Other Modules

Segmentation

Clustering and cell type inference

Background subtraction

Backsub

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

Parameter	Default	Description
`--nonzero_fraction <value>`	`None`	Indicates fraction of pixels per crop above global threshold to ensure tissue and not only background is selected
`--nuclei_index <index>`	`1`	Index of nuclei channel to use for nonzero_fraction argument
`--num_channels <value>`	`None`	Number of channels to export per image (Ex: 40 corresponds to a 40 channel ome.tif image)
`--channelIDs <indices>`	`None`	Integer indices specifying which channels to export (Ex: 1 2 4). NOTE: You must specify a channel to use for filtering in S3segmenter as –probMapChan in –s3seg-opts
`--ring_mask`	Omitted	Specify if you have a ring mask in the same directory to use for reducing size of hdf5 image
`--crop_amount <integer>`	`None`	Number of crops you would like to extract

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

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

Parameter	Default	Description
`--model`		Currently only one model exists (zeisscyto)
`--channel`		channel containing the cytoplasm stain. 0-based indexing.
`--threshold`	0.6	A value between 0 and 1 to filter out false detections.
`--overlap`		The image is split into overlapping tiles before cytoplasm detection. This parameter specifies the amount of overlap in pixels.
`--scalingFactor`	1 (no resizing)	Factor by which to increase/decrease image size by.
`--GPU`	Default behavior is the first GPU card (0-based indexing).	If multiple GPUs are available, this specifies which GPU card to use.

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

Output

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

Optional arguments

Name	Description	Default Value
`--nuclear-channel`	The numerical index of the channel(s) from `nuclear-image` to select. If multiple values are passed, the channels will be summed.	`0`
`--compartment`	Predict nuclear or whole-cell segmentation.	`"whole-cell"`
`--image-mpp`	The resolution of the image in microns-per-pixel. A value of 0.5 corresponds to 20x zoom.	`0.5`
`--batch-size`	Number of images to predict on per batch.	`4`

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:

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

Parameter	Default	Description
`-v, --verbose`		Flag to print out progress of script
`-k NEIGHBORS`	30	The number of nearest neighbors to use when clustering.
`-n NUM_THREADS`	1	The number of cpus to use during the k nearest neighbors part of clustering.
`-c, --method`		Include a column with the method name in the output files.
`--force-transform`		Log transform the input data. If omitted, and –no– transform is omitted, log transform is only performed if the max value in the input data is >1000.
`--no-transform`		Do not perform Log transformation on the input data. If omitted, and –force-transform is omitted, log transform is only performed if the max value in the input data is >1000.

Optional arguments to scapy

Parameter	Default	Description
`-v, --verbose`		Flag to print out progress of script
`-k NEIGHBORS`	30	The number of nearest neighbors to use when clustering.
`-c, --method`		Include a column with the method name in the output files.
`--force-transform`		Log transform the input data. If omitted, and –no– transform is omitted, log transform is only performed if the max value in the input data is >1000.
`--no-transform`		Do not perform Log transformation on the input data. If omitted, and –force-transform is omitted, log transform is only performed if the max value in the input data is >1000.

Optional arguments to FlowSOM

Parameter	Default	Description
`-v, --verbose`		Flag to print out progress of script
`-c, --method`		Include a column with the method name in the output files.
`-n, --num-metaclusters`	25	The number of clusters for meta-clustering.
`--xdim XDIM`	10	The number of neurons in the SOM in the x dimension.
`--ydim YDIM`	10	The number of neurons in the SOM in the y dimension.
`--force-transform`		Log transform the input data. If omitted, and –no– transform is omitted, log transform is only performed if the max value in the input data is >1000.
`--no-transform`		Do not perform Log transformation on the input data. If omitted, and –force-transform is omitted, log transform is only performed if the max value in the input data is >1000.

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

Parameter	Default	Description
`--plots <off\|pdf\|png>`	`off`	Produces QC plots of individual marker fits and summary UMAP plots in .png or .pdf format.
`--id <name>`	`CellID`	Name of the column that contains cell IDs
`--log <yes\|no\|auto>`	`auto`	When a log₁₀ transformation should be applied prior to fitting the data. The tool will do this automatically if it detects large values. Use `--log no` to force the use of original, non-transformed values instead.
`--comb <hmean\|gmean>`	`gmean`	Whether to use harmonic mean (`hmean`) or geometric mean (`gmean`) to combine probabilities of expression for individual markers.
`--sfx <suffix>`	automatically determined	A common suffix on the marker columns (e.g., `_cellMask` or `_nucleiMask`). The suffix will be removed in the output plots and tables to improve readability. Use `$` to force an empty suffix.
`--umap`	disabled	Include this flag to generate UMAP plots.
`--mct <filename>`		The tool has a basic marker -> cell type (mct) mapping in `typemap.csv`. More sophisticated mct mappings can be defined by creating a `custom-map.csv` file with two columns: `Marker` and `State`.

Backsub

Description

Backsub is a background subtraction module for sequential IF images. It performs autofluorescence, 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.

Example params.yml:

workflow:
  background: true

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.