Other Modules

Segmentation

1. Ilastik
2. Cypository
3. Mesmer
4. Cellpose

Clustering and cell type inference

1. Clustering
2. naivestates

Background subtraction

1. Backsub

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Ilastik

Description

The module provides a command-line interface to the popular ilastik toolkit and serves as another method for generating probability maps that can be used as an alternative to UnMICST. Check the GitHub for the most up-to-date documentation.

Usage

By default, MCMICRO runs UnMicst for probability map generation. To run Ilastik instead of or in addition to UnMicst, add segmentation: ilastik to workflow parameters. When specifying multiple methods, the method names should be provided as a list enclosed in square brackets. Arguments should be passed to Ilastik via ilastik: in the module options section, while custom models can be provided to Ilastik via ilastik-model: workflow parameter.

• Example params.yml:

workflow:
  segmentation: [ilastik, unmicst]
  ilastik-model: /full/path/to/mymodel.ilp
options:
  ilastik: --nonzero_fraction 0.5 --num_channels 1

• Default ilastik options: --num_channels 1
• Running outside of MCMICRO: Instructions.

Input

A stitched and registered .ome.tif, preferably flat field corrected. Nextflow will use as input files from the registration/ subdirectory for whole-slide images and from the dearray/ subdirectory for tissue microarrays.

Output

The output is similar to that produced by UnMicst, namely a .tif stack where the different probability maps for each class are concatenated in the Z-axis in the order: nuclei foreground, nuclei contours, and background. Nextflow will write output to the probability-maps/ilastik/ subdirectory within the project folder.

Optional arguments

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Cypository

Description

Cypository is used to segment the cytoplasm of cells. Check the GitHub repository for the most up-to-date documentation.

Usage

Add segmentation: cypository to workflow parameters to enable Cypository. In general, it would be uncommon to run Cypository alongside probability map generators for nuclei, but it can be done by specifying method names as a list enclosed in square brackets, e.g., segmentation: [cypository, unmicst]. Additional Cypository parameters should be provided to MCMICRO by including a cypository: field in the module options section.

• Example params.yml:

  workflow:
  segmentation: cypository
  options:
  cypository: --channel 5
  

• Default cypository options: --model zeisscyto

Input

A stitched and registered .ome.tif, preferably flat field corrected. Nextflow will use as input files from the registration/ subdirectory for whole-slide images and from the dearray/ subdirectory for tissue microarrays.

Output

A .tif file that annotates individual pixels with the probability that they belong to the cytoplasm of a cell. Nextflow will write output to the probability-maps/cypository/ subdirectory within the project folder.

Optional arguments

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Mesmer

Description

The Mesmer module provides an alternative segmentation approach to UnMicst and ilastik. It is implemented and maintained by an external group. Check their GitHub repository for the most up-to-date information.

Usage

Add segmentation: mesmer to workflow parameters to enable Mesmer. When running together with UnMicst and/or ilastik, method names must be provided as a list enclosed in square brackets. Additional Mesmer parameters can be provided to MCMICRO by including a mesmer: field in the module options section.

• Example params.yml:

workflow:
  segmentation: mesmer
options:
  mesmer: --image-mpp 0.25

• Running outside of MCMICRO: Instructions.

Input

A stitched and registered .ome.tif, preferably flat field corrected. Nextflow will use as input files from the registration/ subdirectory for whole-slide images and from the dearray/ subdirectory for tissue microarrays.

Output

A segmentation mask, similar to the ones produced by S3segmenter. Nextflow will write these files directly to segmentation/.

Optional arguments

Cellpose

Description

Cellpose is a DL segmentation algorithm able to segment the nuclear or cytoplasmic compartments of the cell. Publications of this algorithm can be found in 1 and 2. A thorough documentation of the script and CLI can be found here.

Usage

To use this segmentation method add the line segmentation: cellpose in the workflow section of the params.yml file. Under the options section of params.yml specify the input arguments of the cellpose script, such as segmentation model and channel(s) on which the model will be applied. Notice that the channel(s) argument(s), i.e. –chan and –chan2, expect a zero-based index.

For large data sets it is recommended to use the parameters segmentation-recyze: true along with segmentation-channel:. In the example below we consider an image stack of 10 channels with the nuclear marker in channel 2 and membrane marker in channel 7. The use of segmentation-recyze: true will reduce the image stack to these two channels prior to segmentation, hence reindexing the stack channels such that 2–>0 and 7–>1.

• Example params.yml:

workflow:
  segmentation-channel: 2 7 
  segmentation-recyze: true
  segmentation: cellpose
options:
  cellpose: --pretrained_model cyto --chan 1 --chan2 0 --no_npy

• Running outside of MCMICRO: Github, Instructions.

Input

• The image (.tif) to be segmented should be in the registration/ subdirectory.
• –pretained_model: name of the built-in model to be used for segmentation, options include “nuclei”,“cyto” and “cyto2”. Alternatively you can give a file path to a custom retrained model. Custom models can be trained in the cellpose GUI.
• –chan: zero-based index of the channel on which the segmentation model will be applied. When using the “nuclei” model provide the index of the nuclear channel, e.g. DAPI. In the case of the “cyto” models provide the channel of the membrane marker.
• –chan2 [optional]: index of the nuclear marker channel. This argument is valid only when using the “cyto” models.

Output

A .tif image with the segmentation masks in the segmentation/ subdirectory.

Optional arguments

Clustering

Description

MCMICRO integrates three methods for clustering single-cell data. These are FastPG (Fast C++ implementation of the popular Phenograph method), Leiden community detection via scanpy, and FlowSOM.

Usage

Add a downstream: field to workflow parameters to select one or more methods. Method names should be provided as a comma-delimited list enclosed in square brackets. Additional method parameters should be provided to MCMICRO by adding fastpg:, scanpy: and flowsom: fields to the module options section.

• Example params.yml:

workflow:
  stop-at: downstream
  downstream: [fastpg, flowsom, scanpy]
options:
  fastpg: -k 10
  scanpy: -k 10

• Running outside of MCMICRO:
  • Instructions for FastPG
  • Instructions for scanpy
  • Instructions for FlowSOM

Input

All methods accept as input the cell-by-feature matrix in .csv format. Nextflow looks for these files in the quantification/ subfolder within the project directory.

Output

All methods output a .csv file annotating individual cells with their cluster index. Nextflow will write these files to the cell-states/ subfolder within the project directory.

Optional arguments to FastPG

Optional arguments to scapy

Optional arguments to FlowSOM

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Naivestates

Description

naivestates is a label-free, cluster-free tool for inferring cell types from quantified marker expression data, based on known marker <-> cell type associations. Check the GitHub repository for the most up-to-date documentation.

Usage

Add a downstream: field to workflow parameters to select naivestates. When running alongside other cell state inference methods, such as SCIMAP, method names should be provided as a list enclosed in square brackets. Custom marker to cell type (mct) mapping can be provided to naivestates via the naivestates-model: workflow parameters, while additional arguments should be specified by including a naivestates: field in the module options section.

• Example params.yml:

workflow:
  stop-at: downstream
  downstream: naivestates
  naivestates-model: /full/path/to/mct.csv
options:
  naivestates: --log no

• Default naivestates options: -p png
• Running outside of MCMICRO: Instructions.

Inputs

• A cell-by-feature table in .csv format, such as one produced by MCquant. Nextflow will look for such tables in the quantification/ subfolder of the project directory.
• [Optional] A two-column .csv file providing a many-to-many mapping between markers and cell types/states. The columns must be named Marker and State.

Outputs

• A .csv file providing probabilities that a marker is expressed for each cell-marker pair.
• If the mapping to cell types is provided, a second .csv file with probabilistic annotations of each cell with its type/state.
• A set of plots showing probability distributions and UMAP projections

Nextflow will write all outputs to the cell-states/naivestates/ subdirectory within the project. If relevant, additional QC files will be written to qc/naivestates.

Optional arguments

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Backsub

Description

Backsub is an autofluorescence subtraction module for sequential IF images. It performs pixel-level subtraction on large .ome.tif images primarily developed with the Lunaphore COMET platform outputs in mind.

Usage

By default, MCMICRO assumes background subtraction should not be performed. Add background: true to module options to indicate it should be. By default, the background-method parameter is set to backsub. If channels are removed using this module, and segmentation-channel is specified, it should be kept in mind that the index provided with segmentation-channel would refer to the index after channel removal.

• Example params.yml:

workflow:
  background: true
  background-method: backsub

• Running outside of MCMICRO: Instructions.

Inputs

• Stitched and registered multi-cycle .ome.tif
• The markers.csv file must contain a marker_name column specifying channel markers. The background column indicates which channel should be subtracted and the value must match the marker name of the background channel. The exposure column with exposure times for respective channel acquisitions is also required. Additionally, the remove column can have “TRUE” values for channels which shouldn’t be included in the output. An example markers.csv can be found here.

Outputs

• A pyramidal, tiled .ome.tif. Nextflow will write the output file to background/ within the project directory.
• A modified markers.csv to match the background subtracted image.

Optional arguments

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