Welcome to the SMAHP vignette! This document provides an overview of the SMAHP package. SMAHP (Survival Mediation Analysis of High-dimensional Proteogenomic data) is a novel method for survival mediation analysis that simultaneously handles high-dimensional exposures and mediators, integrates multi-omics data, and offers a robust statistical framework for identifying causal pathways on survival outcomes. SMAHP package is a software extension of our methodology work.
We will use the example data built in the package to demonstrate how to use the main function.
The example data is a simulated dataset for demonstration purpose. It contains exposure (X), mediators (M), covariates (C) and survival data (surv_dat).
surv_dat <- example_dat$surv_dat
res <- SMAHP(example_dat$X, example_dat$M, example_dat$C,
time = surv_dat$time, status = surv_dat$status)
#> CV through: ### 20 %
#> CV through: ### ### 40 %
#> CV through: ### ### ### 60 %
#> CV through: ### ### ### ### 80 %
#> CV through: ### ### ### ### ### 100 %
#> Using a basic kernel estimate for local fdr; consider installing the ashr package for more accurate estimation. See ?local_mfdr
#> CV through: ### 20 %
#> CV through: ### ### 40 %
#> CV through: ### ### ### 60 %
#> CV through: ### ### ### ### 80 %
#> CV through: ### ### ### ### ### 100 %print(res$p_final_matrix)
#> M5 M16 M11 M12 M13 M7
#> X1 NA NA NA NA NA NA
#> X2 NA 8.726353e-14 7.263079e-13 3.916867e-13 0.1832984 NA
#> X3 NA NA NA NA NA NA
#> X4 NA NA NA NA NA NA
#> X5 NA NA NA NA NA NA
#> X6 NA NA NA NA NA NA
#> X7 NA NA NA NA NA NA
#> X8 NA 8.726353e-14 NA 3.916867e-13 0.1832984 NA
#> X9 NA 8.726353e-14 7.263079e-13 3.916867e-13 NA NA
#> X10 NA NA NA NA NA NA
#> X11 NA NA 7.263079e-13 NA 0.1832984 NA
#> X12 NA NA NA NA NA NA
#> X13 NA NA NA NA NA NA
#> X14 NA NA NA NA NA NA
#> X15 NA NA NA NA NA NA
#> X16 NA NA NA NA NA NA
#> X17 NA NA NA NA NA NA
#> X18 NA NA NA NA NA NA
#> X19 NA NA NA NA NA NA
#> X20 NA NA NA NA NA NA
#> X21 NA NA NA NA NA NA
#> X22 NA NA NA NA NA NA
#> X23 NA NA NA NA NA NA
#> X24 NA NA NA NA NA NA
#> X25 0 NA NA NA NA 0.6737463
#> X26 NA NA NA NA NA NA
#> X27 NA NA NA NA NA NA
#> X28 NA NA NA NA NA NA
#> X29 NA NA NA NA NA NA
#> X30 NA NA NA NA NA NA
#> X31 NA NA NA NA NA NA
#> X32 NA NA NA NA NA NA
#> X33 NA NA NA NA NA NA
#> X34 NA NA NA NA NA NA
#> X35 0 NA NA NA NA 0.6737463
#> X36 NA NA NA NA NA NA
#> X37 NA NA NA NA NA NA
#> X38 NA NA NA NA NA NA
#> X39 0 NA NA NA NA 0.6737463
#> X40 NA NA NA NA NA NAprint(res$p_adjusted_matrix)
#> M5 M16 M11 M12 M13 M7
#> X1 NA NA NA NA NA NA
#> X2 NA 3.490541e-12 1.452616e-11 1.044498e-11 1 NA
#> X3 NA NA NA NA NA NA
#> X4 NA NA NA NA NA NA
#> X5 NA NA NA NA NA NA
#> X6 NA NA NA NA NA NA
#> X7 NA NA NA NA NA NA
#> X8 NA 3.490541e-12 NA 1.044498e-11 1 NA
#> X9 NA 3.490541e-12 1.452616e-11 1.044498e-11 NA NA
#> X10 NA NA NA NA NA NA
#> X11 NA NA 1.452616e-11 NA 1 NA
#> X12 NA NA NA NA NA NA
#> X13 NA NA NA NA NA NA
#> X14 NA NA NA NA NA NA
#> X15 NA NA NA NA NA NA
#> X16 NA NA NA NA NA NA
#> X17 NA NA NA NA NA NA
#> X18 NA NA NA NA NA NA
#> X19 NA NA NA NA NA NA
#> X20 NA NA NA NA NA NA
#> X21 NA NA NA NA NA NA
#> X22 NA NA NA NA NA NA
#> X23 NA NA NA NA NA NA
#> X24 NA NA NA NA NA NA
#> X25 0 NA NA NA NA 1
#> X26 NA NA NA NA NA NA
#> X27 NA NA NA NA NA NA
#> X28 NA NA NA NA NA NA
#> X29 NA NA NA NA NA NA
#> X30 NA NA NA NA NA NA
#> X31 NA NA NA NA NA NA
#> X32 NA NA NA NA NA NA
#> X33 NA NA NA NA NA NA
#> X34 NA NA NA NA NA NA
#> X35 0 NA NA NA NA 1
#> X36 NA NA NA NA NA NA
#> X37 NA NA NA NA NA NA
#> X38 NA NA NA NA NA NA
#> X39 0 NA NA NA NA 1
#> X40 NA NA NA NA NA NAprint(res$p_med_matrix)
#> M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M16 M17 M18 M19 M20
#> X1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X2 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0
#> X3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X8 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0
#> X9 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0
#> X10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X11 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> X12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X25 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X35 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X39 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> X40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0The main function returns a list containing three matrices: (1) p_final_matrix, which is the raw p-value before adjustment, (2) p_adjusted_matrix, which contains p-values adjusted using the selected adjustment method, and (3) p_med_matrix, a mediation-exposure matrix encoded with 0s and 1s. In p_med_matrix, a value of 1 indicates a detected mediation association, while 0 means no significant mediation association. The NAs in p_final_matrix and p_adjusted_matrix mean the exposure-mediator pairs were not selected in previous steps.
The function get_sig_pathway can be used to extract the the name of exposure and mediator from identified significant mediation pathways with related adjusted p-values.
get_sig_pathway(res_SMAHP = res)
#> X M adjusted_pvalue
#> <char> <char> <num>
#> 1: X25 M5 0.000000e+00
#> 2: X35 M5 0.000000e+00
#> 3: X39 M5 0.000000e+00
#> 4: X2 M11 1.452616e-11
#> 5: X9 M11 1.452616e-11
#> 6: X11 M11 1.452616e-11
#> 7: X2 M12 1.044498e-11
#> 8: X8 M12 1.044498e-11
#> 9: X9 M12 1.044498e-11
#> 10: X2 M16 3.490541e-12
#> 11: X8 M16 3.490541e-12
#> 12: X9 M16 3.490541e-12The function get_coef can be used to extract the the name of exposure and mediator from identified significant mediation pathways with related coefficient estimates.
get_coef(res_SMAHP = res)
#> X M beta1 beta2 beta3
#> <char> <char> <num> <num> <num>
#> 1: X25 M5 0.7848745 4.499896 -0.24182382
#> 2: X35 M5 0.7398743 4.499896 -0.64624279
#> 3: X39 M5 0.6538620 4.499896 -0.53052986
#> 4: X2 M11 0.7700538 3.871182 -0.70411160
#> 5: X9 M11 0.8267428 3.871182 -1.02512189
#> 6: X11 M11 0.7483066 3.871182 -0.86970220
#> 7: X2 M12 0.7796968 3.851632 -0.70411160
#> 8: X8 M12 0.8338865 3.851632 0.06451432
#> 9: X9 M12 0.7770952 3.851632 -1.02512189
#> 10: X2 M16 0.7564046 4.182464 -0.70411160
#> 11: X8 M16 0.7814212 4.182464 0.06451432
#> 12: X9 M16 0.8198452 4.182464 -1.02512189