Package 'FSDAM'

Select Biomarkers from the HVTN 505 Correlates Analysis

Description

See reference.

Usage

data("cc.505")

Format

A data frame with 189 observations on the following 27 variables.

ptid

a character vector

trt

a numeric vector

case

a numeric vector

control

a numeric vector

perprot

a numeric vector

last_uninfec_immun_vst

a numeric vector

racefull

a numeric vector

racefulltxt

a character vector

bmi

a numeric vector

bmicat

a numeric vector

bmicattxt

a character vector

earliest_pos_vst

a numeric vector

level

a character vector

matchlevel

a character vector

samplingfraction

a numeric vector

vst9subcohort

a numeric vector

HIVwk28preunbl

a numeric vector

age

a numeric vector

racecc

a character vector

bhvrisk

a numeric vector

BMI

a numeric vector

stratuminds_vaccs

a numeric vector

stratuminds

a numeric vector

cd4.env.poly

a numeric vector

cd8.env.poly

a numeric vector

mfounders

a numeric vector

wei

a numeric vector

References

Fong, Y, Xu, J. Multi-Stage Simultaneous Deep Autoencoder-based Monotone (MSS-DAM) Nonlinear Dimensionality Reduction Methods, Journal of Computational and Graphical Statistics, in press.

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FS-DAM NLDR

Description

Forward stepwise deep autoencoder-based monotone nonlinear dimension reduction.

Usage

fsdam(dat, opt_numCode = ncol(dat), opt_seed = 1, opt_model = "n", opt_gpu = 0, 
opt_k = 100, opt_nEpochs = 10000, 
opt_constr = c("newpenalization", "constrained", "none"),
 opt_tuneParam = 10, opt_penfun = "mean", opt_ortho = 1, opt_earlystop = "no", 
 verbose = FALSE)

## S3 method for class 'fsdam'
 plot(x, which=c("mse", "history", "decoder.func", "scatterplot"),
 k=NULL, dim.1=NULL, dim.2=NULL, col.predict=2, ...)

Arguments

dat	data frame.
opt_numCode	number of components to extract
opt_seed	seed for torch
opt_model	n for newpenalization
opt_gpu	zero-based index of gpu to be used among all gpus. If negative, then no gpu is used
opt_k	number of nodes in the coding/decoding layers
opt_nEpochs	number of epochs for training
opt_constr	constraint string
opt_tuneParam	tuning parameter for monotonicity penalty
opt_penfun	penalize sum or mean
opt_ortho	tuning parameter for orthogonality penalty
opt_earlystop	whether to stop early
verbose	verbose
x	fsdam object
which	which
k	the component to plot
dim.1	index of the first variable
dim.2	index of the second variable
col.predict	color of the predicted curve when which = scatterplot
...	plotting arguments

Details

If using the default reticulate python virtual env, may need to install some python packages via: reticulate::py_install("torch", pip = TRUE) reticulate::py_install("numpy", pip = TRUE) reticulate::py_install("pandas", pip = TRUE)

Alternatively, use a different python kernel by reticulate::use_python("/app/easybuild/software/Python/3.7.4-foss-2016b/bin/python")

It is recommended that dat is scaled before calling fsdam.

References

Fong, Y, Xu, J. Multi-Stage Simultaneous Deep Autoencoder-based Monotone (MSS-DAM) Nonlinear Dimensionality Reduction Methods, Journal of Computational and Graphical Statistics, in press.

Examples

## Not run: 
    
fit=fsdam(hvtn505tier1[1:100,-1], opt_numCode=2, verbose=TRUE)
fit
plot(fit,which="mse")
plot(fit,which="history")


## End(Not run)

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HVTN 505 Immune Correlates Tier 1 Dataset

Description

Contains eight immune response variables from the vaccine arm of the HVTN 505 trial.

Usage

data("hvtn505tier1")

Format

A data frame with 150 observations on the following 9 variables.

ptid

a character vector

CD8_ANYVRCENV_PolyfunctionalityScore_score

a numeric vector

IgGw28_env_mdw

a numeric vector

IgGw28_V1V2_mdw

a numeric vector

IgGw28_gp41_mdw

a numeric vector

ADCP1

a numeric vector

R2aConSgp140CFI

a numeric vector

IgAw28_env_mdw

a numeric vector

IgG3w28_env_mdw

a numeric vector

References

Fong, Y, Xu, J. Multi-Stage Simultaneous Deep Autoencoder-based Monotone (MSS-DAM) Nonlinear Dimensionality Reduction Methods, Journal of Computational and Graphical Statistics, in press.

Janes, H.E., Cohen, K.W., Frahm, N., De Rosa, S.C., Sanchez, B., Hural, J. et al (2017), Higher T-cell responses induced by DNA/rAd5 HIV-1 preventive vaccine are associated with lower HIV-1 infection risk in an efficacy trial, The Journal of infectious diseases, 215, 1376-1385.

Fong, Y., Shen, X., Ashley, V.C., Deal, A., Seaton, K.E., Yu, C. et al (2018), Vaccine-induced antibody responses modify the association between T-cell immune responses and HIV-1 infection risk in HVTN 505, The Journal of Infectious Diseases, 217, 1280–1288.

Neidich, S.D., Fong, Y., Shen, X., Ashley, V.C., Deal, A., Seaton, K.E. et al (2019), Antibody Fc-effector Functions and IgG3 Associates with Decreased HIV-1 Acquisition Risk, The Journal of Infectious Diseases, 129, 4838-4849.

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