createCYT {CytoTree} | R Documentation |
This function is about how to build an CYT object. An CYT object is the base for the whole analysizing workflow of flow and mass cytometry data.
createCYT( raw.data, markers, meta.data, batch = NULL, batch.correct = FALSE, normalization.method = "none", verbose = FALSE, ... )
raw.data |
matrix. Raw data read from FCS file after perform preprocessing. |
markers |
vector. Detailed marker information in the gate of flow cytometer. |
meta.data |
data.frame. Raw metadata of each cell. Columns "cell" and "stage" are required. |
batch |
vector. Batch covariate (only one batch allowed).
Method to correct batch effect
function is refered to |
batch.correct |
logical. Whether to correct batch effect. If TRUE, batch must be provided. |
normalization.method |
character. Normalization and transformation
method. Whether to normalize and log transformed of raw.data.
In CytoTree workflow, it's better to perform transformation of
FCS data using |
verbose |
logical. Whether to print calculation progress. |
... |
paramters pass to |
An CYT object with raw.data and markers and meta.data
# Read fcs files fcs.path <- system.file("extdata", package = "CytoTree") fcs.files <- list.files(fcs.path, pattern = '.FCS$', full = TRUE) fcs.data <- runExprsMerge(fcs.files, comp = FALSE, transformMethod = "none") # Refine colnames of fcs data recol <- c(`FITC-A<CD43>` = "CD43", `APC-A<CD34>` = "CD34", `BV421-A<CD90>` = "CD90", `BV510-A<CD45RA>` = "CD45RA", `BV605-A<CD31>` = "CD31", `BV650-A<CD49f>` = "CD49f", `BV 735-A<CD73>` = "CD73", `BV786-A<CD45>` = "CD45", `PE-A<FLK1>` = "FLK1", `PE-Cy7-A<CD38>` = "CD38") colnames(fcs.data)[match(names(recol), colnames(fcs.data))] = recol fcs.data <- fcs.data[, recol] day.list <- c("D0", "D2", "D4", "D6", "D8", "D10") meta.data <- data.frame(cell = rownames(fcs.data), stage = gsub(".FCS.+", "", rownames(fcs.data) ) ) meta.data$stage <- factor(as.character(meta.data$stage), levels = day.list) markers <- c("CD43","CD34","CD90","CD45RA","CD31","CD49f","CD73","CD45","FLK1","CD38") # Build the CYT object cyt <- createCYT(raw.data = fcs.data, markers = markers, meta.data = meta.data, normalization.method = "log", verbose = TRUE) # See information cyt