The directory containing all .tiff files

Location of directory containing starfish experiment.json file. Only used when both skip_formatting and skip_processing are true.

Whether to rescale images before running decoding.

If true, QC will not be run.

The name of the aux view to look at in the experiment file for image segmentation.

Pixels above this percentile are set to 1.

Pixels below this percentile are set to 0.

Expected diameter of cells. Should be 0 if a custom model is used.

If true, will save graphical output to a pdf.

If true, segmentation (and QC) will be skipped.

If true, mrna data will be used in cellpose calculations.

The views to use for cellpose segmentation.

PyYML-formatted dictionary outlining how the truerounds in imaging relate to the pseudorounds in the decoding codebook. The keys are truerounds and the values are the corresponding pseudorounds.

Variables to get substituted into the file_format string.

The number of FOVs that are included in this experiment

Root directory containing space_tx formatted experiment. Only used if skip_formatting is true.

Whether to treat the zplanes as a 3D image.

Flattened json input, refer to record entry.

Sigma value for low pass gaussian filter. Will not be run if not provided.

If true, will add blanks with a hamming distance 1 from existing codes.

The index of the first FOV (for file names).

Sigma value for high pass gaussian filter. Will not be run if not provided.

If true, the baysor step will be run.

The name of the auxillary view to be processed in parallel with primary view, such as for anchor round in ISS processing. Will not be included if not provided.

PyYML-formatted list containing a dictionary outlining how the truechannels in imaging relate to the pseudochannels in the decoding codebook. The index of each dict within the list is the trueround % (count of pseudorounds). The keys of the dict are the channels within the image and the values are the pseudochannels in the converted notebook.

String with layout for .tiff files

If true, will run ripley K estimates to find spatial density measures. Can be slow.

If provided, the number of processes that will be spawned for processing. Otherwise, the maximum number of available CPUs will be used.

The number of imaging rounds in the experiment

Whether to generate a reference image and use it alongside spot detection.

Flattened csv input, refer to record entry.

Levelling method for clip and scale application. Defaults to SCALE_BY_CHUNK.

The index of the first round (for file names).

If true, cellpose will be run.

The number of z-planes in each image

If not None, removes cytoplasms whose nuclei lie within the given distance from the border.

The number of total channels per imaging round

Flattened json input, refer to record entry.

The data org file used to describe .dax formatted images.

If provided, steps after conversion will only be run on FOVs with these indices.

Radius for white top hat filter. Should be slightly larger than the expected spot radius. Will not be run if not provided.

The index of the first channel (for file names).

threshold for filtering cell segmentations (increasing this will filter out lower confidence segmentations), range is 0 to infinity

Pixel size labels are dilated by in final step. Helpful for closing small holes that are common from thresholding but can also cause cell boundaries to exceed their true boundaries if set too high. Label dilation respects label borders and does not mix labels.

Flattened directory input, refer to record entry \"binary_mask\"

json file containing parameters for the whole experiment. If variable is present here, it will supercede any passed in the yml.

Radius for rolling ball background subtraction. Larger values lead to increased intensity evening effect. The value of 3 will work for most datasets. Will not be run if not provided.

If provided, the step to run decoding will be split into n batches, where each batch is (FOV count/n) FOVs big.

The name of the auxillary view to be used for background subtraction. Background will be estimated if not provided.

Number of iterations to perform for deconvolution. High values remove more noise while lower values remove less. The value 15 will work for most datasets unless image is very noisy. Will not be run if not provided.

If true, histograms will be equalized.

If true, will skip first two stages and start at processing.

If true, image processing step will be skipped.

Order of non x,y dimensions within each image.

Sigma value for deconvolution. Should be approximately the expected spot size.

Flattened record input, refer to record entry \"binary_mask\"

maximum size for a cell (in pixels)

minimum size for a cell (in pixels)

threshold for stitching together segmentations that occur at the same xy location but in adjacent z slices, range is 0 to 1. This should only be used when the image is 3D.

The name of the auxillary view to be used for image registration.

determines the extent of the segmentations (0 is the default more negative values result in larger cells, more positive values result in smaller cells), range is -6 to 6.

Flattened file input, refer to record entry \"binary_mask\"

If true, `background_view` will be aligned to `aux_name`.

If true, registration will be performed between the first round of register_aux_view and the primary view.

Flattened record input, refer to record entry \"binary_mask\"

Pipeline will not remove genes that do not match a target and do not meet criteria.

Manually trained cellpose model to use.

Cellpose-provided model to use.