Simulation Experiment Recipe

Objectives

In Wang et al. (2023), high-throughput single-cell gene-silencing experiments were performed to assess the presence of epistasis (i.e,. non-additive interaction effects) in the genetic regulation of cardiac structure. More specifically, these single-cell experiments were designed to assess whether the genes pairs CCDC141 and TTN as well as CCDC141 and IGF1R interact non-additively to impact the cell diameter of human induced pluriopotent stem cell-derived cardiomyocytes. Mathematically, this boils down to studying the following regression problem: for a gene pair, say gene 1 and gene 2, consider

\[y_i = \beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2} + \beta_{12} x_{i1} x_{i2} + \epsilon_i,\]

where \(y_i\) is the diameter (\(\mu m\)) of cell \(i\), \(x_{ij}\) is an indicator of whether cell \(i\) belonged to an experimental batch where gene \(j\) was silenced, and \(\epsilon_i\) is the random error or noise term for cell \(i\). Under this model, the quantity of interest is \(\beta_{12}\), or the coefficient corresponding to the non-additive interaction effect between genes 1 and 2, and we would like to assess the null hypothesis that \(\beta_{12} = 0\) (i.e., no epistatic effect) versus the alternative hypothesis that \(\beta_{12} \neq 0\) (i.e., a non-zero epistatic effect).

One possible complication in the statistical analysis of gene-silencing experiments, however, is that the gene-silencing efficiency may not be 100% and may vary across different experimental batches of cells. In this simulation study, we aim to assess whether variation in silencing efficiencies lead to spurious epistasis signals.

Overall, we found unsurprisingly that the signal-to-noise ratio plays a role in understanding the effect of varying silencing efficiencies. But notably, in the very low signal-to-noise regimes that we expect to be realistic in practice, we found that the differences in our observed gene-silencing efficiencies do not typically lead to high false positive rates. Furthermore, when the strength of the interaction effect is small but not 0, the resulting quantile regression p-values are frequently not significant. We realize however that this simulation models the gene-silencing efficiencies in a particular way, and model misspecification of not only the silencing efficiencies but the model form itself is rightfully a concern. We thus simply view this simulation study as a helpful starting point and acknowledge that additional simulations can be done to explore these avenues.

In what follows, we provide additional details regarding the simulation setup (see tabs above) as well as the results of the simulation study (see section tabs on the left side panel).

Data Generation

CCDC141-IGF1R, Diseased

To simulate data from batches with different gene-silencing efficiencies, we generated data via the following model:

\[y_i = \beta_0 + (\beta_1 x_{i1} + \beta_2 x_{i2} + \beta_{12} x_{i1} x_{i2}) \cdot z_i + \epsilon_i,\]

where \(y_i\) is the diameter (\(\mu m\)) of cell \(i\), \(x_{ij}\) is an indicator of whether cell \(i\) belonged to an experimental batch where gene \(j\) was silenced, \(z_i\) is an indicator of whether the gene(s) were successfully silenced in cell \(i\), generated via \(z_i \mid x_{i1} = j, x_{i2} = k \stackrel{ind.}{\sim} Bernoulli(\pi_{jk})\), and \(\epsilon_i \stackrel{iid}{\sim} N(0, \sigma^2)\) is the random error or noise term for cell \(i\).

We simulated \(n = 500\) cells for each of the gene-silencing conditions, that is,

  • 500 scrambled control cells (i.e., \(x_{i1} = x_{i2} = 0\)),
  • 500 cells wherein gene 1 was silenced (i.e., \(x_{i1} = 1\) and \(x_{i2} = 0\)),
  • 500 cells wherein gene 2 was silenced (i.e., \(x_{i1} = 0\) and \(x_{i2} = 1\)), and
  • 500 cells wherein gene 1 and gene 2 were silenced (i.e., \(x_{i1} = x_{i2} = 1\)).

Unless otherwise specified, simulations were run across 100 replicates, and the following simulation parameter values were used:

  • \(\beta_0 = 0\)
  • \(\beta_1 = -1\)
  • \(\beta_2 = -1\)
  • \(\beta_{12} = 0\)
  • \(\sigma^2 = 1\)

We then ran simulations that varied across the interaction effect strength \(\beta_{12} \in \{-1, -0.5, -0.2, -0.1, 0\}\) and the noise level \(\sigma \in \{0.1, 0.2, 0.5, 1, 1.5, 2\}\).

So that we may best connect to the observed experimental data in Wang et al. (2023), we took the observed gene-silencing efficiencies from the experiments and used these values for the simulated gene-silencing efficiencies \(\{\pi_{01}, \pi_{10}, \pi_{11}\}\). Since there were four scenarios of interest (i.e., the CCDC141-TTN interaction in the healthy and diseased (MYH7-R403Q) cell lines and the CCDC141-IGF1R interaction in the healthy and diseased (MYH7-R403Q) cell lines), we repeated the simulation study under four different choices of \(\{\pi_{01}, \pi_{10}, \pi_{11}\}\).

For this particular data-generating process, we set the simulated gene-silencing efficiencies to be those from the CCDC141-IGF1R experiment in the healthy cell line so that

  • \(\pi_{01} = 0.92\)
  • \(\pi_{10} = 0.83\)
  • \(\pi_{11} = 0.695\)

Remark: We note that the above model is a simplistic interpretation of how gene-silencing efficiencies may effect the response of interest and that there are other ways to incorporate gene-silencing efficiencies into the model that may be more realistic. We view this simulation study as a helpful starting point for understanding the effect of varying gene-silencing efficiencies on epistasis modeling.

Function

## function(n, beta0, beta1, beta2, beta12,
##                                eff1 = 1, eff2 = 1, int_eff = 1, 
##                                err_fun = rnorm, ...) {
##   eff1_vec <- rbinom(n, 1, eff1)
##   eff2_vec <- rbinom(n, 1, eff2)
##   int_eff_vec <- rbinom(n, 1, int_eff)
##   
##   y0 <- beta0 + err_fun(n, ...)
##   y1 <- beta0 + beta1 * eff1_vec + err_fun(n, ...)
##   y2 <- beta0 + beta2 * eff2_vec + err_fun(n, ...)
##   y12 <- beta0 + (beta1 + beta2 + beta12) * int_eff_vec + err_fun(n, ...)
##   
##   x <- matrix(0, nrow = 4 * n, ncol = 2)
##   x[(n + 1):(2 * n), 1] <- 1
##   x[(2 * n + 1):(3 * n), 2] <- 1
##   x[(3 * n + 1):(4 * n), 1:2] <- 1
##   y <- c(y0, y1, y2, y12)
##   
##   out <- list(
##     x = x,
##     y = y,
##     eff1_vec = eff1_vec,
##     eff2_vec = eff2_vec,
##     int_eff_vec = int_eff_vec
##   )
## }

Input Parameters

## $n
## [1] 500
## 
## $beta0
## [1] 0
## 
## $beta1
## [1] -1
## 
## $beta2
## [1] -1
## 
## $beta12
## [1] 0
## 
## $eff1
## [1] 0.92
## 
## $eff2
## [1] 0.83
## 
## $int_eff
## [1] 0.695
## 
## $err_fun
## function (n, mean = 0, sd = 1) 
## .Call(C_rnorm, n, mean, sd)
## <bytecode: 0x7feab78c3da0>
## <environment: namespace:stats>
## 
## $sd
## [1] 1

CCDC141-IGF1R, Healthy

In this data-generating process, we simulated data as described above but set the simulated gene-silencing efficiencies to be the observed efficiencies from the CCDC141-IGF1R experiment in the diseased cell line:

  • \(\pi_{01} = 0.66\)
  • \(\pi_{10} = 0.9\)
  • \(\pi_{11} = 0.725\)

Function

## function(n, beta0, beta1, beta2, beta12,
##                                eff1 = 1, eff2 = 1, int_eff = 1, 
##                                err_fun = rnorm, ...) {
##   eff1_vec <- rbinom(n, 1, eff1)
##   eff2_vec <- rbinom(n, 1, eff2)
##   int_eff_vec <- rbinom(n, 1, int_eff)
##   
##   y0 <- beta0 + err_fun(n, ...)
##   y1 <- beta0 + beta1 * eff1_vec + err_fun(n, ...)
##   y2 <- beta0 + beta2 * eff2_vec + err_fun(n, ...)
##   y12 <- beta0 + (beta1 + beta2 + beta12) * int_eff_vec + err_fun(n, ...)
##   
##   x <- matrix(0, nrow = 4 * n, ncol = 2)
##   x[(n + 1):(2 * n), 1] <- 1
##   x[(2 * n + 1):(3 * n), 2] <- 1
##   x[(3 * n + 1):(4 * n), 1:2] <- 1
##   y <- c(y0, y1, y2, y12)
##   
##   out <- list(
##     x = x,
##     y = y,
##     eff1_vec = eff1_vec,
##     eff2_vec = eff2_vec,
##     int_eff_vec = int_eff_vec
##   )
## }

Input Parameters

## $n
## [1] 500
## 
## $beta0
## [1] 0
## 
## $beta1
## [1] -1
## 
## $beta2
## [1] -1
## 
## $beta12
## [1] 0
## 
## $eff1
## [1] 0.66
## 
## $eff2
## [1] 0.9
## 
## $int_eff
## [1] 0.725
## 
## $err_fun
## function (n, mean = 0, sd = 1) 
## .Call(C_rnorm, n, mean, sd)
## <bytecode: 0x7feab72fd800>
## <environment: namespace:stats>
## 
## $sd
## [1] 1

CCDC141-TTN, Diseased

In this data-generating process, we simulated data as described above but set the simulated gene-silencing efficiencies to be the observed efficiencies from the CCDC141-TTN experiment in the diseased cell line:

  • \(\pi_{01} = 0.92\)
  • \(\pi_{10} = 0.99\)
  • \(\pi_{11} = 0.92\)

Function

## function(n, beta0, beta1, beta2, beta12,
##                                eff1 = 1, eff2 = 1, int_eff = 1, 
##                                err_fun = rnorm, ...) {
##   eff1_vec <- rbinom(n, 1, eff1)
##   eff2_vec <- rbinom(n, 1, eff2)
##   int_eff_vec <- rbinom(n, 1, int_eff)
##   
##   y0 <- beta0 + err_fun(n, ...)
##   y1 <- beta0 + beta1 * eff1_vec + err_fun(n, ...)
##   y2 <- beta0 + beta2 * eff2_vec + err_fun(n, ...)
##   y12 <- beta0 + (beta1 + beta2 + beta12) * int_eff_vec + err_fun(n, ...)
##   
##   x <- matrix(0, nrow = 4 * n, ncol = 2)
##   x[(n + 1):(2 * n), 1] <- 1
##   x[(2 * n + 1):(3 * n), 2] <- 1
##   x[(3 * n + 1):(4 * n), 1:2] <- 1
##   y <- c(y0, y1, y2, y12)
##   
##   out <- list(
##     x = x,
##     y = y,
##     eff1_vec = eff1_vec,
##     eff2_vec = eff2_vec,
##     int_eff_vec = int_eff_vec
##   )
## }

Input Parameters

## $n
## [1] 500
## 
## $beta0
## [1] 0
## 
## $beta1
## [1] -1
## 
## $beta2
## [1] -1
## 
## $beta12
## [1] 0
## 
## $eff1
## [1] 0.92
## 
## $eff2
## [1] 0.99
## 
## $int_eff
## [1] 0.92
## 
## $err_fun
## function (n, mean = 0, sd = 1) 
## .Call(C_rnorm, n, mean, sd)
## <bytecode: 0x7feab75fcfd8>
## <environment: namespace:stats>
## 
## $sd
## [1] 1

CCDC141-TTN, Healthy

In this data-generating process, we simulated data as described above but set the simulated gene-silencing efficiencies to be the observed efficiencies from the CCDC141-TTN experiment in the healthy cell line:

  • \(\pi_{01} = 0.66\)
  • \(\pi_{10} = 0.8\)
  • \(\pi_{11} = 0.93\)

Function

## function(n, beta0, beta1, beta2, beta12,
##                                eff1 = 1, eff2 = 1, int_eff = 1, 
##                                err_fun = rnorm, ...) {
##   eff1_vec <- rbinom(n, 1, eff1)
##   eff2_vec <- rbinom(n, 1, eff2)
##   int_eff_vec <- rbinom(n, 1, int_eff)
##   
##   y0 <- beta0 + err_fun(n, ...)
##   y1 <- beta0 + beta1 * eff1_vec + err_fun(n, ...)
##   y2 <- beta0 + beta2 * eff2_vec + err_fun(n, ...)
##   y12 <- beta0 + (beta1 + beta2 + beta12) * int_eff_vec + err_fun(n, ...)
##   
##   x <- matrix(0, nrow = 4 * n, ncol = 2)
##   x[(n + 1):(2 * n), 1] <- 1
##   x[(2 * n + 1):(3 * n), 2] <- 1
##   x[(3 * n + 1):(4 * n), 1:2] <- 1
##   y <- c(y0, y1, y2, y12)
##   
##   out <- list(
##     x = x,
##     y = y,
##     eff1_vec = eff1_vec,
##     eff2_vec = eff2_vec,
##     int_eff_vec = int_eff_vec
##   )
## }

Input Parameters

## $n
## [1] 500
## 
## $beta0
## [1] 0
## 
## $beta1
## [1] -1
## 
## $beta2
## [1] -1
## 
## $beta12
## [1] 0
## 
## $eff1
## [1] 0.66
## 
## $eff2
## [1] 0.8
## 
## $int_eff
## [1] 0.93
## 
## $err_fun
## function (n, mean = 0, sd = 1) 
## .Call(C_rnorm, n, mean, sd)
## <bytecode: 0x7feab7ce5bc0>
## <environment: namespace:stats>
## 
## $sd
## [1] 1

Efficiency = 1

In this data-generating process, we simulated data as described above but set the simulated gene-silencing efficiencies to be 1. That is, \(\pi_{01} = \pi_{10} = \pi_{11} = 1\). This data-generating process is akin to the setting where the gene-silencing efficiences are perfect, and there is no variation in gene-silencing efficiences across different batches/gene-silencing conditions.

Function

## function(n, beta0, beta1, beta2, beta12,
##                                eff1 = 1, eff2 = 1, int_eff = 1, 
##                                err_fun = rnorm, ...) {
##   eff1_vec <- rbinom(n, 1, eff1)
##   eff2_vec <- rbinom(n, 1, eff2)
##   int_eff_vec <- rbinom(n, 1, int_eff)
##   
##   y0 <- beta0 + err_fun(n, ...)
##   y1 <- beta0 + beta1 * eff1_vec + err_fun(n, ...)
##   y2 <- beta0 + beta2 * eff2_vec + err_fun(n, ...)
##   y12 <- beta0 + (beta1 + beta2 + beta12) * int_eff_vec + err_fun(n, ...)
##   
##   x <- matrix(0, nrow = 4 * n, ncol = 2)
##   x[(n + 1):(2 * n), 1] <- 1
##   x[(2 * n + 1):(3 * n), 2] <- 1
##   x[(3 * n + 1):(4 * n), 1:2] <- 1
##   y <- c(y0, y1, y2, y12)
##   
##   out <- list(
##     x = x,
##     y = y,
##     eff1_vec = eff1_vec,
##     eff2_vec = eff2_vec,
##     int_eff_vec = int_eff_vec
##   )
## }

Input Parameters

## $n
## [1] 500
## 
## $beta0
## [1] 0
## 
## $beta1
## [1] -1
## 
## $beta2
## [1] -1
## 
## $beta12
## [1] 0
## 
## $eff1
## [1] 1
## 
## $eff2
## [1] 1
## 
## $int_eff
## [1] 1
## 
## $err_fun
## function (n, mean = 0, sd = 1) 
## .Call(C_rnorm, n, mean, sd)
## <bytecode: 0x7feab664cf30>
## <environment: namespace:stats>
## 
## $sd
## [1] 1

Methods and Evaluation

Methods

Quantile Regression (tau = 0.5)

In Wang et al. (2023), epistasis was assessed in the experimental data by fitting a quantile regression model and testing whether the interaction effect term was significantly different from 0. In this simulation study, we thus follow suit. Given the simulated data, we fit a quantile regression model:

\[y_i = \beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2} + \beta_{12} x_{i1} x_{i2} + \epsilon_i,\]

where \(y_i\) is the diameter (\(\mu m\)) of cell \(i\), \(x_{ij}\) is an indicator of whether cell \(i\) belonged to an experimental batch where gene \(j\) was silenced, and \(\epsilon_i\) is the random error or noise term for cell \(i\). Using this regression model, we performed a t-test to assess the null hypothesis that \(\beta_{12} = 0\) (i.e., no epistatic effect) versus the alternative hypothesis that \(\beta_{12} \neq 0\) (i.e., a non-zero epistatic effect) at the 0.5 quantile level (i.e., median).

We note that in Wang et al. (2023), percentile bootstrap hypothesis tests were conducted in addition to the t-tests, but as they gave similar conclusions in Wang et al. (2023), we omit the bootstrap hypothesis tests here to reduce the computational burden.

Function

## function(x, y, tau = 0.5, min_thr = 1e-16, ...) {
##   fit_df <- data.frame(.y = y, x)
##   fit <- quantreg::rq(formula = .y ~ X1 * X2, tau = tau, data = fit_df)
##   fit_summary <- summary(fit, se = "boot")$coefficients %>%
##     as.data.frame() %>%
##     tibble::rownames_to_column("Term") %>%
##     dplyr::rename(Coefficient = Value) %>%
##     dplyr::filter(Term == "X1:X2") %>%
##     dplyr::mutate(`Pr(>|t|)` = max(min_thr, `Pr(>|t|)`))
##   return(fit_summary)
##   # return(broom::tidy(fit_summary))
## }

Input Parameters

## list()

Evaluation

P-values Summary

N/A

Function

## function (fit_results, vary_params = NULL, nested_cols = NULL, 
##     feature_col, imp_col, group_cols = NULL, na_rm = FALSE, summary_funs = c("mean", 
##         "median", "min", "max", "sd", "raw"), custom_summary_funs = NULL, 
##     eval_id = "feature_importance") 
## {
##     group_vars <- c(".dgp_name", ".method_name", vary_params, 
##         group_cols, feature_col)
##     eval_tib <- eval_feature_importance(fit_results = fit_results, 
##         vary_params = vary_params, nested_cols = nested_cols, 
##         feature_col = feature_col, imp_col = imp_col, group_cols = group_cols) %>% 
##         dplyr::group_by(dplyr::across(tidyselect::all_of(group_vars)))
##     eval_summary <- eval_summarizer(eval_data = eval_tib, eval_id = eval_id, 
##         value_col = imp_col, summary_funs = summary_funs, custom_summary_funs = custom_summary_funs, 
##         na_rm = na_rm)
##     return(eval_summary)
## }
## <bytecode: 0x7feacc42f890>
## <environment: namespace:simChef>

Input Parameters

## $eval_id
## [1] "pval"
## 
## $nested_cols
## NULL
## 
## $feature_col
## [1] "Term"
## 
## $imp_col
## [1] "Pr(>|t|)"

Visualizations

P-values Plot

In this visualization, we plot the distribution of p-values corresponding to the interaction effect term (i.e., \(\beta_{12}\)) across 100 simulation replicates. The red line indicates the \(\alpha = 0.05\) significance level. The y-axis is on the log10 scale. When \(\beta_{12} = 0\), we would hope that the p-values are greater than the significance threshold.

CCDC141-IGF1R, Diseased Simulation - CCDC141-IGF1R, Diseased - Varying beta12

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab36244e0>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
CCDC141-IGF1R, Diseased Simulation - CCDC141-IGF1R, Diseased - Varying sd

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feace7e3600>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
CCDC141-IGF1R, Healthy Simulation - CCDC141-IGF1R, Healthy - Varying beta12

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab254bca0>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
CCDC141-IGF1R, Healthy Simulation - CCDC141-IGF1R, Healthy - Varying sd

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab4433fb8>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
CCDC141-TTN, Diseased Simulation - CCDC141-TTN, Diseased - Varying beta12

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab517f108>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
CCDC141-TTN, Diseased Simulation - CCDC141-TTN, Diseased - Varying sd

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab45e2240>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
CCDC141-TTN, Healthy Simulation - CCDC141-TTN, Healthy - Varying beta12

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab5405090>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
CCDC141-TTN, Healthy Simulation - CCDC141-TTN, Healthy - Varying sd

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab278b738>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
Efficiency = 1 Simulation - Efficiency = 1 - Varying beta12

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab5a03530>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>
Efficiency = 1 Simulation - Efficiency = 1 - Varying sd

Function

## function(fit_results = NULL, eval_results, vary_params = NULL,
##                          eval_name, eval_id, add_ggplot_layers = NULL) {
##   vary_params <- unique(vary_params)
##   plt_df <- eval_results[[eval_name]] %>%
##     dplyr::filter(!is.na(!!rlang::sym(vary_params)))
##   plt <- plt_df %>%
##     tidyr::unnest(cols = tidyselect::all_of(sprintf("raw_%s", eval_id))) %>%
##     vdocs::plot_boxplot(
##       x_str = vary_params, y_str = sprintf("raw_%s", eval_id)
##     ) +
##     ggplot2::labs(y = "P-value")
##   if (vary_params == "sd") {
##     plt <- plt + ggplot2::labs(x = bquote("SD of Noise" ~ (sigma)))
##   } else if (vary_params == "beta12") {
##     plt <- plt + ggplot2::labs(x = bquote("Interaction Effect" ~ (beta[12])))
##   }
##   n_dgps <- length(unique(plt_df$.dgp_name))
##   if (n_dgps > 1) {
##     plt <- plt + ggplot2::facet_wrap(~ .dgp_name)
##   }
##   if (!is.null(add_ggplot_layers)) {
##     for (ggplot_layer in add_ggplot_layers) {
##       plt <- plt + ggplot_layer
##     }
##   }
##   return(plt)
## }
## <bytecode: 0x7feab5a41ce0>

Input Parameters

## $eval_name
## [1] "P-values Summary"
## 
## $eval_id
## [1] "pval"
## 
## $add_ggplot_layers
## $add_ggplot_layers[[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
## 
## $add_ggplot_layers[[2]]
## mapping: yintercept = ~yintercept 
## geom_hline: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity 
## 
## $add_ggplot_layers[[3]]
## <ggproto object: Class CoordCartesian, Coord, gg>
##     aspect: function
##     backtransform_range: function
##     clip: on
##     default: FALSE
##     distance: function
##     expand: TRUE
##     is_free: function
##     is_linear: function
##     labels: function
##     limits: list
##     modify_scales: function
##     range: function
##     render_axis_h: function
##     render_axis_v: function
##     render_bg: function
##     render_fg: function
##     setup_data: function
##     setup_layout: function
##     setup_panel_guides: function
##     setup_panel_params: function
##     setup_params: function
##     train_panel_guides: function
##     transform: function
##     super:  <ggproto object: Class CoordCartesian, Coord, gg>

CCDC141-IGF1R, Diseased Simulation

CCDC141-IGF1R, Diseased

Varying beta12

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-IGF1R, Diseased`
## $dgp$`CCDC141-IGF1R, Diseased`$beta12
## [1]  0.0 -0.1 -0.2 -0.5 -1.0
## 
## 
## 
## $method
## list()

Varying sd

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-IGF1R, Diseased`
## $dgp$`CCDC141-IGF1R, Diseased`$sd
## [1] 0.1 0.2 0.5 1.0 1.5 2.0
## 
## 
## 
## $method
## list()

CCDC141-IGF1R, Healthy Simulation

CCDC141-IGF1R, Healthy

Varying beta12

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-IGF1R, Healthy`
## $dgp$`CCDC141-IGF1R, Healthy`$beta12
## [1]  0.0 -0.1 -0.2 -0.5 -1.0
## 
## 
## 
## $method
## list()

Varying sd

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-IGF1R, Healthy`
## $dgp$`CCDC141-IGF1R, Healthy`$sd
## [1] 0.1 0.2 0.5 1.0 1.5 2.0
## 
## 
## 
## $method
## list()

CCDC141-TTN, Diseased Simulation

CCDC141-TTN, Diseased

Varying beta12

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-TTN, Diseased`
## $dgp$`CCDC141-TTN, Diseased`$beta12
## [1]  0.0 -0.1 -0.2 -0.5 -1.0
## 
## 
## 
## $method
## list()

Varying sd

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-TTN, Diseased`
## $dgp$`CCDC141-TTN, Diseased`$sd
## [1] 0.1 0.2 0.5 1.0 1.5 2.0
## 
## 
## 
## $method
## list()

CCDC141-TTN, Healthy Simulation

CCDC141-TTN, Healthy

Varying beta12

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-TTN, Healthy`
## $dgp$`CCDC141-TTN, Healthy`$beta12
## [1]  0.0 -0.1 -0.2 -0.5 -1.0
## 
## 
## 
## $method
## list()

Varying sd

P-values Plot

Parameter Values

## $dgp
## $dgp$`CCDC141-TTN, Healthy`
## $dgp$`CCDC141-TTN, Healthy`$sd
## [1] 0.1 0.2 0.5 1.0 1.5 2.0
## 
## 
## 
## $method
## list()

Efficiency = 1 Simulation

Efficiency = 1

Varying beta12

P-values Plot

Parameter Values

## $dgp
## $dgp$`Efficiency = 1`
## $dgp$`Efficiency = 1`$beta12
## [1]  0.0 -0.1 -0.2 -0.5 -1.0
## 
## 
## 
## $method
## list()

Varying sd

P-values Plot

Parameter Values

## $dgp
## $dgp$`Efficiency = 1`
## $dgp$`Efficiency = 1`$sd
## [1] 0.1 0.2 0.5 1.0 1.5 2.0
## 
## 
## 
## $method
## list()
---
title: "`r params$sim_name`"
author: "`r params$author`"
date: "`r format(Sys.time(), '%B %d, %Y')`"
output: rmarkdown::html_document
params:
  author: 
    label: "Author:"
    value: ""
  sim_name:
    label: "Simulation Experiment Name:"
    value: ""
  sim_path:
    label: "Path to Simulation Experiment Folder:"
    value: ""
  write_filename:
    label: "Output File:"
    value: ""
  show_code:
    label: "Show Code:"
    value: TRUE
  show_eval:
    label: "Show Evaluators:"
    value: TRUE
  show_viz:
    label: "Show Visualizers:"
    value: TRUE
  eval_order:
    label: "Order of Evaluators:"
    value: NULL
  viz_order:
    label: "Order of Visualizers:"
    value: NULL
  use_icons:
    label: "Use Icons:"
    value: TRUE
  use_vmodern:
    label: "Use vthemes::vmodern:"
    value: TRUE
  write:
    label: "Write File:"
    value: FALSE
  verbose:
    label: "Verbose Level:"
    value: 2
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  cache = FALSE,
  fig.align = "center",
  fig.pos = "H",
  fig.height = 12,
  fig.width = 10
)

options(
  width = 10000,
  knitr.kable.NA = "NA"
)

# scrollable text output
local({
  hook_output <- knitr::knit_hooks$get("output")
  knitr::knit_hooks$set(output = function(x, options) {
    if (!is.null(options$max.height)) {
      options$attr.output <- c(
        options$attr.output,
        sprintf('style="max-height: %s;"', options$max.height)
      )
    }
    hook_output(x, options)
  })
})

chunk_idx <- 1
doc_dir <- file.path(params$sim_path, "docs")
write_filename <- params$write_filename
```

```{r helper-funs}

#' Wrap text/code in knitr code chunk string
#'
#' @param code String of code to wrap in knitr code chunk
#' @param chunk_args String of arguments to place in the knitr code chunk header
#' @return String of code, wrapped inside the knitr code chunk ``` markers
write_code_chunk <- function(code = "", chunk_args = "") {
  sprintf("\n```{r, %s}\n%s\n```\n", chunk_args, code)
}

#' Write text to vector (write_flag = TRUE) or to console (write_flag = FALSE)
#'
#' @param ... Text to write to vector or to console
#' @param old_text Previous text to append to when writing to a vector
#' @param write_flag Boolean indicating whether to write text to a vector
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @return If write_flag = TRUE, returns vector of text. Otherwise, text is
#'   written to console via `cat()`.
write <- function(..., old_text = NULL, write_flag) {
  if (write_flag) {
    return(c(old_text, ...))
  } else {
    dots_list <- list(...) %>%
      purrr::map(
        function(x) {
          if (stringr::str_detect(x, "`r .*`")) {
            # run r code before printing results in cat()
            out <- stringr::str_replace(
              x, "`r .*`",
              eval(parse(text = stringr::str_extract(x, "(?<=`r )(.*?)(?=`)")))
            )
          } else {
            out <- x
          }
          return(out)
        }
      )
    do.call(cat, args = c(dots_list, list(sep = "")))
  }
}

#' Write text to file
#'
#' @param path Path to output file
#' @param ... Text to output to file
write_to_file <- function(path, ...) {
  storelines <- readLines(path)
  storelines <- c(storelines, ...)
  writeLines(storelines, path)
}

#' Get order of objects to display
#'
#' @param obj_names Vector of all object names that need to be displayed.
#' @param obj_order Vector of object names in the desired appearance order.
#' @return Vector of object names in the order in which they will be displayed.
get_object_order <- function(obj_names, obj_order = NULL) {
  if (is.null(obj_order)) {
    return(obj_names)
  } else {
    return(intersect(obj_order, obj_names))
  }
}

#' Get all experiments under a given directory name
#'
#' @param dir_name name of directory
#' @return list of named experiments
get_descendants <- function(dir_name) {
  experiments <- list()
  for (d in list.dirs(dir_name)) {
    if (file.exists(file.path(d, "experiment.rds"))) {
      if (identical(d, params$sim_path)) {
        exp_name <- "Base"
      } else {
        exp_name <- stringr::str_replace_all(
          stringr::str_remove(d, paste0(params$sim_path, "/")),
          "/", " - "
        )
      }
      experiments[[exp_name]] <- readRDS(file.path(d, "experiment.rds"))
    }
  }
  return(experiments)
}

#' Check if experiment exists
#'
#' @param dir_name name of directory or vector thereof
#' @param recursive logical; if TRUE, checks if experiment exists under the
#'   given directory(s); if FALSE, checks if any experiment exists under the
#'   directory(s) and its descendants
#' @return TRUE if experiment exists and FALSE otherwise
experiment_exists <- function(dir_name, recursive = FALSE) {
  res <- purrr::map_lgl(
    dir_name,
    function(d) {
      if (!recursive) {
        exp_fname <- file.path(d, "experiment.rds")
        return(file.exists(exp_fname))
      } else {
        descendants <- get_descendants(d)
        return(length(descendants) > 0)
      }
    }
  )
  return(any(res))
}

#' Displays content for specified part of recipe
#'
#' @param field_name part of recipe to show; must be one of "dgp", "method",
#'   "evaluator", or "visualizer"
#' @param write_flag Boolean indicating whether to write text to a vector
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @return content for recipe
show_recipe <- function(field_name = c(
                          "dgp", "method", "evaluator", "visualizer"
                        ),
                        write_flag = FALSE) {
  field_name <- match.arg(field_name)
  func_name <- dplyr::case_when(
    field_name == "evaluator" ~ "eval",
    field_name == "visualizer" ~ "viz",
    TRUE ~ field_name
  )
  descendants <- get_descendants(dir_name = params$sim_path)
  objs <- purrr::map(descendants, ~ .x[[paste0("get_", field_name, "s")]]())
  obj_names <- unique(purrr::reduce(sapply(objs, names), c))

  if (field_name %in% c("method", "evaluator")) {
    obj_header <- "\n\n#### %s {.tabset .tabset-pills .tabset-circle .tabset-recipe}\n\n"
    showtype_header <- "\n\n##### %s {.tabset .tabset-pills}\n\n"
    exp_header <- "\n\n###### %s \n\n"
  } else {
    obj_header <- "\n\n### %s {.tabset .tabset-pills .tabset-circle .tabset-recipe}\n\n"
    showtype_header <- "\n\n#### %s {.tabset .tabset-pills}\n\n"
    exp_header <- "\n\n##### %s \n\n"
  }

  if (params$use_icons) {
    if (params$use_vmodern) {
      description_label <- "`r fontawesome::fa('readme', fill = 'white')`"
      code_label <- "`r fontawesome::fa('code', fill = 'white')`"
    } else {
      description_label <- "`r fontawesome::fa('readme')`"
      code_label <- "`r fontawesome::fa('code')`"
    }
  } else {
    description_label <- "Description"
    code_label <- "Code"
  }

  if (all(sapply(objs, length) == 0)) {
    if (write_flag) {
      return("N/A")
    } else {
      return(cat("N/A"))
    }
  }

  recipe <- c()
  for (idx in 1:length(obj_names)) {
    obj_name <- obj_names[idx]
    description_fpath <- file.path(
      doc_dir, paste0(field_name, "s"), paste0(obj_name, ".md")
    )
    
    if (params$use_vmodern) {
      recipe <- write(
        "\n\n<div class='panel panel-default padded-panel'>\n\n",
        old_text = recipe, write_flag = write_flag
      )
    }

    recipe <- write(
      sprintf(obj_header, obj_name),
      sprintf(showtype_header, description_label),
      pasteMd(description_fpath),
      old_text = recipe, write_flag = write_flag
    )

    if (params$show_code) {
      recipe <- write(
        sprintf(showtype_header, code_label),
        old_text = recipe, write_flag = write_flag
      )

      keep_objs <- purrr::compact(purrr::map(objs, obj_name))
      is_identical <- all(
        purrr::map_lgl(keep_objs, ~ isTRUE(check_equal(.x, keep_objs[[1]])))
      )
      for (exp in names(keep_objs)) {
        obj <- keep_objs[[exp]]
        if (!is_identical) {
          recipe <- write(
            sprintf(exp_header, exp),
            old_text = recipe, write_flag = write_flag
          )
        }

        recipe <- write(
          "\n\n**Function**\n\n",
          old_text = recipe, write_flag = write_flag
        )
        if (write_flag) {
          recipe <- sprintf(
            "show_recipe(%s_objs, '%s', '%s', what = 'function')",
            func_name, obj_name, exp
          ) %>%
            write_code_chunk(chunk_args = "max.height='200px'") %>%
            write(old_text = recipe, write_flag = write_flag)
        } else {
          vthemes::subchunkify(
            obj[[paste0(func_name, "_fun")]], chunk_idx,
            other_args = "max.height='200px'"
          )
          chunk_idx <<- chunk_idx + 1
        }

        recipe <- write(
          "\n\n**Input Parameters**\n\n",
          old_text = recipe, write_flag = write_flag
        )
        if (write_flag) {
          recipe <- sprintf(
            "show_recipe(%s_objs, '%s', '%s', what = 'parameters')",
            func_name, obj_name, exp
          ) %>%
            write_code_chunk(chunk_args = "max.height='200px'") %>%
            write(old_text = recipe, write_flag = write_flag)
        } else {
          vthemes::subchunkify(
            obj[[paste0(func_name, "_params")]], chunk_idx,
            other_args = "max.height='200px'"
          )
          chunk_idx <<- chunk_idx + 1
        }

        if (is_identical) {
          break
        }
      }
    }

    if (params$use_vmodern) {
      recipe <- write(
        "\n\n</div>\n\n", old_text = recipe, write_flag = write_flag
      )
    }
  }

  return(recipe)
}

#' Reads in file if it exists and returns NULL if the file does not exist
#'
#' @param filename name of .rds file to try reading in
#' @return output of filename.rds if the file exists and NULL otherwise
get_results <- function(filename) {
  if (file.exists(filename)) {
    results <- readRDS(filename)
  } else {
    results <- NULL
  }
  return(results)
}

#' Displays output (both from evaluate() and visualize()) from saved results under
#' a specified directory
#'
#' @param dir_name name of directory
#' @param depth integer; depth of directory from parent/base experiment's folder
#' @param base logical; whether or not this is a base experiment
#' @param show_header logical; whether or not to show section header
#' @param verbose integer; 0 = no messages; 1 = print out directory name only;
#'   2 = print out directory name and name of evaluators/visualizers
#' @param write_flag Boolean indicating whether to write text to a vector
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @return content results from evaluate() and visualize() from the experiment
show_results <- function(dir_name, depth, base = FALSE, show_header = TRUE,
                         verbose = 1, write_flag = FALSE) {
  if (verbose >= 1) {
    inform(paste0(paste(rep("*", depth), collapse = ""), basename(dir_name)))
  }

  if (depth == 1) {
    header_template <- "\n\n%s %s {.tabset .tabset-pills .tabset-vmodern}\n\n"
  } else {
    if (base || !experiment_exists(dir_name)) {
      header_template <- "\n\n%s %s {.tabset .tabset-pills}\n\n"
    } else {
      header_template <- "\n\n%s %s {.tabset .tabset-pills .tabset-circle}\n\n"
    }
  }

  results <- c()
  if (show_header) {
    results <- sprintf(
      header_template,
      paste(rep("#", depth), collapse = ""),
      basename(dir_name)
    ) %>%
      write(old_text = results, write_flag = write_flag)
  }

  if (base) {
    results <- sprintf(
      "\n\n%s Base - %s {.tabset .tabset-pills .tabset-circle}\n\n",
      paste(rep("#", depth + 1), collapse = ""),
      basename(dir_name)
    ) %>%
      write(old_text = results, write_flag = write_flag)
    depth <- depth + 1
  }

  showtype_template <- paste0(
    "\n\n", paste(rep("#", depth + 1), collapse = ""), " %s\n\n"
  )
  figname_template <- paste0(
    "\n\n", paste(rep("#", depth + 2), collapse = ""), " %s\n\n"
  )
  invisible_header <- paste0(
    "\n\n", paste(rep("#", depth + 3), collapse = ""),
    " {.tabset .tabset-pills}\n\n"
  )
  plt_template <- paste0(
    "\n\n", paste(rep("#", depth + 4), collapse = ""), " %s\n\n"
  )

  if (params$use_icons) {
    if (params$use_vmodern) {
      evaluator_label <- "`r fontawesome::fa('table', fill = 'white')`"
      visualizer_label <- "`r fontawesome::fa('chart-bar', fill = 'white')`"
      code_label <- "`r fontawesome::fa('code', fill = 'white')`"
    } else {
      evaluator_label <- "`r fontawesome::fa('table')`"
      visualizer_label <- "`r fontawesome::fa('chart-bar')`"
      code_label <- "`r fontawesome::fa('code')`"
    }
  } else {
    evaluator_label <- "Evaluators"
    visualizer_label <- "Visualizers"
    code_label <- "Varying Parameters"
  }

  exp_fname <- file.path(dir_name, "experiment.rds")
  eval_fname <- file.path(dir_name, "eval_results.rds")
  viz_fname <- file.path(dir_name, "viz_results.rds")

  exp <- get_results(exp_fname)
  eval_results <- get_results(eval_fname)
  viz_results <- get_results(viz_fname)

  if (!is.null(eval_results) && params$show_eval) {
    results <- write(
      sprintf(showtype_template, evaluator_label),
      old_text = results, write_flag = write_flag
    )

    eval_names <- get_object_order(names(eval_results), params$eval_order)
    for (eval_name in eval_names) {
      evaluator <- exp$get_evaluators()[[eval_name]]
      if (evaluator$doc_show) {
        if (verbose >= 1) {
          inform(paste0(paste(rep(" ", depth + 1), collapse = ""), eval_name))
        }
        results <- write(
          sprintf(figname_template, eval_name),
          old_text = results, write_flag = write_flag
        )
        if (is.null(evaluator$doc_nrows)) {
          eval_results_show <- eval_results[[eval_name]]
        } else {
          keep_rows <- 1:min(evaluator$doc_nrows, nrow(eval_results[[eval_name]]))
          eval_results_show <- eval_results[[eval_name]][keep_rows, ]
          if (nrow(eval_results[[eval_name]]) > evaluator$doc_nrows) {
            omitted_nrows <- nrow(eval_results[[eval_name]]) - evaluator$doc_nrows
            results <- write(
              sprintf(
                "Showing preview of %s results. %s rows have been omitted.\n\n",
                eval_name, omitted_nrows
              ),
              old_text = results, write_flag = write_flag
            )
          }
        }
        if (write_flag) {
          results <- sprintf(
            "show_results('%s', '%s', 'evaluator')", dir_name, eval_name
          ) %>%
            write_code_chunk(chunk_args = "results = 'asis'") %>%
            write(old_text = results, write_flag = write_flag)
        } else {
          do.call(
            vthemes::pretty_DT,
            c(list(eval_results_show), evaluator$doc_options)
          ) %>%
            vthemes::subchunkify(i = chunk_idx)
          chunk_idx <<- chunk_idx + 1
        }
      }
    }
  }

  if (!is.null(viz_results) && params$show_viz) {
    results <- write(
      sprintf(showtype_template, visualizer_label),
      old_text = results, write_flag = write_flag
    )

    viz_names <- get_object_order(names(viz_results), params$viz_order)
    for (viz_name in viz_names) {
      visualizer <- exp$get_visualizers()[[viz_name]]
      if (visualizer$doc_show) {
        if (verbose >= 1) {
          inform(paste0(paste(rep(" ", depth + 1), collapse = ""), viz_name))
        }
        results <- write(
          sprintf(figname_template, viz_name),
          invisible_header,
          old_text = results, write_flag = write_flag
        )
        plts <- viz_results[[viz_name]]
        if (!inherits(plts, "list")) {
          plts <- list(plt = plts)
        }
        if (is.null(names(plts))) {
          names(plts) <- 1:length(plts)
        }
        for (plt_name in names(plts)) {
          if (length(plts) != 1) {
            results <- write(
              sprintf(plt_template, plt_name),
              old_text = results, write_flag = write_flag
            )
          }
          plt <- plts[[plt_name]]
          is_plot <- inherits(plt, "plotly") || 
            inherits(plt, "gg") || 
            inherits(plt, "ggplot")
          
          if (params$use_vmodern && is_plot) {
            chunk_args <- "fig.height = %s, fig.width = %s, out.width = '100%%', add.panel = TRUE"
            add_class <- "panel panel-default padded-panel"
          } else {
            chunk_args <- "fig.height = %s, fig.width = %s, out.width = '100%%'"
            add_class <- NULL
          }
          
          if (write_flag) {
            results <- sprintf(
              "show_results('%s', '%s', 'visualizer')", dir_name, viz_name
            ) %>%
              write_code_chunk(
                chunk_args = sprintf(
                  chunk_args,
                  visualizer$doc_options$height, visualizer$doc_options$width
                )
              ) %>%
              write(old_text = results, write_flag = write_flag)
          } else {
            vthemes::subchunkify(plt,
              i = chunk_idx,
              fig_height = visualizer$doc_options$height,
              fig_width = visualizer$doc_options$width,
              other_args = "out.width = '100%'", 
              add_class = add_class
            )
            chunk_idx <<- chunk_idx + 1
          }
        }
      }
    }
  }

  if (!is.null(exp) && params$show_code) {
    if ((length(exp$get_vary_across()$dgp) != 0) ||
        (length(exp$get_vary_across()$method) != 0)) {
      results <- write(
        sprintf(showtype_template, code_label),
        "\n\n**Parameter Values**\n\n",
        old_text = results, write_flag = write_flag
      )
      if (write_flag) {
        results <- sprintf(
          "show_results('%s', NULL, 'vary_params')", dir_name
        ) %>%
          write_code_chunk(chunk_args = "max.height='200px'") %>%
          write(old_text = results, write_flag = write_flag)
      } else {
        vthemes::subchunkify(exp$get_vary_across(),
          chunk_idx,
          other_args = "max.height='200px'"
        )
        chunk_idx <<- chunk_idx + 1
      }
    }
  }

  return(results)
}

#' Displays output of experiment for all of its (saved) descendants
#'
#' @param dir_name name of parent experiment directory
#' @param depth placeholder for recursion; should not be messed with
#' @param write_flag Boolean indicating whether to write text to a file
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @param write_filename Name of file to write to if write_flag = TRUE
#' @param ... other arguments to pass into show_results()
show_descendant_results <- function(dir_name, depth = 1, write_flag = FALSE,
                                    write_filename = NULL, ...) {
  children <- list.dirs(dir_name, recursive = FALSE)
  if (length(children) == 0) {
    return()
  }
  for (child_idx in 1:length(children)) {
    child <- children[child_idx]
    if (!experiment_exists(child, recursive = TRUE)) {
      next
    }
    if (experiment_exists(child, recursive = FALSE) &&
      (experiment_exists(list.dirs(child, recursive = TRUE)[-1]) ||
        (depth == 1))) {
      base <- TRUE
    } else {
      base <- FALSE
    }
    results <- show_results(child, depth, base, write_flag = write_flag, ...)
    if (write_flag) {
      write_to_file(path = write_filename, results)
    }
    show_descendant_results(child, depth + 1, write_flag, write_filename, ...)
  }
}

#' Clean output file (e.g., remove excessive blank lines)
#'
#' @param path Path to output file
clean_file <- function(path) {
  storelines <- readLines(path)
  rle_out <- rle(storelines == "")
  line_ids <- which((rle_out$lengths > 2) & rle_out$values)
  keep_lines <- rep(TRUE, length(storelines))
  for (line_id in line_ids) {
    num_blank <- rle_out$lengths[line_id]
    line_ptr <- sum(rle_out$lengths[1:line_id])
    # only allow for max of two consecutive blank lines
    keep_lines[(line_ptr - num_blank + 3):line_ptr] <- FALSE
  }
  writeLines(storelines[keep_lines], path)
}

#' Insert lines to add extra resources (css/js) for simChef R Markdown theme
#' 
#' @param path Path to output file
insert_simChef_resources <- function(path) {
  storelines <- readLines(path)
  pattern <- "<Insert extra simChef resources here>"
  replace <- sprintf(
    '<script src="%s"></script>\n\n<link rel="stylesheet" href="%s">', 
    system.file("rmd", "js", "simchefNavClass.js",
                package = utils::packageName()),
    system.file("rmd", "css", "simchef.css",
                package = utils::packageName())
  ) 
  storelines[storelines == pattern] <- replace
  writeLines(storelines, path)
}

#' Remove lines with simChef R Markdown theme-specific code
#' 
#' @param path Path to output file
remove_simChef_resources <- function(path) {
  storelines <- readLines(path)
  
  pattern <- "add.panel = function"
  line_id <- which(stringr::str_detect(storelines, pattern))
  remove_lines <- (line_id - 2):(line_id + 5)
  storelines <- storelines[-remove_lines]
  
  pattern <- "<Insert extra simChef resources here>"
  remove_lines <- which(stringr::str_detect(storelines, pattern))
  storelines <- storelines[-remove_lines]
  
  writeLines(storelines, path)
}

```

```{r}
if (params$write) {
  if (params$use_vmodern) {
    insert_simChef_resources(write_filename)
  } else {
    remove_simChef_resources(write_filename)
  }
} else {
  if (params$use_vmodern) {
    htmltools::HTML('<script src="js/simchefNavClass.js"></script>\n\n<link rel="stylesheet" href="css/simchef.css">')
  }
}
```


# Simulation Experiment Recipe {.tabset .tabset-vmodern}

## Objectives

```{r objectives, results = "asis"}
if (params$use_vmodern) {
  objectives <- write(
    "\n\n<div class='panel panel-default padded-panel'>\n\n",
    pasteMd(file.path(doc_dir, "objectives.md")),
    "\n\n</div>\n\n",
    write_flag = params$write
  )
} else {
  objectives <- write(
    pasteMd(file.path(doc_dir, "objectives.md")),
    write_flag = params$write
  )
}
if (params$write) {
  write_to_file(path = write_filename, "\n\n## Objectives\n\n", objectives)
}
```

## Data Generation

```{r dgps, results = "asis"}
dgp_recipe <- show_recipe(field_name = "dgp", write_flag = params$write)
if (params$write) {
  write_to_file(path = write_filename, "\n\n## Data Generation\n\n", dgp_recipe)
}
```

## Methods and Evaluation

### Methods

```{r methods, results = "asis"}
method_recipe <- show_recipe(field_name = "method", write_flag = params$write)
if (params$write) {
  write_to_file(
    path = write_filename,
    "\n\n## Methods and Evaluation\n\n", "\n\n### Methods\n\n",
    method_recipe
  )
}
```

### Evaluation

```{r evaluators, results = "asis"}
eval_recipe <- show_recipe(field_name = "evaluator", write_flag = params$write)
if (params$write) {
  write_to_file(path = write_filename, "\n\n### Evaluation\n\n", eval_recipe)
}
```

## Visualizations

```{r visualizers, results = "asis"}
viz_recipe <- show_recipe(field_name = "visualizer", write_flag = params$write)
if (params$write) {
  write_to_file(path = write_filename, "\n\n## Visualizations\n\n", viz_recipe)
}
```



```{r res, results = "asis"}
if (params$verbose > 0) {
  inform(sprintf("Creating R Markdown report for %s...", params$sim_name))
}

# show results
if (experiment_exists(params$sim_path)) {
  base_header <- write(
    sprintf("\n\n# Base %s \n\n", params$sim_name),
    "\n\n## {.tabset .tabset-pills .tabset-circle}\n\n",
    write_flag = params$write
  )
  base_results <- show_results(
    params$sim_path,
    depth = 2, base = FALSE, show_header = FALSE,
    verbose = params$verbose, write_flag = params$write
  )

  if (params$write) {
    write_to_file(path = write_filename, base_header, base_results)
  }
}

show_descendant_results(
  params$sim_path,
  verbose = params$verbose,
  write_flag = params$write, 
  write_filename = write_filename
)
```



```{r}
if (params$write) {
  clean_file(path = write_filename)
}
```
