csmpv (version 1.0.5)

confirmVars: Biomarker Confirmation Function

Description

This function confirms and validates known biomarkers in a given dataset.

Usage

confirmVars(
  data = NULL,
  standardization = FALSE,
  columnWise = TRUE,
  biomks = NULL,
  outcomeType = c("binary", "continuous", "time-to-event"),
  Y = NULL,
  time = NULL,
  event = NULL,
  outfile = "nameWithPath",
  timeUnits = "years"
)

Value

A list containing:

fit

A model with selected variables for the given outcome variable.

allplot

A list of plots generated during the confirmation/validation process.

There might be extra plots in the list for time-to-event outcome

Arguments

data

A data matrix or data frame with samples in rows and features/traits (including outcome and biomarkers) in columns.

standardization

Logical; indicates if standardization is needed before biomarker confirmation/validation. Default is FALSE.

columnWise

Logical; indicates if column-wise or row-wise normalization is needed for standardization. Default is TRUE.

biomks

A vector of biomarker names to confirm/validate. Subset of column names in the data parameter.

outcomeType

The type of the outcome variable. It has three choices: "binary" (default), "continuous", and "time-to-event".

Y

The outcome variable name when the outcome type is either "binary" or "continuous".

time

The time variable name when the outcome type is "time-to-event".

event

The event variable name when the outcome type is "time-to-event".

outfile

A string representing the output file, including the path if necessary, but without the file type extension.

timeUnits

A character vector specifying the units in which time is measured for the survival data. Default is "years".

Author

Aixiang Jiang

Details

Use this function to assess whether individual variables or groups of variables have an impact on an outcome variable within a dataset. The outcome variable can be binary, continuous, or time-to-event. Note that this function is not intended for model confirmation, as it doesn't incorporate coefficients from previous research.

References

Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Therneau, T., Grambsch, P., Modeling Survival Data: Extending the Cox Model. Springer-Verlag, 2000.

Kassambara A, Kosinski M, Biecek P (2021). survminer: Drawing Survival Curves using 'ggplot2', R package version 0.4.9, <https://CRAN.R-project.org/package=survminer>.

Examples

# Load in data sets:
data("datlist", package = "csmpv")
tdat = datlist$training

# The confirmVars function saves files locally. You can define your own temporary directory. 
# If not, tempdir() can be used to get the system's temporary directory.
temp_dir = tempdir()

# As an example, let's define Xvars, which will be used later:
Xvars = c("highIPI","B.Symptoms", "MYC.IHC", "BCL2.IHC", "CD10.IHC", "BCL6.IHC")

# confirmVars can work with three different outcome types. 
# Here, we use binary as an example:
bconfirm = confirmVars(data = tdat, biomks = Xvars, Y = "DZsig",
                        outfile = paste0(temp_dir, "/confirmBinary"))
# You might save the files to the directory you want.

# To delete the temp_dir, use the following:
unlink(temp_dir)