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Parameter reference

Parameters controlling the pipeline behavior

The following parameters control the pipeline as a whole. These can be specified on the command line using the double-dash format (e.g., --in), or inside a YAML file as key-value pairs. Parameters that don’t require an explicit value because their presence controls the behavior (e.g., --tma) should instead be assigned to true in the YAML file. For example,

Example: nextflow run labsyspharm/mcmicro --in /my/data --tma

or equivalently: nextflow run labsyspharm/mcmicro -params-file myparams.yml, where myparams.yml contains

in: /my/data
tma: true

Mandatory parameters:

--in /local/pathLocation of the data

Optional parameters:

--sample-name <myname>Directory name supplied to --inThe name of the experiment/specimen
--start-at <step>registrationName of the first step to be executed by the pipeline. Must be one of illumination, registration, dearray (TMA only), probability-maps, segmentation, quantification, cell-states
--stop-at <step>quantificationName of the final step to be executed by the pipeline. Spans the same vocabulary as --start-at.
--tmaOmittedIf specified, mcmicro treats input data as a TMA. If omitted, the input is assumed to be a whole-slide image.
--ilastik-model <model.ilp>NoneA custom .ilp file to be used as the classifier model for ilastik.
--probability-maps <choice>unmicstWhich module(s) to use for probability map computation. Must be one of unmicst, ilastik, all (unmicst AND ilastik), and cypository for cytoplasm segmentation

Parameters for individual modules

Module-specific parameters can be specified using the various opts arguments, followed by the parameters enclosed inside single quotes ':

Example 1: nextflow run labsyspharm/mcmicro --in /my/data --ashlar-opts '-m 30 --pyramid'

Example 2: nextflow run labsyspharm/mcmicro --in /my/data --nstates-opts '--log no --plots pdf'

Example 3: nextflow run labsyspharm/mcmicro --in /my/data --quant-opts '--masks cytoMask.tif nucleiMask.tif'

Arguments to ASHLAR (--ashlar-opts):

Up-to-date list can be viewed at

Arguments to Coreograph(--core-opts):

Up-to-date list can be viewed at

Arguments to UnMicst(--unmicst-opts):

--tool <version>unmicst-soloUnMicst version: unmicst-legacy is the old single channel model. unmicst-solo uses DAPI. unmicst-duo uses DAPI and lamin.
--modelhuman nuclei from DAPIThe name of the UNet model. By default, this is the human nuclei model that identifies nuclei centers, nuclei contours, and background from a DAPI channel. Other models include mouse nuclei from DAPI, and cytoplasm from stains resembling WGA
--channel <number>1The channel used to infer and generate probability maps from. If using UnMicst2, then specify 2 channels. If only 1 channel is specified, it will simply be duplicated. NOTE: If not using default value, the 1st channel must be specified to S3segmenter as –probMapChan in –s3seg-opts
--classOrderNoneIf your training data isn’t in the order 1. background, 2. contours, 3. foreground, you can specify the order here. For example, if you had trained the class order backwards, specify --classOrder 3 2 1. If you only have background and contours, use --classOrder 1 2 1.
--mean <value>Extracted from the modelOverride the trained model’s mean intensity. Useful if your images are significantly dimmer or brighter.
--std <value>Extracted from the modelOverride the trained model’s standard deviation intensity. Useful if your images are significantly dimmer or brighter.
--scalingFactor <value>1An upsample or downsample factor used to resize the image. Useful when the pixel sizes of your image differ from the model (ie. 0.65 microns/pixel for human nuclei model)
--stackOutputSpecifiedIf selected, UnMicst will write all probability maps as a single multipage tiff file. Otherwise, UnMicst will write each class as a separate file.
--GPU <index>AutomaticExplicitly specify which GPU (1-based indexing) you want to use. Useful for running on local workstations with multiple GPUs.

Arguments to Ilastik(--ilastik-opts):

--nonzero_fraction <value>NoneIndicates fraction of pixels per crop above global threshold to ensure tissue and not only background is selected
--nuclei_index <index>1Index of nuclei channel to use for nonzero_fraction argument
--cropOmittedIf specified, crop regions for ilastik training
--num_channels <value>NoneNumber of channels to export per image (Ex: 40 corresponds to a 40 channel ome.tif image)
--channelIDs <indices>NoneInteger 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_maskOmittedSpecify if you have a ring mask in the same directory to use for reducing size of hdf5 image
--crop_amount <integer>NoneNumber of crops you would like to extract

Up-to-date list can be viewed at

Arguments to S3Segmenter(--s3seg-opts):

--probMapChan <index>1which channel is used for nuclei segmentation. Coincides with the channel used in upstream semantic segmentation modules. Must specify when different from default.
--crop <selection>noCropType of cropping: interactiveCrop - a window will appear for user input to crop a smaller region of the image; plate - this is for small fields of view such as from a multiwell plate; noCrop, the default, is to use the entire image

Nuclei parameters:

--nucleiFilter <selection>IntPMMethod to filter false positive nuclei: IntPM - filter based on probability intensity; Int - filted based on raw image intensity
--logSigma <value> <value>3 60A range of nuclei diameters to search for.

Cytoplasm parameters:

--segmentCytoplasm <selection>ignoreCytoplasmSelect whether to segmentCytoplasm or ignoreCytoplasm
--CytoMaskChan <index>2One or more channels to use for segmenting cytoplasm, specified as 1-based indices (e.g., 2 is the 2nd channel).
--cytoMethod <selection>distanceTransformThe method to segment cytoplasm: distanceTransform - take the distance transform outwards from each nucleus and mask with the tissue mask; ring - take an annulus of a certain pixel size around the nucleus (see cytoDilation); hybrid - uses a combination of greyscale intensity and distance transform to more accurately approximate the extent of the cytoplasm. Similar to Cellprofiler’s implementation.
--cytoDilation <value>5The number of pixels to expand from the nucleus to get the cytoplasm ring.
--TissueMaskChan <index>Union of probMapChan and CytoMaskChanOne or more channels to use for identifying the general tissue area for masking purposes.

Arguments to quantification(--quant-opts):

Up-to-date list can be viewed at

Arguments to naivestates(--nstates-opts):

Up-to-date list can be viewed at