FARDEEP (version 1.0.1)

fardeep: Using the idea of least trimmed square to detect and remove outliers before estimating the coefficients. A robust method for gene-expression deconvolution.

Description

Using the idea of least trimmed square to detect and remove outliers before estimating the coefficients. A robust method for gene-expression deconvolution.

Usage

fardeep(X, Y, alpha1 = 0.1, alpha2 = 1.5, up = 10, low = 1,
  nn = TRUE, intercept = TRUE, lognorm = TRUE, permn = 100,
  QN = FALSE)

Arguments

X

input matrix of predictors with n rows and p columns.

Y

input vector of dependent variable.

alpha1

parameter used to adjust the upper bound of outliers. Take value from 0 to 1, default 0.1.

alpha2

parameter used to adjust the lower bound of outliers. Take value larger than 1, default 1.5.

up

upper bound of parameter k in function alts, default 10.

low

lower bound of parameter k in function alts, default 1.

nn

whether coefficients are non-negative,default TRUE.

intercept

whether intercept is included in model, default TRUE.

lognorm

whether noise is log-normal distributed, default TRUE.

permn

the number of permutation to get the p-values, default TRUE.

QN

whether perform quantile normalization, default TRUE.

Value

abs.beta: estimation of abosulute abundance of cells (TIL subset scores).

relative.beta: estimation of relative proportions by normalizing abs.beta to 1.

pval: statistical significance for the deconvolution result.

k.value: tuned paprameter by modified BIC.

References

Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie. Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares. <doi:10.1101/358366>

Examples

# NOT RUN {
library(FARDEEP)
data(LM22)
data(mixture)
# toy examples
result = fardeep(LM22, mixture[, 1:2], permn = 0)
# }
# NOT RUN {
result = fardeep(LM22, mixture)
coef = result$abs.beta
# }