LASSO_plus {csmpv}

LASSO_plus Variable Selection and Modeling

Description

This function performs variable selection using the LASSO_plus algorithm and builds a model afterward.

Usage

LASSO_plus(
  data = NULL,
  standardization = FALSE,
  columnWise = TRUE,
  biomks = NULL,
  outcomeType = c("binary", "continuous", "time-to-event"),
  Y = NULL,
  time = NULL,
  event = NULL,
  topN = 10,
  outfile = "nameWithPath",
  height = 6
)

Arguments

data

A data matrix or a data frame, samples are in rows, and features/traits are in columns.

standardization

A logic variable to indicate if standardization is needed before variable selection, the default is FALSE.

columnWise

A logic variable to indicate if column wise or row wise normalization is needed, the default is TRUE, which is to do column-wise normalization. This is only meaningful when "standardization" is TRUE.

biomks

A vector of potential biomarkers for variable selection, they should be a subset of "data" column names.

outcomeType

Outcome variable type. There are three choices: "binary" (default), "continuous", and "time-to-event".

Y

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

time

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

event

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

topN

An integer indicating the desired number of variables to be selected.

outfile

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

height

An integer to indicate the forest plot height in inches

Details

The LASSO_plus algorithm combines LASSO, single variable regression, and stepwise regression to select variables associated with an outcome variable in a given dataset. The outcome variable can be binary, continuous, or time-to-event. After variable selection, a model is built using common R functions such as lm, glm, and coxph, depending on the outcome type.

Value

A list is returned:

fit

A model with selected variables for the given outcome variable

outplot

A forest plot

Author(s)

Aixiang Jiang

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22, doi:10.18637/jss.v033.i01.

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, doi:10.18637/jss.v039.i05.

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>.

Aoki T, Jiang A, Xu A et al.,(2023) Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes in Relapsed/Refractory Hodgkin Lymphoma. J Clin Oncol. 2023 Dec 19:JCO2301115. doi: 10.1200/JCO.23.01115. Epub ahead of print. PMID: 38113419.

Examples

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

# The 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")
# The function can work with three different outcome types. 
# Here, we use binary as an example:
bfit = LASSO_plus(data = tdat, biomks = Xvars, Y = "DZsig", topN = 5,
                  outfile = paste0(temp_dir, "/binaryLASSO_plus"))
# You might save the files to the directory you want.

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