| Title: | Robust prediction of clinical outcomes using cytometry data without cell gating |
|---|---|
| Description: | This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. |
| Authors: | Zicheng Hu |
| Maintainer: | Zicheng Hu <[email protected]> |
| License: | GPL-2 |
| Version: | 1.27.0 |
| Built: | 2024-10-30 05:20:01 UTC |
| Source: | https://github.com/bioc/CytoDx |
A function that builds the CytoDx model.
CytoDx.fit(x, y, xSample, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), type1 = "response", type2 = "response", parallelCore = 1, reg = FALSE, ...)CytoDx.fit(x, y, xSample, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), type1 = "response", type2 = "response", parallelCore = 1, reg = FALSE, ...)
x |
The marker profile of cells pooled from all samples. Each row is a cell, each column is a marker. |
y |
The clinical outcomes associated with samples to which cells belong. Length must be equal to nrow(x). For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class). For family="multinomial", can be a nc>=2 level factor, or a matrix with nc columns of counts or proportions. For either "binomial" or "multinomial", if y is presented as a vector, it will be coerced into a factor. For family="cox", y should be a two-column matrix with columns named 'time' and 'status'. The latter is a binary variable, with '1' indicating death, and '0' indicating right censored. The function Surv() in package survival produces such a matrix. For family="mgaussian", y is a matrix of quantitative responses. |
xSample |
A vector specifying which sample each cell belongs to. Length must equal to nrow(x). |
family |
Response type. Must be one of the following: "gaussian","binomial","poisson","multinomial","cox","mgaussian" |
type1 |
Type of first level prediction. Type of prediction required. Type "link" gives the linear predictors for "binomial", "multinomial", "poisson" or "cox" models; for "gaussian" models it gives the fitted values. Type "response" gives the fitted probabilities for "binomial" or "multinomial", fitted mean for "poisson" and the fitted relative-risk for "cox"; for "gaussian" type "response" is equivalent to type "link". |
type2 |
Type of second level prediction. |
parallelCore |
The number of core to be used. Only used when reg is TRUE. |
reg |
If elestic net regularization will be used. |
... |
Other parameters to be passed into the glmnet or the cv.glmnet function in the glmnet package. |
Returns a list. train.Data.cell contains the trainig data and the predicted y for the training data at the cell level. model.cell contains the cell stage statistical model. Data.sample contains the trainig data and the predicted y for the training data at the sample level. model.sample contains the sample stage statistical model. family specifies the regression type. method specifies the type of learning method. type.cell is the type of cell level prediction. type.sample is the type of sample level prediction.
# Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) # Read cytometry files using fcs2DF function train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time")) # build the model fit <- CytoDx.fit(x=as.matrix(train_data[,1:7]), y=train_data$y, xSample = train_data$xSample, reg=FALSE, family="binomial") # check accuracy for training data pred <- CytoDx.pred(fit, xNew=as.matrix(train_data[,1:7]), xSampleNew=train_data$xSample) boxplot(pred$xNew.Pred.sample$y.Pred.s0~ fcs_info$Label)# Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) # Read cytometry files using fcs2DF function train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time")) # build the model fit <- CytoDx.fit(x=as.matrix(train_data[,1:7]), y=train_data$y, xSample = train_data$xSample, reg=FALSE, family="binomial") # check accuracy for training data pred <- CytoDx.pred(fit, xNew=as.matrix(train_data[,1:7]), xSampleNew=train_data$xSample) boxplot(pred$xNew.Pred.sample$y.Pred.s0~ fcs_info$Label)
A function that makes prediction using the CytoDx model.
CytoDx.pred(fit, xNew, xSampleNew)CytoDx.pred(fit, xNew, xSampleNew)
fit |
The two stage statistical model. Must be the object returned by CytoDx.fit. |
xNew |
The marker profile of cells pooled from all new samples. Each row is a cell, each column is a marker. |
xSampleNew |
A vector specifying which sample each cell belongs to. Length must equal to nrow(xNew). |
Returns a list. xNew.Pred1 contains the predicted y for the new data at the cell level. xNew.Pred2 contains the predicted y for the new data at the sample level.
# Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time")) # build the model fit <- CytoDx.fit(x=as.matrix(train_data[,1:7]), y=train_data$y, xSample = train_data$xSample, reg=FALSE, family="binomial") # check accuracy for training data pred <- CytoDx.pred(fit, xNew=as.matrix(train_data[,1:7]), xSampleNew=train_data$xSample) boxplot(pred$xNew.Pred.sample$y.Pred.s0~ fcs_info$Label)# Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time")) # build the model fit <- CytoDx.fit(x=as.matrix(train_data[,1:7]), y=train_data$y, xSample = train_data$xSample, reg=FALSE, family="binomial") # check accuracy for training data pred <- CytoDx.pred(fit, xNew=as.matrix(train_data[,1:7]), xSampleNew=train_data$xSample) boxplot(pred$xNew.Pred.sample$y.Pred.s0~ fcs_info$Label)
A function that convert fcs files to a data frame.
fcs2DF(fcsFiles, y = NULL, assay = c("FCM", "CyTOF"), b = 1/200, fileSampleSize = 5000, compFiles = NULL, nameDict = NULL, excludeTransformParameters = c("FSC-A", "FSC-W", "FSC-H", "Time", "Cell_length"))fcs2DF(fcsFiles, y = NULL, assay = c("FCM", "CyTOF"), b = 1/200, fileSampleSize = 5000, compFiles = NULL, nameDict = NULL, excludeTransformParameters = c("FSC-A", "FSC-W", "FSC-H", "Time", "Cell_length"))
fcsFiles |
A vector specifying the location of fcs files (relative to working directory). |
y |
A vector containing the clinical outcome of each sample. Must have the same length as fcsFiles. Null for testing data. |
assay |
Either "FCM" or "CyTOF" to indicate the type of cytometry data. |
b |
A positive number used to specify the arcsinh transformation. f(x) = asinh (b*x) where x is the original value and f(x) is the value after transformation. The suggested value is 1/150 for flow cytometry (FCM) data and 1/8 for CyTOF data. |
fileSampleSize |
An integer specifying the number of events sampled from each fcs file. If NULL, all the events will be pre-processed and wrote out to the new fcs files. |
compFiles |
A vector specifying the paths of user supplied compensation matrix for each fcs file. The matrix must be stored in csv files. |
nameDict |
A vector used to change marker names.Each element in the vector is the prefered name of a marker. The name of each element is the marker name used in the fcs file. For example, a vector c("CD8b"="CD8","cd8"="CD8") will change "CD8b" and "cd8" into "CD8", making annotations more consistent. |
excludeTransformParameters |
A vector specifying the name of parameters not to be transformed (left at linear scale). |
Returns a data frame containing the preprocessed cytometry data. Cells from different fcs files are combined into one flow frame. A new column, xSample, is introduced to indicate the origin of each cell. The data frame also includes the clinical outcome y.
# Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) # Read cytometry files using fcs2DF function train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time"))# Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) # Read cytometry files using fcs2DF function train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time"))
A function that calulate mean or take unique elements of a vector.
meanUnique(x)meanUnique(x)
x |
a vector |
If x is numeric, returns the mean. Otherwise, returns the unique elements of x.
x <- 1:5 meanUnique(x) x=c("a","a","b") meanUnique(x)x <- 1:5 meanUnique(x) x=c("a","a","b") meanUnique(x)
A function that performs the rank transformation of the data.
pRank(x, xSample)pRank(x, xSample)
x |
A data frame containing the pooled data from fcs files. Each row is a cell, each column is a marker. |
xSample |
A vector specifying which sample each cell belongs to. Length must equal to nrow(x). |
Returns data frame containing rank transformed data.
x <- pRank(x=iris[,1:4],xSample=iris$Species)x <- pRank(x=iris[,1:4],xSample=iris$Species)
A function that performs the Percentile rank transformation of a vector
rank.ub.average(x)rank.ub.average(x)
x |
A numeric vector. |
Returns the percentile rank of each element.
rank.ub.average(1:10)rank.ub.average(1:10)
A function that convert a flowSet to a data frame.
set2DF(flowSet, fcsFiles, y = NULL)set2DF(flowSet, fcsFiles, y = NULL)
flowSet |
A flowSet object |
fcsFiles |
A vector containing the name of each fcs file included in flowSet. |
y |
The clinical outcome each fcs file associated with. Null for testing data. |
Returns a data frame containing the cytometry data. Cells from different fcs files are combined into one flow frame. A new column, xSample, is introduced to indicate the origin of each cell. The data frame also includes the clinical outcome y.
library(flowCore) # Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) fSet <- read.flowSet(fn) df <- set2DF(flowSet=fSet,fcsFiles=fn,y = fcs_info$Label)library(flowCore) # Find the table containing fcs file names in CytoDx package path <- system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) fSet <- read.flowSet(fn) df <- set2DF(flowSet=fSet,fcsFiles=fn,y = fcs_info$Label)
A function that sse decision tree to find a group of cells that are associated with clinical outcome.
treeGate(P, x, ...)treeGate(P, x, ...)
P |
The predicted association of each cell with a clinical outcome. |
x |
The marker profile of each cell. Each row is a cell, each column is a marker. Must have length(P) rows. |
... |
Other parameters to be passed into the rpart function |
Returns a object created by rpart function. Also plots a graph of decision tree.
# Find the table containing fcs file names in CytoDx package path=system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) # Read cytometry files using fcs2DF function train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time")) # build the model fit <- CytoDx.fit(x=as.matrix(train_data[,1:7]), y=train_data$y, xSample = train_data$xSample, reg=FALSE, family="binomial") # check accuracy for training data pred <- CytoDx.pred(fit, xNew=as.matrix(train_data[,1:7]), xSampleNew=train_data$xSample) boxplot(pred$xNew.Pred.sample$y.Pred.s0~ fcs_info$Label) # Find the associated population using treeGate TG <- treeGate(P = fit$train.Data.cell$y.Pred.s0, x= train_data[,1:7])# Find the table containing fcs file names in CytoDx package path=system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) # Read cytometry files using fcs2DF function train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time")) # build the model fit <- CytoDx.fit(x=as.matrix(train_data[,1:7]), y=train_data$y, xSample = train_data$xSample, reg=FALSE, family="binomial") # check accuracy for training data pred <- CytoDx.pred(fit, xNew=as.matrix(train_data[,1:7]), xSampleNew=train_data$xSample) boxplot(pred$xNew.Pred.sample$y.Pred.s0~ fcs_info$Label) # Find the associated population using treeGate TG <- treeGate(P = fit$train.Data.cell$y.Pred.s0, x= train_data[,1:7])