

To install Baysor as a Julia package run the following code:

using Pkg
Pkg.add([PackageSpec(name="UMAP", rev="master"), PackageSpec(url="https://github.com/kharchenkolab/Baysor.git")])
import Baysor

After that you can create Baysor executable by running

printf "#! /usr/bin/env julia\nimport Baysor: run_cli\nrun_cli()" >> baysor && chmod +x baysor

Build CLI application from source

This method requires a linux installation with git, gcc, wget and make tools installed. Then, to build the command-line tool without a julia installation, you need to clone this package and run the Makefile:

git clone https://github.com/kharchenkolab/Baysor.git
cd Baysor/bin
make

The command takes ~10 minutes to complete. It creates executable file ./Baysor/bin/baysor, which can be used for spatial data segmentation.

Docker

Alternatively, you can use Docker. It contains executable baysor to run Baysor from CLI, as well as IJulia installation to use Baysor with Jupyter. The repo also has images for older versions.

docker run -it --rm vpetukhov/baysor:master

Build by hands:

docker pull julia:latest
cd Baysor/docker
docker build .

You can find more info about dockers at Docker Cheat Sheet.

Run

NOTE: The tool is in the beta-version now, so some parameters and functionality can be changed in the future. Please, report any problems and suggestions to Issues.

Dataset preview

As full run takes some time, it can be useful to run a quick preview to get meaning from the data and to get some guesses about parameters of the full run.

baysor preview [-x X_COL -y Y_COL --gene GENE_COL -c config.toml -o OUTPUT_PATH] MOLECULES_CSV

Here:

• MOLECULES_CSV must contain information about x and y positions and gene assignment for each molecule
• Parameters X_COL, Y_COL and GENE_COL must specify the corresponding column names. Default values are "x", "y" and "gene" correspondingly
• OUTPUT_PATH determines path to the output html file with the diagnostic plots

Full run

Normal run

To run the algorithm on your data, use

baysor run [-s SCALE -x X_COL -y Y_COL -z Z_COL --gene GENE_COL] -c config.toml MOLECULES_CSV [PRIOR_SEGMENTATION]

Here:

• MOLECULES_CSV must contain information about x and y positions and gene assignment for each molecule
• PRIOR_SEGMENTATION is optional molecule segmentation obtrained from other method (see Using prior segmentation)
• Parameters X_COL, Y_COL, Z_COL and GENE_COL must specify the corresponding column names. Default values are "x", "y", "z" and "gene" correspondingly.
• SCALE is a crutial parameter and must be approximately equal to the expected cell radius in the same units as "x", "y" and "z". In general, it's around 5-10 micrometers, and the preview run can be helpful to determine it for a specific dataset (by eyes, for now).

To see full list of command-line options run

baysor run --help

For more info see examples (though probably are out-of-date).

For the description of all config parameters, see example_config.toml.

Using a prior segmentation

In some cases, you may want to use another segmentation as a prior for Baysor. The most popular case is having a segmentation based on DAPI/poly-A stainings: such information helps to understand where nucleis are positioned, but it's often quite imprecise. To take this segmentation into account you can pass it as the second positional arguments to Baysor:

baysor run [ARGS] MOLECULES_CSV [PRIOR_SEGMENTATION]

Here, PRIOR_SEGMENTATION can be a path to a binary image with segmentation mask, an image with integer cell segmentation labels or a column name in the MOLECULES_CSV with integer cell assignment per molecule. In the later case, column name must have : prefix, e.g. for column cell you should use baysor run [ARGS] molecules.csv :cell. In case the image is too big to be stored in the tiff format, Baysor supports MATLAB '.mat' format: it should contain a single field with an integer matrix for either a binary mask or segmentation labels. When loading the segmentation, Baysor filters segments that have less than min-molecules-per-segment molecules. It can be set in the toml config, and the default value is min-molecules-per-segment = min-molecules-per-cell / 4. Note: only CSV column prior is currently supported for 3D segmentation.

To specify expected quality of the prior segmentation you may use prior-segmentation-confidence parameter. The value 0.0 makes the algorithm ignore the prior, while the value 1.0 restricts the algorithm from contradicting the prior. Prior segmentation is mainly needed for the cases where gene expression signal is not enough, e.g. with very sparse protocols (such as ISS or DARTFISH). Another potential usecase is high-quality data with visible sub-cellular structure. In these situations, setting prior-segmentation-confidence > 0.7 is recommended. Otherwise, the default value 0.2 should work well.

Segmenting stains

If you have a non-segmented DAPI image, the simplest way to segment it would go through the following steps ImageJ:

1. Open the image (File -> Open)
2. Go to Image -> Type and pick "8-bit"
3. Run Process -> Filters -> Gaussian Blur, using Sigma = 1.0. The value can vary, depending on your DAPI, but 1.0 generally works fine.
4. Run Image -> Adjust -> Auto Threshold, using Method = Default. Different methods can give the best results for different cases. Often "Mean" also works well.
5. Run Process -> Binary -> Watershed
6. Save the resulting image in the .tif

Another promising tool is CellPose, however it may require some manual labeling to fine-tune the network.

Segmenting cells with pronounced intracellular structure

High-resolution protocols, such as MERFISH or seq-FISH, can capture intracellular structure. Most often, it would mean pronounced difference between nuclear and citoplasmic gene composition. By default, such differences would push Baysor to recognize compartments as different cells. However, if some compartment-specific genes are known, they may be used to mitigate the situation. These genes can be specified through --nuclei-genes and --cyto-genes options, e.g.:

baysor run -m 30 --n-clusters=1 -s 30 --scale-std=50% --nuclei-genes=Neat1 --cyto-genes=Apob,Net1,Slc5a1,Mptx2 --exclude-genes='Blank*' ./molecules.csv

Please, notice that it's highly recommended to set --n-clusters=1, so molecule clustering would not be affected by compartment differences.

Note. Currently, there is no automated way to determine such compartment-specific genes. So, the only way we can suggest is interactive explaration of data. In theory, it should be straightforward to infer such information from DAPI and poly-A stains, however it is not implemented yet. If you have particular need for such functionality, please submit an issue with description of your experimental set up.

Outputs

• segmentation_borders.html: visualization of cell borders for the dataset colored by local gene expression composition (first part) and molecule clusters (second part). Shown only when -p is set.
• segmentation_cell_stats.csv: diagnostic info about cells. The following parameters can be used to filter low-quality cells:
  • area: area of the convex hull around the cell molecules
  • avg_confidence: average confidence of the cell molecules
  • density: cell area divided by the number of molecules in cell
  • elongation: ration of the two eigenvalues of the cell covariance matrix
  • n_transcripts: number of molecules per cell
• segmentation_config.toml: copy of the config to improve reproducibility
• segmentation_diagnostics.html: visualization of the algoritm QC. Shown only when -p is set.
• segmentation_params.dump: aggregated parameters from the config and CLI
• segmentation_polygons.json: polygons used for visualization in GeoJSON format. In case of 3D segmentation, it is an array of with GeoJSON polygons per z-plane, as well as "joint" polygons. Shown only if --save-polygons=geojson is set.
• segmentation.csv: segmentation info per molecule:
  • confidence: probability of a molecule to be real (i.e. not noise)
  • cell: id of the assigned cell. Value "0" corresponds to noise.
  • cluster: id of molecule cluster
  • assignment_confidence: confidence that the molecule is assigned to a correct cell
  • is_noise: shows whether molecule was assigned to noise (it's equal "true" if and only if "cell" == 0)
  • ncv_color: RGB code of the neighborhood composition coloring

Choice of parameters

Most important parameters:

• scale is the most sensitive parameter, which specifies expected radius of a cell. It doesn't have to be precise, but wrong set-up can lead to over- or under-segmentation. This parameter is inferred automatically if cell centers are provided.
• min-molecules-per-cell is the number of molecules, required for a cell to be considered as real. It really depends on a protocol. For instance, for ISS it's fine to set it to 3, while for MERFISH it can require hundreds of molecule.

Some other sensitive parameters (normally, shouldn't be changed):

• new-component-weight is proportional to probability of generating new cell for a molecule, instead of assigning it to one of existing cells. More precisely, probability to assign a molecule to a particular cell linearly depends on number of molecules, already assigned to this cell. And this parameter is used as number of molecules for a cell, which is just generated for this new molecule. The algorithm is robust to small changes in this parameter. And normally values in range 0.1-0.9 should work fine. Smaller values would lead to slower convergence of the algorithm, while larger values force emergence of large number of small cells on each iteration, which can produce noise in the result. In general, default value should work well.

Run parameters:

• num-cells-init expected number of cells in data. This parameter influence only convergence speed of the algorithm. It's better to set larger values than smaller ones.
• iters number of iterations for the algorithm. At the moment, no convergence criteria is implemented, so it will work exactly iters iterations. Thus, to small values would lead to non-convergence of the algorithm, while larger ones would just increase working time. Optimal values can be estimated by the convergence plots, produced among the results.

Citation

If you find Baysor useful for your publication, please cite:

Petukhov V, Xu RJ, Soldatov RA, Cadinu P, Khodosevich K, Moffitt JR & Kharchenko PV.
Cell segmentation in imaging-based spatial transcriptomics. 
Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01044-w