CytoDx.fit: Build the CytoDx model

In hzc363/CytoDx: Robust prediction of clinical outcomes using cytometry data without cell gating

Description

A function that builds the CytoDx model.

Usage

CytoDx.fit(x, y, xSample, family = c("gaussian", "binomial", "poisson",
  "multinomial", "cox", "mgaussian"), type1 = "response",
  type2 = "response", parallelCore = 1, reg = FALSE, ...)

Arguments

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.

Value

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.

Examples

# 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)

hzc363/CytoDx documentation built on May 8, 2019, 11:56 p.m.