| Type: | Package |
| Title: | Survival Mediation Analysis of High-Dimensional Proteogenomic Data |
| Version: | 0.0.5 |
| Description: | SMAHP (pronounced as SOO-MAP) is a novel multi-omics framework for causal mediation analysis of high-dimensional proteogenomic data with survival outcomes. The full methodological details can be found in our recent preprint by Ahn S et al. (2025) <doi:10.48550/arXiv.2503.08606>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | data.table, fdrtool, ncvreg, penAFT, survival, stats, glmnet |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Language: | en-US |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 3.5.0) |
| NeedsCompilation: | no |
| Packaged: | 2025-03-21 18:47:12 UTC; fuw01 |
| Author: | Seungjun Ahn 
[aut],
Weijia Fu [cre,
aut],
Zhigang Li [ctb] |
| Maintainer: | Weijia Fu <weijia.fu@mountsinai.org> |
| Repository: | CRAN |
| Date/Publication: | 2025-03-24 12:00:12 UTC |
SIS combo
Description
A function for SIS + SIS combo.
Usage
SIS_combo(X, M, C, time, status, p_adjust_option = "BH", thres = 0.05)
Arguments
X |
An n by p matrix of exposures. |
M |
An n by p matrix of mediators. |
C |
An n by p matrix of covariates. |
time |
A vector of survival time of samples. |
status |
A vector of status indicator: 0=alive, 1=dead. |
p_adjust_option |
The method for multiple correction. Option include q-value, holm, hochberg, hommel, bonferroni, BH, BY, and fdr. Default is BH. |
thres |
Threshold for determining significance. |
Value
A list which includes the final p-value matrix (p_final_matrix), adjusted p-value matrix (p_adjusted_matrix) and mediation-exposure matrix (p_med_matrix).
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
SIS_combo(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time,
status = surv_dat$status)
Main function
Description
An approach for survival mediation analysis of high-dimensional proteogenomic data.
Usage
SMAHP(
X,
M,
C,
time,
status,
model_option = "MCP",
SIS_thres = "n/log(n)",
p_adjust_option = "BH",
p_thres = 0.05
)
Arguments
X |
An n by p matrix of exposures. |
M |
An n by p matrix of mediators. |
C |
An n by p matrix of covariates. If there are no covariates, set C = NULL. |
time |
A vector of survival time of samples. |
status |
A vector of status indicator: 0=alive, 1=dead. |
model_option |
The penalization method used when selecting X for M. Options include MCP, elastic net and lasso. Default is MCP. |
SIS_thres |
SIS thresholds. Default is "n/log(n)". Other options include "n-1", "n/2log(n)", "2n/log(n)", "3n/log(n)". |
p_adjust_option |
The method for multiple correction. Option include q-value, holm, hochberg, hommel, bonferroni, BH, BY, and fdr. Default is BH. |
p_thres |
Threshold for determining significance. |
Value
A list with the following components:
p_final_matrix |
raw p-value matrix before adjustment |
p_adjusted_matrix |
adjusted p-value matrix |
p_med_matrix |
mediation-exposure matrix |
outcome_model |
coefficient estimates from outcome model |
med_results |
coefficient estimates from mediation model |
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
SMAHP(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time, status = surv_dat$status)
Penalized mediation and outcome models
Description
An auxiliary function which conducts variable selection of X for Y using penAFT, X for M using the selected penalized model and M for Y using penAFT.
Usage
StepOne(X, M, time, status, model_option = "MCP")
Arguments
X |
An n by p matrix of exposures. |
M |
An n by p matrix of mediators. |
time |
A vector of survival time of samples. |
status |
A vector of status indicator: 0=alive, 1=dead. |
model_option |
The penalization method used when selecting X for M. The options include MCP, elastic net and lasso. Default is MCP. |
Value
A list with the following components:
X_sel_Y_s1 |
X selected for Y using penAFT |
M_X_s1 |
X selected for M using the selected penalized model |
M_sel_Y_s1 |
M selected for Y using penAFT |
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_step1 <- StepOne(X = example_dat$X, M = example_dat$M, time = surv_dat$time,
status = surv_dat$status, model_option = "MCP")
Post-hoc model
Description
An auxiliary function which generates p-value based on outcome and mediation model.
Usage
StepThree(X, M, C, time, status, X_sel_Y_s1, M_X_sel_s2)
Arguments
X |
An n by p matrix of exposures. |
M |
An n by p matrix of mediators. |
C |
An n by p matrix of covariates. If there are no covariates, set C = NULL. |
time |
A vector of survival time of samples. |
status |
A vector of status indicator: 0=alive, 1=dead. |
X_sel_Y_s1 |
Outputs from StepOne: A vector of selected X for Y. |
M_X_sel_s2 |
Outputs from StepTwo: A data table with selected M, X pairs and related effect size. |
Value
A list with the following components:
p_beta_m |
p-values generated from outcome model |
p_alpha_x |
p-values generated from mediation modell |
outcome_model |
coefficient estimates from outcome model |
med_results |
coefficient estimates from mediation model |
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_step1 <- StepOne(X = example_dat$X, M = example_dat$M, time = surv_dat$time,
status = surv_dat$status, model_option = "MCP")
M_X_sel_s2 <- StepTwo(X = example_dat$X, M = example_dat$M, time = surv_dat$time,
status = surv_dat$status, X_sel_Y_s1 = res_step1$X_sel_Y_s1, M_X_s1 = res_step1$M_X_s1,
M_sel_Y_s1 = res_step1$M_sel_Y_s1)
res_step3 <- StepThree(X = example_dat$X, M = example_dat$M, C = example_dat$C,
time = surv_dat$time, status = surv_dat$status, X_sel_Y_s1 = res_step1$X_sel_Y_s1,
M_X_sel_s2 = M_X_sel_s2)
Select M-X pairs via SIS
Description
An auxiliary function which selects M-X paris using SIS.
Usage
StepTwo(
X,
M,
time,
status,
X_sel_Y_s1,
M_X_s1,
M_sel_Y_s1,
SIS_thres = "n/log(n)"
)
Arguments
X |
An n by p matrix of exposures. |
M |
An n by p matrix of mediators. |
time |
A vector of survival time of samples. |
status |
A vector of status indicator: 0=alive, 1=dead. |
X_sel_Y_s1 |
Outputs from StepOne: A vector of selected X for Y. |
M_X_s1 |
Outputs from StepOne: A list of selected X for M. |
M_sel_Y_s1 |
Outputs from StepOne: A vector of selected M for Y. |
SIS_thres |
SIS thresholds. Default is "n/log(n)". Other options include "n-1", "n/2log(n)", "2n/log(n)", "3n/log(n)". |
Value
A data table with selected M, X pairs and related effect size.
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_step1 <- StepOne(X = example_dat$X, M = example_dat$M, time = surv_dat$time,
status = surv_dat$status, model_option = "MCP")
M_X_sel_s2 <- StepTwo(X = example_dat$X, M = example_dat$M, time = surv_dat$time,
status = surv_dat$status, X_sel_Y_s1 = res_step1$X_sel_Y_s1, M_X_s1 = res_step1$M_X_s1,
M_sel_Y_s1 = res_step1$M_sel_Y_s1)
Example Dataset
Description
A example simulated dataset used in the SMAP package.
Usage
data(example_dat)
Format
example_dat
Examples
data(example_dat)
A function that returns adjusted p-values
Description
A function that returns adjusted p-values.
Usage
get_adjusted_p(res_SMAHP)
Arguments
res_SMAHP |
Outputs from SMAHP |
Value
A matrix of adjusted p-values.
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_SMAHP <- SMAHP(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time,
status = surv_dat$status)
get_adjusted_p(res_SMAHP)
A function that extracts coefficients.
Description
A function that extracts coefficients from mediation and outcome model.
Usage
get_coef(res_SMAHP)
Arguments
res_SMAHP |
Outputs from SMAHP |
Value
A data frame which includes the name of exposure and mediator with coefficients from mediation and outcome model.
beta1 |
The coefficient estimate of exposure in mediation model |
beta2 |
The coefficient estimate of mediator in outcome model |
beta3 |
The coefficient estimate of exposure in outcome model |
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_SMAHP <- SMAHP(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time,
status = surv_dat$status)
get_coef(res_SMAHP)
A function that returns mediation-exposure matrix
Description
A function that returns final mediation-exposure matrix.
Usage
get_pairs(res_SMAHP)
Arguments
res_SMAHP |
Outputs from SMAHP |
Value
Mediation-exposure matrix.
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_SMAHP <- SMAHP(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time,
status = surv_dat$status)
get_pairs(res_SMAHP)
A function that returns unadjusted p-values
Description
A function that returns unadjusted p-values.
Usage
get_raw_p(res_SMAHP)
Arguments
res_SMAHP |
Outputs from SMAHP |
Value
A matrix of unadjusted p-values.
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_SMAHP <- SMAHP(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time,
status = surv_dat$status)
get_raw_p(res_SMAHP)
A function that returns significant pathways.
Description
A function that returns significant pathways.
Usage
get_sig_pathway(res_SMAHP)
Arguments
res_SMAHP |
Outputs from SMAHP |
Value
A data frame which includes the name of exposure and mediator with adjusted p-value from identified significant pathways.
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
res_SMAHP <- SMAHP(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time,
status = surv_dat$status)
get_sig_pathway(res_SMAHP)