Description Usage Arguments Value Author(s) References Examples
Using the idea of least trimmed square to detect and remove outliers before estimating the coefficients. A robust method for gene-expression deconvolution.
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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. |
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.
Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie
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>
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