| Title: | Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares |
| Version: | 1.0.1 |
| Description: | Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>. |
| Depends: | R (≥ 3.3.0) |
| License: | MIT + file LICENSE |
| Imports: | nnls(≥ 1.4), stats, preprocessCore |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2019-04-18 06:51:24 UTC; yuning |
| Author: | Yuning Hao [aut], Ming Yan [aut], Blake R. Heath [aut], Yu L. Lei [aut], Yuying Xie [aut, cre] |
| Maintainer: | Yuying Xie <xyy@egr.msu.edu> |
| Repository: | CRAN |
| Date/Publication: | 2019-04-24 15:10:03 UTC |
Siganature matrix
Description
A dataset containing 547 genes and 22 TILs.
Usage
LM22
Format
A data frame with 547 rows and 22 variables:
- B.cells.naive
naive B cells
- B.cells.memory
memory B cells
- Plasma.cells
Plasma cells
- T.cells.CD8
CD8 T cells
- T.cells.CD4.naive
naive CD4 T cells
- T.cells.CD4.memory.resting
resting memory CD4 T cells
- T.cells.CD4.memory.activated
activated memory CD4 T cells
- T.cells.follicular.helper
follicular helper T cells
- T.cells.regulatory.Tregs.
regulatory T cells
- T.cells.gamma.delta
gamma delta T cells
- NK.cells.resting
resting natural killer cells
- NK.cells.activated
activated natural killer cells
- Monocytes
monocytes
- Macrophages.M0
M0 macrophages
- Macrophages.M1
M1 macrophages
- Macrophages.M2
M2 macrophages
- Dendritic.cells.resting
resting dendritic cells
- Dendritic.cells.activated
activated dendritic cells
- Mast.cells.resting
resting mast cells
- Mast.cells.activated
activated mast cells
- Eosinophils
eosinophils
- Neutrophils
neutrophils
References
Aaron M. Newman, Chih Long Liu, Michael R. Green, Andrew J. Gentles, Weiguo Feng, Yue Xu, Chuong D. Hoang, Maximilian Diehn and Ash A. Alizadeh. Robust enumeration of cell subsets from tissue expression profiles.
Using the basic idea of least trimmed square to detect and remove outliers before estimating the coefficients. Adaptive least trimmed square.
Description
Using the basic idea of least trimmed square to detect and remove outliers before estimating the coefficients. Adaptive least trimmed square.
Usage
alts(x, y, alpha1 = 0.1, alpha2 = 1.5, k = 6, nn = TRUE,
intercept = TRUE)
Arguments
x |
input matrix of predictors with n rows and p columns. |
y |
input vector of dependent variable with length n. |
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. |
k |
parameter used to determine the boundary of outliers in the following step of algorithm. Take value from 1 to 10, default 6. |
nn |
whether coefficients are non-negative,default TRUE. |
intercept |
whether intercept is included in model, default TRUE. |
Value
beta: estimation of coefficients.
number_outlier: number of outliers.
outlier_detect: index of detected outliers.
X.new: good observed points for independent variables.
Y.new: good observed points for dependent variables.
k: modified k (if the input value is not appropriate).
Author(s)
Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie
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
library(FARDEEP)
samp = sample.sim(n = 500, p = 20, sig = 1, a1 = 0.1, a2 = 0.2, nn = TRUE, intercept = TRUE)
result = alts(samp$x, samp$y, alpha1 = 0.1, alpha2 = 1.5, k = 6, nn = TRUE, intercept = TRUE)
coef = result$beta
Using the idea of least trimmed square to detect and remove outliers before estimating the coefficients. A robust method for gene-expression deconvolution.
Description
Using the idea of least trimmed square to detect and remove outliers before estimating the coefficients. A robust method for gene-expression deconvolution.
Usage
fardeep(X, Y, alpha1 = 0.1, alpha2 = 1.5, up = 10, low = 1,
nn = TRUE, intercept = TRUE, lognorm = TRUE, permn = 100,
QN = FALSE)
Arguments
X |
input matrix of predictors with n rows and p columns. |
Y |
input vector of dependent variable. |
alpha1 |
parameter used to adjust the upper bound of outliers. Take value from 0 to 1, default 0.1. |
alpha2 |
parameter used to adjust the lower bound of outliers. Take value larger than 1, default 1.5. |
up |
upper bound of parameter k in function alts, default 10. |
low |
lower bound of parameter k in function alts, default 1. |
nn |
whether coefficients are non-negative,default TRUE. |
intercept |
whether intercept is included in model, default TRUE. |
lognorm |
whether noise is log-normal distributed, default TRUE. |
permn |
the number of permutation to get the p-values, default TRUE. |
QN |
whether perform quantile normalization, default TRUE. |
Value
abs.beta: estimation of abosulute abundance of cells (TIL subset scores).
relative.beta: estimation of relative proportions by normalizing abs.beta to 1.
pval: statistical significance for the deconvolution result.
k.value: tuned paprameter by modified BIC.
Author(s)
Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie
References
Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie. Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares. <doi:10.1101/358366>
Examples
library(FARDEEP)
data(LM22)
data(mixture)
# toy examples
result = fardeep(LM22, mixture[, 1:2], permn = 0)
result = fardeep(LM22, mixture)
coef = result$abs.beta
Gene-expression data from 14 follicular lymphoma patients
Description
This gene-expression dataset consists of surgical lymph node biopsies of 14 follicular lymphoma patients with 19416 genes. It is available on Gene Expression Omnibus (GEO) with accession number GSE65135. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65135.
Usage
mixture
Format
A data frame with 19416 rows and 14 variables:
- GSM1587831
FL lymph node biopsy, untreated, 1063
- GSM1587832
FL lymph node biopsy, untreated, 1080
- GSM1587833
FL lymph node biopsy, untreated, 575
- GSM1587834
FL lymph node biopsy, untreated, 581
- GSM1587835
FL lymph node biopsy, untreated, 598
- GSM1587836
FL lymph node biopsy, untreated, 639
- GSM1587837
FL lymph node biopsy, untreated, 664
- GSM1587838
FL lymph node biopsy, untreated, 666
- GSM1587839
FL lymph node biopsy, untreated, 695
- GSM1587840
FL lymph node biopsy, untreated, 706
- GSM1587841
FL lymph node biopsy, untreated, 726
- GSM1587842
FL lymph node biopsy, untreated, 731
- GSM1587843
FL lymph node biopsy, untreated, 944
- GSM1587844
FL lymph node biopsy, untreated, 959
References
Aaron M. Newman, Chih Long Liu, Michael R. Green, Andrew J. Gentles, Weiguo Feng, Yue Xu, Chuong D. Hoang, Maximilian Diehn and Ash A. Alizadeh. Robust enumeration of cell subsets from tissue expression profiles.
Generate random sample with different proportion of outliers and leverage points
Description
Generate random sample with different proportion of outliers and leverage points
Usage
sample.sim(n, p, sig, a1, a2, nn = TRUE, intercept = FALSE)
Arguments
n |
number of observations. |
p |
number of independent variables (predictors). |
sig |
variance of dependent variable. |
a1 |
proportion of outliers. |
a2 |
proportion of leverage points in outliers. |
nn |
whether coefficients are non-negative, default TRUE. |
intercept |
whether intercept is included in model, default TRUE. |
Value
y: vector of dependent variable.
x: matrix of predictors with n rows and p columns.
loc: index of added outliers.
beta: vector of coefficients.
Author(s)
Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie
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
library(FARDEEP)
samp = sample.sim(n = 500, p = 20, sig = 1, a1 = 0.1, a2 = 0.2, nn = TRUE, intercept = TRUE)
Tuning parameter k in function alts using Bayesian Information Criterion (BIC) with some adjustment.
Description
Tuning parameter k in function alts using Bayesian Information Criterion (BIC) with some adjustment.
Usage
tuningBIC(x, y, alpha1 = 0.1, alpha2 = 1.5, up = 10, low = 1,
nn = TRUE, intercept = TRUE, lognorm = TRUE)
Arguments
x |
input matrix of predictors with n rows and p columns. |
y |
input vector of dependent variable with length n. |
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. |
Value
k: tuning result of parameter k for function alts.
Author(s)
Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie
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
library(FARDEEP)
samp = sample.sim(n = 500, p = 20, sig = 1, a1 = 0.1, a2 = 0.2, nn = TRUE, intercept = TRUE)
k = tuningBIC(samp$x, samp$y, lognorm = FALSE)