The goal of CellTyPETool is to facilitate research that requires deconvolution of bulk-tissue RNAseq data. The package can be used to generate cell type proportion estimations from bulk-tissue RNAseq using two different validated methods, markerGeneProfile and BRETIGEA. While other R packages have been published that calculate cell type proportions (McKenzie et al, 2018; Mancarci et al., 2017), they only offer their own analysis and not a comparison between, nor do they offer visualizations between cell-type proportions associations with disease or other important phenotypes. Additionally, they don’t offer simple way to check which marker genes were encorporated in the cell-type proportion calculation.
To install the latest version of the package:
require("devtools")
devtools::install_github("meconsens/CellTyPETool", build_vignettes = TRUE)
library("CellTyPETool")data(package = "CellTyPETool")
ls("package:CellTyPETool")CellTyPETool has 4 datasets.
The bretCellMarkers dataset is a dataframe with two columns: markers and cell. The markers column contains marker genes for brain cell types astrocyte, endothelial, microglia, neuron, oligodnedrocyte and oligodendrocyte precursor. This is the marker gene list from the BRETIGEA R package. The cell column indicates which of the cell types ( astrocyte, endothelial, microglia, neuron, oligodnedrocyte and oligodendrocyte precursor cells) the marker gene is a marker for. The dataset is formatted to be run by the BRETIGEA findCells method or the CellTyPETool findCellsMod function.
The mgpCellMarkers dataset is a nested list with 6 sub-lists of marker genes for brain cell types astrocyte, endothelial, microglia, neuron, oligodnedrocyte and oligodendrocyte precursor cells.This is the marker gene list from the BRETIGEA R package that has been reformatted to be run by the markerGeneProfile markerGeneProfile method.
The countDf dataset is a dataframe with 395 rows of genes and 378 subjects taken from the BRETIGEA R package.
The metadata dataset is a dataframe with 345 subjects also in countDf and 3 rows indicating the subjects id (Sample), some covariate (covariate) and disease state score (DiseasePhenotypeScore). Sample is the subjects id which corresponds to countDf, Covariate is some confounding factor that must be accounted for when finding signals between cell-type proportions and a disease phenotype (could be batch etc.), DiseasePhenotypeScore is some indication of disease state, could be a score of combined AD pathology variables (CERAD score, Braak pathology stage etc.).
CellTyPETool contains 6 functions.
The bretMarkerEffectOnPathology function makes use of a modified findCells function from BRETIGEA R package to a graph of the significance of the cell type proportion specified’s association to the pathology indicated upon marker addition from 0 to n.
The markersPathology function runs a linear model on the cell-type proportion estimates by BRETIGEA R package findCells function.
The findCellsMod function is a modifed version of the findCells function from the R package BRETIGEA, modified to return a list of sample-by-cell type matrix of estimate cell type proportion variables and the markers used.
The calcAndCompare function calculates cell type proportions using two validated methods, markerGeneProfile and findCells, from the markerGeneProfile and BRETIGEA R packages respectfully.
The estimatesVPath function generates a volcano plot showing the significance of associations between each cell type proportion derived and the pathology in question.
The genesUsed function returns the list of marker genes used in the cell type proportions calculations of two validated methods, markerGeneProfile and findCells, from the markerGeneProfile and BRETIGEA R packages respectfully.
The mgpQCMetrics function returns a dataframe of the genes used, the removedMarkerRatios and the proportion of variance explained by the first PC in the markerGeneProfile estimation method. This provides a way to evaluate the accuracy of the markerGeneProfile method for cell type estimations from the markerGeneProfile R package.
browseVignettes("CellTyPETool")An overview of the package is illustrated below.
The author of the package is Micaela Consens.
The bretMarkerEffectOnPathology function makes use of a modified findCells function from BRETIGEA R package to a graph of the significance of the cell type proportion specified’s association to the pathology indicated upon marker addition from 0 to n. The bretMarkerEffectOnPathology function uses the tidyr, dplyr, tibble, scales, stats, utils, ggplot2, ggrepel, and magrittr packages.
The markersPathology function runs a linear model on the cell-type proportion estimates by BRETIGEA. The markersPathology function uses the stats and magrittr packages.
The findCellsMod function is a modifed version of the findCells function from the R package BRETIGEA, modified to return a list of sample-by-cell type matrix of estimate cell type proportion variables and the markers used. (Part of the code for findCellsMod function has been taken from BRETIGEA R package. Section of the borrowed code is clearly indicated and referenced in the findCellsMod help file). The findCellsMod function uses the stats package.
The calcAndCompare function calculates cell type proportions using two validated methods, markerGeneProfile and findCells, from the markerGeneProfile and BRETIGEA R packages respectfully. It then compares the cell type proportions calculated by each. (Part of the code for findCellsMod function has been taken from BRETIGEA and markerGeneProfile and R package. Section of the borrowed code is clearly indicated and referenced in the calcAndCompare help file). The calcAndCompare function uses the markerGeneProfile, BRETIGEA, tibble, dplyr, stats, reshape, ggplot2, and magrittr package.
The estimatesVPath function generates a volcano plot showing the significance of associations between each cell type proportion derived and the pathology in question. It then generates a volcano plot to view this. The estimatesVPath function uses the stats, ggrepel, ggplot2, and magrittr package.
The genesUsed function returns the list of marker genes used in the cell type proportions calculations of two validated methods, markerGeneProfile and findCells, from the markerGeneProfile and BRETIGEA R packages respectfully. (Part of the code for genesUsed function has been taken from BRETIGEA and markerGeneProfile and R package. Section of the borrowed code is clearly indicated and referenced in the genesUsed help file). The genesUsed function uses the markerGeneProfile, and magrittr package.
The mgpQCMetrics function returns various QC metrics that are relevant to the markerGeneProfile R package method of calculating cell type proportions. The mgpEstimate method is taken from the markerGeneProfile method, but the code to extract the necessary features of the method to evaluate the accuracy/effectivenes of the cell-type proportion estimates is novel. The dataframe returned by this function details a list of genesUsed, the the removedMarkerRatios and the proportion of variance explained by the first PC in the markerGeneProfile R package estimation method.
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Hadley Wickham and Dana Seidel (2020). scales: Scale Functions for Visualization. R package version 1.1.1. https://CRAN.R-project.org/package=scales
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
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.0212-17.2017
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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/.
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
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
This package was developed as part of an assessment for 2020 BCB410H: Applied Bioinformatics, University of Toronto, Toronto,CANADA.