estimatesVPath: Displays strength of association between pathologies and cell...

In meconsens/CellTyPETool: Facilitates Deconvolution of Bulk Tissue RNAseq Data for Disease Phenotype Analysis

Description

A function that generates a volcano plot showing the significance of associations between each cell type proportion derived and the pathology in question

Usage

estimatesVPath(estimates, metadata, cellTypeNames, covar, pathologyName)

Arguments

estimates	The estimates of cell type proportions returned by calcAndCompare() either markerGeneProfile derived or BRETIGEA derived
metadata	A dataframe with subjects also in countDf and rows indicating the subjects id, some covariate, and disease state score or pathology.
cellTypeNames	The names of all the unique cell types for which there are markers in bretCellMarkers: unique(bretCellMarkers$cell)
covar	A covariate to be taken into account when running linear models to check the association between the cell type indicated by cell and the pathology indicated by pathologyName.
pathologyName	The pathology associated with the disease in question for which the association between it and the cell type indicated by cell is being examined.

Value

Returns a volcano plot showing the significance of associations between each cell type proportion derived and the pathology in question

References

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Kamil Slowikowski (2020). ggrepel: Automatically Position Non-Overlapping Text Labels with 'ggplot2'. R package version 0.8.2. https://CRAN.R-project.org/package=ggrepel

Mancarci, B. O., Toker, L., Tripathy, S. J., Li, B., Rocco, B., Sibille, E., & Pavlidis, P. (2017). CrossLaboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data. eNeuro, 4(6), ENEURO.0212-17.2017. https://doi.org/10.1523/ENEURO.0212-17.201

McKenzie, A.T., Wang, M., Hauberg, M.E. et al. Brain Cell Type Specific Gene Expression and Coexpression Network Architectures. Sci Rep 8, 8868 (2018). https://doi.org/10.1038/s41598-018-27293-5

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.

Examples

# Examples 1:
# Using countDf, bretCellMarkers, mgpCellMarkers datasets available with package

calcAndCompareResults <- calcAndCompare (
                countDf = countDf,
                mgpCellMarkers = mgpCellMarkers,
                bretCellMarkers = bretCellMarkers)

estimatesVPathResults <- estimatesVPath(
                estimates = calcAndCompareResults$bret,
                metadata = metadata,
                cellTypeNames = unique(bretCellMarkers$cell),
                covar = "Covariate",
                pathology = "DiseasePhenotypeScore")

meconsens/CellTyPETool documentation built on Jan. 1, 2021, 9:25 a.m.