CIRCular robUST statistical methodology to analyse human molecular rhythms from post-mortem samples.
CIRCUST is achieved in R and is easy to use. The code provided in this GitHub replicated the four steps described in CIRCUST paper (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011510).
Run the R script named runCIRCURST.R to ,conduct the methodology. The file matrixIn.RData is loaded on the R script and serves as example of unorderd post-mortem gene expression matrix. matrixIn: gene expression matrix of size 56200X479 (genes X unordered samples/individuals) at a given tissue. Note that it is a real example, so it requires a reasonable amount of computational time.
INPUTS, see CIRCUST paper for details:
- Expression matrix (matrixIn).
- Number of random selections of the genes at the TOP (K).
- Name tissue (nameTissue).
- Core clock gene names (coreG).
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Preparatory work. When you use our tool, you should source R source functions and install some R packages detailed. Run in the Rscrip: source("functionGTEX_cores.R")
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Preprocessing. Run the code line under this name in runCIRCURST.R to clean and normalize the data.
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Preliminary order. Run the code lines under this name in runCIRCURST.R to obtain a preliminary order based on the core clok gene. Note that the second element of the list obtained as output from basicOder_cores function provides CIRCUST estimates for the circadian phase time for each sample. In the code we usually refer to these values using the term "esc".
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TOP Rhythmic orderings. Run the code lines under this name in runCIRCURST.R to derive the tissue-specific TOP gene list and K circular orders based on K random selections.
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Robust Estimation. Run the code lines under this name in runCIRCURST.R to compute FMM predictions as functions of K circular ordering for the TOP genes.
OUTPUT:
- Data frame with the FMM parameter estimations for TOP genes, k=1,...,K (outs).