bretMarkerEffectOnPathology: Runs BRETIGEA using top 1, top 1 & 2, top 1,2,3 ... to top...

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

Description

Runs BRETIGEA using top 1, top 1 & 2, top 1,2,3 ... to top 1...n markers A function that reruns the BRETIGEA findCells method (modified) on a loop to see the influence of different combinations of markers in determining the cell-type proportion estimate.

Usage

bretMarkerEffectOnPathology(
  countDf,
  bretCellMarkers,
  cellName,
  metadata,
  covar,
  pathologyName,
  cellTypeNames,
  n
)

Arguments

countDf	A dataframe with Gene rows and Subject columns.
bretCellMarkers	A dataframe with two columns, markers and cell where markers are genes for cell types and cell indicates the cell type markers are the gene for.
cellName	A string indicating which of the cells in the cell type marker list is being specified to look at.
metadata	A dataframe with subjects also in countDf and rows indicating the subjects id, some covariate, and disease state score or pathology.
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 pathologyNames.
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.
cellTypeNames	The names of all the unique cell types for which there are markers in bretCellMarkers: unique(bretCellMarkers$cell)
n	Specifies how many times BRETIGEA will run BRETIGEA using top 1, top 1 & 2, top 1,2,3 ... to top 1...n markers

Value

Returns a graph of the significance of the cell type proportion specified's association to the pathology indicated upon marker addition from 0 to n

References

Hadley Wickham and Dana Seidel (2020). scales: Scale Functions for Visualization. R package version 1.1.1. https://CRAN.R-project.org/package=scales

Kirill Müller and Hadley Wickham (2020). tibble: Simple Data Frames. R package version 3.0.3. https://CRAN.R-project.org/package=tibble

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

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/.

Stefan Milton Bache and Hadley Wickham (2014). magrittr: A Forward-Pipe Operator for R. R package version 1.5.https://CRAN.R-project.org/package=magrittr

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

Examples

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

bretMarkerEffectOnPathologyResults <- bretMarkerEffectOnPathology (
                          countDf = countDf,
                          bretCellMarkers = bretCellMarkers,
                          cellName = "mic",
                          metadata = metadata,
                          covar = "Covariate",
                          pathologyName = "DiseasePhenotypeScore",
                          cellTypeNames = unique(bretCellMarkers$cell),
                          n= 10)

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