README.md

This repository is an accompaniment to the paper: Cell identity revealed by precise cell cycle state mapping links data modalities.

Contents

1. data4paper
2. figs4paper
3. continuousCCprediction_figures4paper.Rmd

data4paper

This folder contains three subfolders which hold much of the data required to generate the results of the paper.

1. A01_rawData: The original images; three channels, 70 z slices, and 4 fields of view; along with the metadata from image collection.
2. A06_multiSignals_Linked: Single cell visualizations, cell segmentation coordinates per channel per cell, and the raw cell feature data as a .txt.
3. A08_Pseudotime: The cycle predictions from the models discussed in the paper, along with data objects for use in R.

figs4paper

This folder contains input images for generating the figures in the paper. Some of these images are required as they are:

1. Raw images
2. Processed in ImageJ
3. Hand spliced for better resolution, spacing, and coherency

continuousCCprediction_figures4paper.Rmd

This Rmarkdown contains the code required to generate the majority of the figures included in the paper: Fig1, 3, 4, 5; Supplementary Fig1, 2, 3, 5; and Supplementary Tables 1, 2.

For insight into the generation of Supplementary Figure 4, see doi: 10.1016/j.patter.2022.100523.

FUCCI reporter classification for new CellProfiler tables

Use scripts/run_fucci_reporters.py to run the accepted reporter classifier on one FoF or a manifest of all FoFs. Supply the new feature table, matching original segmentation mask, and raw green/red image planes. The runner selects one dominant component per original label, reruns the original classifier and permits only neither → both refinement.

See the installation, inputs, batch commands and output guide. Default thresholds were calibrated on 241016/FoF5; applying them to other fields does not establish their biological accuracy there.