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‎.Rbuildignore

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^DevKidCC\.Rproj$
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^\.Rproj\.user$
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^doc$
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^Meta$

‎.Rhistory

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‎.gitignore

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.Rproj.user
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inst/doc
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doc
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Meta
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backupdata
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‎DESCRIPTION

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Package: DevKidCC
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Title: Kidney Cell Classifier
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Version: 0.2.0
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Authors@R:
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person(given = "Sean",
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family = "Wilson",
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role = c("aut", "cre"),
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email = "[email protected]",
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comment = c(ORCID = "0000-0002-8994-0781"))
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Description: Thsi package takes a Seurat based single cell dataset and classifies the cells into their most likely kidney cell type.
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License: MIT + file LICENSE
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Encoding: UTF-8
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LazyData: true
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Roxygen: list(markdown = TRUE)
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RoxygenNote: 7.1.1
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Suggests:
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testthat (>= 2.1.0),
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knitr,
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rmarkdown
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Imports:
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ggplot2,
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dplyr,
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magrittr,
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purrr,
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networkD3,
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rlang,
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Seurat,
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patchwork,
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tibble,
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utils,
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formattable
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VignetteBuilder: knitr
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Depends:
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R (>= 2.10),
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scPred

‎DevKidCC.Rproj

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Version: 1.0
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RestoreWorkspace: No
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SaveWorkspace: No
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AlwaysSaveHistory: Default
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EnableCodeIndexing: Yes
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UseSpacesForTab: Yes
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NumSpacesForTab: 2
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Encoding: UTF-8
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RnwWeave: Sweave
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LaTeX: pdfLaTeX
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AutoAppendNewline: Yes
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StripTrailingWhitespace: Yes
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BuildType: Package
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PackageUseDevtools: Yes
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PackageInstallArgs: --no-multiarch --with-keep.source
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PackageRoxygenize: rd,collate,namespace

‎LICENSE

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YEAR: 2020
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COPYRIGHT HOLDER: Sean Wilson

‎NAMESPACE

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# Generated by roxygen2: do not edit by hand
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export("%>%")
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export(ComponentPlot)
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export(DevKidCC)
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export(DotPlotCompare)
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export(GeneSummary)
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export(SankeyPlot)
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importFrom(Seurat,"DefaultAssay<-")
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importFrom(Seurat,"Idents<-")
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importFrom(Seurat,CellsByIdentities)
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importFrom(Seurat,CreateSeuratObject)
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importFrom(Seurat,DimPlot)
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importFrom(Seurat,FetchData)
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importFrom(Seurat,Idents)
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importFrom(Seurat,MinMax)
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importFrom(Seurat,NoLegend)
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importFrom(Seurat,NormalizeData)
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importFrom(dplyr,arrange)
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importFrom(dplyr,bind_cols)
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importFrom(dplyr,bind_rows)
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importFrom(dplyr,filter)
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importFrom(dplyr,inner_join)
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importFrom(dplyr,left_join)
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importFrom(dplyr,mutate)
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importFrom(dplyr,select)
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importFrom(dplyr,transmute)
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importFrom(ggplot2,aes)
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importFrom(ggplot2,aes_string)
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importFrom(ggplot2,coord_flip)
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importFrom(ggplot2,element_blank)
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importFrom(ggplot2,element_line)
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importFrom(ggplot2,element_text)
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importFrom(ggplot2,facet_wrap)
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importFrom(ggplot2,geom_bar)
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importFrom(ggplot2,geom_point)
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importFrom(ggplot2,geom_text)
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importFrom(ggplot2,ggplot)
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importFrom(ggplot2,ggtitle)
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importFrom(ggplot2,guide_legend)
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importFrom(ggplot2,guides)
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importFrom(ggplot2,labs)
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importFrom(ggplot2,position_stack)
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importFrom(ggplot2,rel)
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importFrom(ggplot2,scale_colour_gradient2)
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importFrom(ggplot2,scale_colour_viridis_c)
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importFrom(ggplot2,scale_fill_gradient2)
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importFrom(ggplot2,scale_fill_manual)
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importFrom(ggplot2,scale_y_continuous)
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importFrom(ggplot2,theme)
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importFrom(ggplot2,theme_classic)
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importFrom(ggplot2,theme_void)
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importFrom(magrittr,"%>%")
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importFrom(networkD3,sankeyNetwork)
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importFrom(patchwork,plot_layout)
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importFrom(patchwork,wrap_plots)
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importFrom(purrr,map)
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importFrom(purrr,map2)
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importFrom(purrr,map_dbl)
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importFrom(rlang,.data)
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importFrom(scPred,scPredict)
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importFrom(tibble,column_to_rownames)
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importFrom(tibble,rownames_to_column)

‎NEWS.md

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# DevKidCC 0.2.0
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# DevKidCC 0.1.6
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‎R/DevKidCC-package.R

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#' @keywords internal
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"_PACKAGE"
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#' @importFrom ggplot2 aes coord_flip element_text facet_wrap geom_bar geom_text ggplot ggtitle position_stack theme rel scale_fill_manual scale_colour_gradient2 scale_fill_gradient2
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#' @importFrom ggplot2 aes_string geom_point element_blank scale_colour_viridis_c element_line labs theme_classic theme_void guides guide_legend scale_y_continuous
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#' @importFrom dplyr bind_rows filter mutate left_join transmute inner_join select bind_cols inner_join arrange
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#' @importFrom tibble rownames_to_column column_to_rownames
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#' @importFrom scPred scPredict
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#' @importFrom patchwork plot_layout wrap_plots
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#' @importFrom Seurat DimPlot NoLegend FetchData CellsByIdentities Idents CreateSeuratObject DefaultAssay<- NormalizeData MinMax Idents<-
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#' @importFrom magrittr %>%
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#' @importFrom purrr map map2 map_dbl
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#' @importFrom rlang .data
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#'
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# The following block is used by usethis to automatically manage
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# roxygen namespace tags. Modify with care!
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## usethis namespace: start
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## usethis namespace: end
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NULL

‎R/DevKidCC.R

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#' DevKidCC
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#'
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#' @param seurat seurat object
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#' @param threshold minimum value for an identity to be assigned within the model call, default is 0.7
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#' @param max.iter Can ask scPred to run this number of integrations, set to 0 be default
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#'
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#' @return
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#' @export
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#'
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#' @examples
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DevKidCC <- function(seurat, threshold = 0.7, max.iter = 0) {
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if (("DKCC" %in% colnames(seurat@meta.data)) == FALSE){
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seurat@misc$old.meta <- seurat@meta.data
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} else {
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seurat@meta.data <- seurat@misc$old.meta
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}
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md <- seurat@meta.data
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#if ("cell" %in% colnames(md)) {
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# [email protected] %>% rownames_to_column("celltemp") %>% select(-cell) %>% column_to_rownames("celltemp")
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#}
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old.seurat <- seurat
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seurat <- CreateSeuratObject(old.seurat@assays$RNA@counts, meta.data = md)
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DefaultAssay(seurat) <- "RNA"
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seurat <- NormalizeData(seurat)
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kcc <- data.frame()
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seurat <- scPred::scPredict(seurat, reference = model1.all, threshold = threshold, max.iter.harmony=max.iter)
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seurat$LineageID <- seurat$scpred_prediction
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kcc <- seurat@meta.data %>% rownames_to_column("cell") %>% filter(LineageID %in% c("unassigned", "NPC", "Endothelial")) %>% transmute(cell = cell, kcc = LineageID)
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#kcc$kcc <- gsub("unassigned", "OffTarget", kcc$kcc)
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t1 <- seurat[, seurat$LineageID%in%c("unassigned", "NPC", "Endothelial")]
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#[email protected] <- [email protected][, 1:ncol(md)]
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if (nrow(seurat@meta.data %>% filter(LineageID=="Nephron")) > 2){
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nephronid <- scPred::scPredict(seurat[, seurat$LineageID=="Nephron"], reference = model2.nephron,
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threshold = 0.0, max.iter.harmony=max.iter)
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nephronid$NephronID <- nephronid$scpred_prediction
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nephronid$NephronID <- gsub("unassigned", "Nephron_NC", nephronid$NephronID)
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kcc <- bind_rows(kcc,
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nephronid@meta.data %>% rownames_to_column("cell") %>% filter(NephronID %in% c("CellCycle", "EarlyNephron", "unassigned")) %>% transmute(cell = cell, kcc = NephronID))
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if (nrow(nephronid@meta.data %>% filter(NephronID %in% c("CellCycle", "EarlyNephron", "unassigned"))) > 0){
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neph <- nephronid[, nephronid$NephronID %in% c("CellCycle", "EarlyNephron", "unassigned")]
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t1 <- merge(t1, neph)
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}
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if (nrow(nephronid@meta.data %>% filter(NephronID=="ProximalNephron")) > 2){
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proximalid <- scPred::scPredict(nephronid[, nephronid$NephronID =="ProximalNephron"],
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reference = model3.pn, threshold = 0.0, max.iter.harmony=max.iter)
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proximalid$SegmentID <- proximalid$scpred_prediction
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kcc <- bind_rows(kcc,
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proximalid@meta.data %>% rownames_to_column("cell") %>% transmute(cell = cell, kcc = SegmentID))
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t1 <- merge(t1, proximalid)
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}
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if (nrow(nephronid@meta.data %>% filter(NephronID=="DistalNephron")) > 2){
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distalid <- scPred::scPredict(nephronid[, nephronid$NephronID =="DistalNephron"],
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reference = model3.dn, threshold = 0.0, max.iter.harmony=max.iter)
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distalid$SegmentID <- distalid$scpred_prediction
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kcc <- bind_rows(kcc,
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distalid@meta.data %>% rownames_to_column("cell") %>% transmute(cell = cell, kcc = SegmentID))
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t1 <- merge(t1, distalid)
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}
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if (nrow(nephronid@meta.data %>% filter(NephronID=="RenalCorpuscle")) > 2){
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rcid <- scPred::scPredict(nephronid[, nephronid$NephronID =="RenalCorpuscle"], reference = model3.rc,
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threshold = 0.0, max.iter.harmony=max.iter)
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rcid$SegmentID <- rcid$scpred_prediction
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kcc <- bind_rows(kcc,
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rcid@meta.data %>% rownames_to_column("cell") %>% transmute(cell = cell, kcc = SegmentID))
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t1 <- merge(t1, rcid)
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}
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}
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if (nrow(seurat@meta.data %>% filter(LineageID=="Stroma")) > 2){
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stromaid <- scPred::scPredict(seurat[, seurat$LineageID=="Stroma"], reference = model2.stroma,
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threshold = 0.0, max.iter.harmony=max.iter)
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stromaid$StromaID<- stromaid$scpred_prediction
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stromaid$StromaID <- gsub(pattern = "unassigned", replacement = "Stroma_NC", x = stromaid$StromaID)
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kcc <- bind_rows(kcc,
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stromaid@meta.data %>% rownames_to_column("cell") %>% transmute(cell = cell, kcc = StromaID))
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t1 <- merge(t1, stromaid)
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}
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if (nrow(seurat@meta.data %>% filter(LineageID=="UrEp")) > 2){
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urepid <- scPred::scPredict(seurat[, seurat$LineageID=="UrEp"], reference = model2.urep,
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threshold = 0.0, max.iter.harmony = max.iter)
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urepid$UrEpID <- urepid$scpred_prediction
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urepid$UrEpID <- gsub(pattern = "unassigned", replacement = "UrEp", x = urepid$UrEpID)
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kcc <- bind_rows(kcc,
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urepid@meta.data %>% rownames_to_column("cell") %>% transmute(cell = cell, kcc = UrEpID))
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t1 <- merge(t1, urepid)
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}
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if ("NephronID" %in% colnames(t1@meta.data)){
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t1$NephronID <- gsub("CellCycle", "CC", t1$NephronID)
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t1$NephronID <- gsub("EarlyNephron", "EN", t1$NephronID)
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t1$NephronID <- gsub("DistalNephron", "DN", t1$NephronID)
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t1$NephronID <- gsub("ProximalNephron", "PN", t1$NephronID)
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t1$NephronID <- gsub("RenalCorpuscle", "RC", t1$NephronID)
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t1$NephronID <- factor(t1$NephronID, levels = c("unassigned", "CC", "EN", "DN", "PN", "RC"))
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}
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kcc$kcc <- gsub("CellCycle", "CC", kcc$kcc)
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kcc$kcc <- gsub("EarlyNephron", "EN", kcc$kcc)
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levels <- c("unassigned", "NPC", "Endothelial",
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"CC", "EN", "DN", "PN", "RC",
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"SPC", "Cortex", "Medullary", "Mesangial", "Stroma_NC",
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"Tip", "OuterStalk", "InnerStalk",
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"EDT_EMT", "DT", "LOH", "EPT", "PT", "PEC", "EPod", "Pod", "Nephron_NC")
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t1$DKCC <- factor(kcc$kcc, levels = levels[levels %in% unique(kcc$kcc)])
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t1$LineageID <- factor(t1$LineageID, levels = c("unassigned", "Endothelial", "Stroma", "NPC", "Nephron", "UrEp"))
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#
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old.seurat@meta.data <- left_join(md %>% rownames_to_column("cell") %>% select(cell),
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t1@meta.data %>% rownames_to_column("cell"), by = "cell") %>% column_to_rownames("cell")
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return(old.seurat)
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}
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