Module Options

Parameters under the namespace options regulate the function of individual modules in the pipeline. Parameters are matched to the appropriate module using the name: fields in the namespace modules.

Click on the different modules to learn more.

Core modules:

All modules in MCMICRO are available as standalone executable Docker containers. When running modules within MCMICRO, the inputs and outputs will be handled by the pipeline and do not need to be specified explicitly.

BaSiC

Illumination correction

Description

The module implements the BaSiC method for correcting uneven illumination, developed externally by (Peng et al., 2017). The module doesn’t have any additional parameters.

Usage

By default, MCMICRO skips this step as it requires manual inspection of the outputs to ensure that illumination correction does not introduce artifacts for downstream processing. Add start-at: illumination to workflow parameters to request that MCMICRO runs the module.

• Example params.yml:

workflow:
  start-at: illumination

• Running outside of MCMICRO: Instructions.

Input

Unstitched images in any BioFormats-compatible format. Nextflow will take these from the raw/ subdirectory within the project.

Output

Dark-field and flat-field profiles for each unstitched image. Nextflow will write these to the illumination/ subdirectory within the project.

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ASHLAR

Stitching and registration

Description

The module performs simultaneous stiching of tiles and registration across channels. Check the ASHLAR website for the most up-to-date documentation.

Usage

MCMICRO runs ASHLAR by default. Add ashlar: to options to control its behavior.

• Example params.yml:

options:
  ashlar: --flip-y -c 5

• Default: ashlar: -m 30
• Running outside of MCMICRO: ASHLAR website.

Input

• Unstitched images in any BioFormats-compatible format. Nextflow will take these from the raw/ subdirectory within the project.
• [Optional] Dark-field and flat-field profiles from illumination correction. Nextflow will take these from the illumination/ subdirectory within the project.

Output

A pyramidal, tiled .ome.tif. Nextflow will write the output file to registration/ within the project directory.

Optional parameters for ASHLAR

Troubleshooting

Visit the ASHLAR website for troubleshooting tips.

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Coreograph

TMA core detection and dearraying

Description

The modules uses the popular UNet deep learning architecture to identify cores within a tissue microarray (TMA). After identifying the cores, it extracts each one into a separate image to enable parallel downstream processing of all cores.

Usage

By default, MCMICRO assumes that the input is a whole-slide image. Add tma: true to workflow to indicate that the input is a TMA instead. Add coreograph: to options to control the module behavior.

• Example params.yml:

workflow:
  tma: true
options:
  coreograph: --channel 3

• Running outside of MCMICRO: Instructions.

Input

A fluorescence image of a tissue microarray where at least one channel is of DNA (such as Hoechst or DAPI). Nextflow will take this from the registration/ subfolder within the project.

Output*

1. Individual cores as .tif stacks with user-selectable channel ranges
2. Binary tissue masks (saved in the ‘mask’ subfolder)
3. A TMA map showing the labels and outlines of each core for quality control purposes
   
4. A text file listing the centroids of each core in the format: Y, X

* Nextflow will write images and masks to the dearray/ subfolder and the TMA map to the qc/coreo/ subfolder within the project.

Optional arguments to Coreograph

Troubleshooting

A troubleshooting guide can be found within Coreograph parameter tuning.

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UnMICST

Image segmentation - probability map generation

Description

UnMICST uses a convolutional neural network to annotate each pixel with the probability that it belongs to a given subcellular component (nucleus, cytoplasm, cell boundary). Check the UnMICST website for the most up-to-date documentation.

Usage

MCMICRO applies UnMicst to all input images by default. Add unmicst: to options to control its behavior.

• Example params.yml:

options:
  unmicst: --scalingFactor 0.5

• Running outside of MCMICRO: Instructions.

Input

An .ome.tif, preferably flat field corrected. The model is trained on images acquired at a pixelsize of 0.65 microns/px. If your settings differ, you can upsample/downsample to some extent. Nextflow will use as input files from the registration/ subdirectory for whole-slide images and from the dearray/ subdirectory for tissue microarrays.

Output *

1. 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.
2. a QC image with the DNA image concatenated with the nuclei contour probability map with suffix Preview

* Nextflow will write probability maps to the probability-maps/unmicst/ subfolder and the previews to the qc/unmicst/ subfolder within the project.

Optional arguments to UnMicst

Troubleshooting

A troubleshooting guide can be found within UnMICST parameter tuning - additional information is also available on the UnMICST website .

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S3segmenter

Image segmentation - cell mask generation

Description

The modules applies standard watershed segmentation to probability maps to produce the final cell/nucleus/cytoplasm/etc. masks.

Usage

By default, MCMICRO applies S3segmenter to the output of all modules that produce probability maps. Add s3seg: to options to control its behavior..

• Example params.yml:

options:
  s3seg: --logSigma 2 10

Inputs

1. A fully-stitched and registered .ome.tif, preferably flat field corrected. Nextflow will take these from the registration/ and dearray/ subdirectories, as approrpriate.
2. A 3-class probability map, as derived by modules such as UnMICST or Ilastik.

S3segmenter assumes that you have:

1. Acquired images of your sample with optimal acquisition settings.
2. Stitched and registered the tiles and channels respectively (if working with a large piece of tissue) and saved it as a Bioformats compatible tiff file.
3. Processed your image in some way so as to increase contrast between individual nuclei using classical or machine learning methods such as Ilastik (a random forest model) or UnMICST (a deep learning semantic segmentation model based on the UNet architecture). MCMICRO supports both.

Output

1) 32-bit label masks for each compartment of the cell:

• nuclei.ome.tif (nuclei)
• cytoplasm.ome.tif (cytoplasm)
• cell.ome.tif (whole cell)
• If only nuclei segmentation was carried out, cell.ome.tif is identical to nuclei.ome.tif

Nextflow saves these files to the segmentation/ subfolder within your project.

2) Two-channel quality control files with outlines overlaid on gray scale image of channel used for segmentation

• nucleiOutlines.tif (nuclei),
• cytoplasmOutlines.tif (cytoplasm)
• cellOutlines.tif (whole cell)
• If only nuclei segmentation was carried out, cellOutlines.tif is identical to nucleiOutilnes.tif

Nextflow saves these files to the qc/s3seg/ subfolder within your project.

NOTE: There are at least 2 ways to segment cytoplasm: i) using a watershed approach or ii) taking an annulus/ring around nuclei. Files generated using the annulus/ring method will have ‘Ring’ in the filename whereas files generated using watershed segmentation will not. It is important that these two groups of files are NOT combined and analyzed simultaneously as cell IDs will be different between them.

Optional arguments to S3Segmenter

Nuclei parameters:

Cytoplasm parameters:

Troubleshooting

A troubleshooting guide can be found within S3segmenter parameter tuning.

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MCQuant

Single-cell data quantification

Description

The modules uses one or more segmentation masks against the original image to quantify the expression of every channel on a per-cell basis. Check the MCQuant README for the most up-to-date documentation.

Usage

By default, MCMICRO runs MCQuant on all cell segmentation masks that match the cell*.tif filename pattern. Add mcquant: to options to specify a different mask or to provide additional module-specific arguments to MCMICRO.

• Example params.yml:

options:
  mcquant: --masks cytoMask.tif nucleiMask.tif

• Running outside of MCMICRO: Instructions.

Inputs

1. A fully stitched and registered image in .ome.tif format. Nextflow will use images in the registration/ and dearray/ subfolders as appropriate.
2. One or more segmentation masks in .tif format. Nextflow will use files in the segmentation/ subfolder within the project.
3. A .csv file containing a marker_name column specifying names of individual channels. Nextflow will look for this file in the project directory.

Output

A cell-by-feature table mapping Cell IDs to marker expression and morphological features (including x,y coordinates).

Optional parameters for MCQuant

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CyLinter

Quality control

Description

CyLinter is a human-in-the-loop quality control pipeline. It accepts as input the set of files generated by MCMICRO, including segmentation masks and single-cell feature tables, and returns a set of de-noised feature tables for use in downstream analyses.

Usage

Because it requires human interactivity, CyLinter is not executed by MCMICRO directly. Instead, users are encourage to follow steps outlined on the CyLinter website after applying MCMICRO to their data.

Screenshots depicting different phases of the CyLinter workflow.

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SCIMAP

Spatial analysis

Description

SCIMAP is a suite of tools that enables spatial single-cell analyses. Check the SCIMAP website for the most up-to-date documentation.

Usage

MCMICRO allows users to automatically apply SCIMAP’s clustering algorithms to the cell-by-feature table produced by MCQuant. The clustering results can be subsequently used for manual assignment of cell states. Since MCMICRO stops at MCQuant by default, users will need to explicitly request that the pipeline continues to the clustering step. MCMICRO’s usage of SCIMAP doesn’t have any parameters, and users are encouraged to check the SCIMAP website for more sophisticated human-in-the-loop analyses.

Add downstream: scimap and stop-at: downstream to workflow parameters to enable SCIMAP. Add mcquant: to options to control its behavior.

• Example params.yml:

workflow:
  stop-at: downstream
  downstream: scimap
options:
  scimap: --csv

• Running outside of MCMICRO: Instructions.

Input

A cell-by-feature table in .csv format, as produced by MCQuant. Nextflow will look for these tables in the quantification/ subdirectory within the project.

Output

1. A table of cluster assignments for each cell by the different clustering algorithms implemented within SCIMAP. These tables will be generated in .csv and .h5ad formats.
2. A set of UMAP plots for the different clustering algorithms, with individual plots written to the plots/ subdirectory in .pdf format.

Nextflow will write all outputs to the cell-states/scimap/ subdirectory within the project.

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Minerva

Interactive viewing and sharing

Description

Minerave allows for fast, interactive viewing of multiplexed images. It also enables highlighting and effective sharing of important regions of interest among collaborators.

Usage

MCMICRO can automatically generate un-annotated Minerva stories if users enable the viz workflow parameter.

Annotated narratives must be manually generated, and users must provide MCMICRO outputs to Minerva in a separate workflow. To learn more about making Minerva stories, visit the Minerva wiki for the most up-to-date information about the Minerva suite.

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Other modules

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.

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: mesmer
options:
  mesmer: --image-mpp 0.25 --nuclear-channel 0 --membrane-channel 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

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

Optional arguments

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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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Imagej-rolling-ball

Description

Imagej-rolling-ball is a background subtraction module that applies ImageJ’s “Subtract Background…” function to the multi-channel whole-slide images. Application of rolling ball background subtraction for widefield fluorescent microscopy is demonstrated here

Usage

By default, MCMICRO does not perform background subtraction. Add background: true and background-method: imagej-rolling-ball to module options to run it.

Note that as mentioned here, the radius of the rolling ball “should be at least as large as the radius of the largest object in the image that is not part of the background”. The default radius is 100 pixels as below (imagej-rolling-ball: 100 -n=4 -j="-Xmx4g"). We generally use 100 for images with resolution of 0.325 µm/pixel.

• Example params.yml:

workflow:
  background: true
  background-method: imagej-rolling-ball

options:
  imagej-rolling-ball: 100 -n=4 -j="-Xmx4g"

• Running outside of MCMICRO: Instructions.

Inputs

• Stitched and registered multi-cycle .ome.tif

Outputs

• A pyramidal, tiled {input-image-name}-ij_rolling_ball_{radius}.ome.tif. Nextflow will write the output file to background/ within the project directory.

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Suggest a module

Module suggestions can be made by posting to https://forum.image.sc/ and tagging your post with the mcmicro tag.

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