This repository contains the code used to perform the single-cell proteogenomic analysis of the human cell cycle. This study was based on immunofluorescence staining of ~200k cells for single-cell analysis of proteomic heterogeneity and ~1k cells for analysis of single-cell RNA variability. These analyses were integrated with other proteomic studies and databases to investigate the functional importance of transcript-regulated and non-transcript regulated variability.
Please find the preprint manuscript here: https://www.biorxiv.org/content/10.1101/543231v2
The code is listed in order of execution, e.g. "1_", "2_" etc. The output of each script is used in the subsequent script.
The logic for these analyses is contained in the SingleCellProteogenomics folder.
The input files are contained in the "input" folder. This folder is linked here as a zip file, input.zip. Expand this folder within the SingleCellProteogenomics folder. (If you are looking for the raw imaging proteomic dataset produced after filtering artifacts and such, that is located here.)
The output files are added to a folder "output" during the analysis, and figures are added to a folder "figures."
An R-script used to analyze skewness and kurtosis (noted in the Methods of the manuscript) is contained in the other_scripts folder. The other_scripts/ProteinDisorder.py script utilizes IUPRED2A and a human UniProt database.
Prerequisites are listed in enviro.yaml file. They can be installed using conda:
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Install Miniconda from https://docs.conda.io/en/latest/miniconda.html
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From this directory run
conda env create -n fucci -f enviro.yaml; conda activate fuccito install and activate these prerequisites.
For the snakemake workflow used to analyze the scRNA-Seq dataset, including RNA velocity calculations and louvain unsupervised clustering, please see this repository: https://github.com/CellProfiling/FucciSingleCellSeqPipeline.