Package 'microbiomeDASim' reference manual

Title:	Microbiome Differential Abundance Simulation
Description:	A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance.
Authors:	Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson
Maintainer:	Justin Williams <[email protected]>
License:	MIT + file LICENSE
Version:	1.21.0
Built:	2024-11-29 08:28:21 UTC
Source:	https://github.com/bioc/microbiomeDASim

Beta Specification Check

Description

Function for checking that the appopriate beta parameters are specified for each of the mean trend specifications

Usage

form_beta_check(form, beta, IP, timepoints)
form_beta_check(form, beta, IP, timepoints)

Arguments

`form`	character value specifying the type of time trend. Options include 'linear', 'quadratic', 'cubic', 'M', 'W', 'L_up', and 'L_down'.
`beta`	vector specifying the appropriate parameters for functional trend. See details of `mean_trend` for explanation for each form
`IP`	vector specifying the inflection points. See details of `mean_trend` for explanation for each form
`timepoints`	numeric vector specifying the points to fit the functional trend. @keywords internal

Value

Nothing returned unless an error is returned.

Generate Longitduinal Differential Abundance from Multivariate Normal

Description

Generate Longitduinal Differential Abundance from Multivariate Normal

Usage

gen_norm_microbiome(
  features = 10,
  diff_abun_features = 5,
  n_control,
  n_treat,
  control_mean,
  sigma,
  num_timepoints,
  t_interval,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  missing_pct,
  missing_per_subject,
  miss_val = NA,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE,
  asynch_time = FALSE
)
gen_norm_microbiome(
  features = 10,
  diff_abun_features = 5,
  n_control,
  n_treat,
  control_mean,
  sigma,
  num_timepoints,
  t_interval,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  missing_pct,
  missing_per_subject,
  miss_val = NA,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE,
  asynch_time = FALSE
)

Arguments

`features`	numeric value specifying the number of features/microbes to simulate. Default is 10.
`diff_abun_features`	numeric value specifying the number of differentially abundant features. Default is 5.
`n_control`	integer value specifying the number of control individuals
`n_treat`	integer value specifying the number of treated individuals
`control_mean`	numeric value specifying the mean value for control subjects. all control subjects are assummed to have the same population mean value.
`sigma`	numeric value specifying the global population standard deviation for both control and treated individuals.
`num_timepoints`	integer value specifying the number of timepoints per subject.
`t_interval`	numeric vector of length two specifying the interval of time from which to draw observatoins [t_1, t_q]. Assumed to be equally spaced over the interval unless `asynch_time` is set to TRUE.
`rho`	value for the correlation parameter. must be between [0, 1]. see `mvrnorm_corr_gen` for details.
`corr_str`	correlation structure selected. see `mvrnorm_corr_gen` for details.
`func_form`	character value specifying the functional form for the longitduinal mean trend. see `mean_trend` for details.
`beta`	vector value specifying the parameters for the differential abundance function. see `mean_trend` for details.
`IP`	vector specifying any inflection points. depends on the type of functional form specified. see `mean_trend` for details. by default this is set to NULL.
`missing_pct`	numeric value that must be between [0, \1] that specifies what percentage of the individuals will have missing values.
`missing_per_subject`	integer value specifying how many observations per subject should be dropped. note that we assume that all individuals must have baseline value, meaning that the maximum number of `missing_per_subject` is equal to `num_timepoints` - 1.
`miss_val`	value used to induce missingness from the simulated data. by default missing values are assummed to be NA but other common choices include 0.
`dis_plot`	logical argument on whether to plot the simulated data or not. by default plotting is turned off.
`plot_trend`	specifies whether to plot the true mean trend. see `mean_trend` for details.
`zero_trunc`	logical indicator designating whether simulated outcomes should be zero truncated. default is set to TRUE
`asynch_time`	logical indicator designed to randomly sample timepoints over a specified interval if set to TRUE. default is FALSE.

Value

This function returns a list with the following objects

Y The full simulated feature sample matrix where each row represent a feature and each column a sample. Note that the differential and non-differential bugs are marked by row.names

Examples

gen_norm_microbiome(features = 5, diff_abun_features = 2,
               n_control = 10, n_treat = 10, control_mean = 8, sigma = 1,
               num_timepoints = 5, t_interval=c(0, 4), rho = 0.8,
               corr_str = "compound", func_form = "linear", beta =  c(0, 1),
               missing_pct = 0.3, missing_per_subject = 2)

gen_norm_microbiome(features = 5, diff_abun_features = 2,
               n_control = 10, n_treat = 10, control_mean = 8, sigma = 1,
               num_timepoints = 5, t_interval=c(0, 4), rho = 0.8,
               corr_str = "compound", func_form = "linear", beta =  c(0, 1),
               missing_pct = 0.3, missing_per_subject = 2)

Generate Longitduinal Differential Abundance from Multivariate Normal with Observed Data

Description

Generate Longitduinal Differential Abundance from Multivariate Normal with Observed Data

Usage

gen_norm_microbiome_obs(
  features = 10,
  diff_abun_features = 5,
  id,
  time,
  group,
  ref,
  control_mean,
  sigma,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE
)
gen_norm_microbiome_obs(
  features = 10,
  diff_abun_features = 5,
  id,
  time,
  group,
  ref,
  control_mean,
  sigma,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE
)

Arguments

`features`	numeric value specifying the number of features/microbes to simulate. Default is 10.
`diff_abun_features`	numeric value specifying the number of differentially abundant features. Default is 5.
`id`	vector of length `N` that identifies repeated measurements for each unit
`time`	vector of length `N` that determines when values will be sampled for each unit
`group`	factor vector with two levels indicating the group assignment for each respective id
`ref`	character value identifying which group value to treat as control and which value to treat as treatment
`control_mean`	numeric value specifying the mean value for control subjects. all control subjects are assummed to have the same population mean value.
`sigma`	numeric value specifying the global population standard deviation for both control and treated individuals.
`rho`	value for the correlation parameter. must be between [0, 1]. see `mvrnorm_corr_gen` for details.
`corr_str`	correlation structure selected. see `mvrnorm_corr_gen` for details.
`func_form`	character value specifying the functional form for the longitduinal mean trend. see `mean_trend` for details.
`beta`	vector value specifying the parameters for the differential abundance function. see `mean_trend` for details.
`IP`	vector specifying any inflection points. depends on the type of functional form specified. see `mean_trend` for details. by default this is set to NULL.
`dis_plot`	logical argument on whether to plot the simulated data or not. by default plotting is turned off.
`plot_trend`	specifies whether to plot the true mean trend. see `mean_trend` for details.
`zero_trunc`	logical indicator designating whether simulated outcomes should be zero truncated. default is set to TRUE

Value

This function returns a list with the following objects

Y The full simulated feature sample matrix where each row represent a feature and each column a sample. Note that the differential and non-differential bugs are marked by row.names

Examples

set.seed(011520)
id_list <- lapply(seq_len(60), function(i){
obs <- sample(5:10, size=1)
id_rep <- rep(i, obs)
})

time_interval <- c(0, 10)
time_list <- lapply(id_list, function(x){
time_len <- length(x)
times <- runif(time_len, min=time_interval[1], max=time_interval[2])
times <- times[order(times)]
})

group_list <- lapply(id_list, function(x){
group_len <- length(x)
tx_ind <- sample(seq_len(2), 1)
tx_group <- ifelse(tx_ind==1, "Control", "Treatment")
groups <- rep(tx_group, group_len)
})
id <- unlist(id_list)
group <- factor(unlist(group_list), levels = c("Control", "Treatment"))
time <- unlist(time_list)

# control times
ct <- unlist(lapply(unique(id[group=="Control"]), function(x){
length(id[id==x])
}))

tt <- unlist(lapply(unique(id[group=="Treatment"]), function(x){
length(id[id==x])
}))
mean(ct)
mean(tt)

gen_norm_microbiome_obs(features=4, diff_abun_features=2,
id=id, time=time, group=group, ref="Control", control_mean=2,
               sigma=1, rho=0.7, corr_str="compound", func_form="L_up",
               beta=1, IP=5, zero_trunc=TRUE)

set.seed(011520)
id_list <- lapply(seq_len(60), function(i){
obs <- sample(5:10, size=1)
id_rep <- rep(i, obs)
})

time_interval <- c(0, 10)
time_list <- lapply(id_list, function(x){
time_len <- length(x)
times <- runif(time_len, min=time_interval[1], max=time_interval[2])
times <- times[order(times)]
})

group_list <- lapply(id_list, function(x){
group_len <- length(x)
tx_ind <- sample(seq_len(2), 1)
tx_group <- ifelse(tx_ind==1, "Control", "Treatment")
groups <- rep(tx_group, group_len)
})
id <- unlist(id_list)
group <- factor(unlist(group_list), levels = c("Control", "Treatment"))
time <- unlist(time_list)

# control times
ct <- unlist(lapply(unique(id[group=="Control"]), function(x){
length(id[id==x])
}))

tt <- unlist(lapply(unique(id[group=="Treatment"]), function(x){
length(id[id==x])
}))
mean(ct)
mean(tt)

gen_norm_microbiome_obs(features=4, diff_abun_features=2,
id=id, time=time, group=group, ref="Control", control_mean=2,
               sigma=1, rho=0.7, corr_str="compound", func_form="L_up",
               beta=1, IP=5, zero_trunc=TRUE)

Spaghetti Plots using `ggplot2`

Description

This function allows the user to create spaghetti plots for individuals with time varying covariates. You can also break this down into subgroups to analyze different trentds.

Usage

ggplot_spaghetti(
  y,
  id,
  time,
  alpha = 0.2,
  method = "loess",
  jit = 0,
  group = NULL
)
ggplot_spaghetti(
  y,
  id,
  time,
  alpha = 0.2,
  method = "loess",
  jit = 0,
  group = NULL
)

Arguments

`y`	This is the y-axis parameter to specify. Generally it is a continuous variable.
`id`	This is the id parameter that identifies the unique individuals or units.
`time`	This is the time vector and must be numeric.
`alpha`	Scalar value between [0,1] that specifies the transparencey of the lineplots.
`method`	Character value that specifies which type of method to use for fitting. Optional methods come from `geom_smooth` function.
`jit`	Scalar value that specifies how much you want to jitter each individual observation. Useful if many of the values share the same y values at a time point.
`group`	Specifies a grouping variable to be used, and will plot it by color on one single plot.

Details

Note that the data must be in long format.

Value

Plots a time series data by each individual/unit with group trends overlayed.

Examples

library(ggplot2)
num_subjects_per_group <- 15
sim_obj <- mvrnorm_sim(n_control=num_subjects_per_group,
                       n_treat=num_subjects_per_group,
                       control_mean=5, sigma=1, num_timepoints=5,
                       t_interval = c(0, 4),
                       rho=0.95, corr_str='ar1', func_form='linear',
                       beta=c(0, 0.25),
                       missing_pct=0.6, missing_per_subject=2)

with(sim_obj$df, suppressWarnings(ggplot_spaghetti(y=Y_obs, id=ID, time=time,
                                                  jit=0.1, group=group)))+
  labs(title="Simulated Microbiome Data from Multivariate Normal",
       y="Normalized Reads", x="Time") +
  scale_linetype_manual(values=c("solid","dashed"), name="Group") +
  scale_color_manual(values=c("#F8766D", "#00BFC4"), name="Group")

library(ggplot2)
num_subjects_per_group <- 15
sim_obj <- mvrnorm_sim(n_control=num_subjects_per_group,
                       n_treat=num_subjects_per_group,
                       control_mean=5, sigma=1, num_timepoints=5,
                       t_interval = c(0, 4),
                       rho=0.95, corr_str='ar1', func_form='linear',
                       beta=c(0, 0.25),
                       missing_pct=0.6, missing_per_subject=2)

with(sim_obj$df, suppressWarnings(ggplot_spaghetti(y=Y_obs, id=ID, time=time,
                                                  jit=0.1, group=group)))+
  labs(title="Simulated Microbiome Data from Multivariate Normal",
       y="Normalized Reads", x="Time") +
  scale_linetype_manual(values=c("solid","dashed"), name="Group") +
  scale_color_manual(values=c("#F8766D", "#00BFC4"), name="Group")

Function for Generating Various Longitudinal Mean Trends

Description

In order to investigate different functional forms of longitudinal differential abundance we allow the mean time trend to take a variety of forms. These functional forms include linear, quadratic, cubic, M, W, L_up, or L_down. For each form the direction/concavity/fold change can be specified using the beta parameter.

Usage

mean_trend(
  timepoints,
  form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  plot_trend = FALSE
)
mean_trend(
  timepoints,
  form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  plot_trend = FALSE
)

Arguments

`timepoints`	numeric vector specifying the points to fit the functional trend.
`form`	character value specifying the type of time trend. Options include 'linear', 'quadratic', 'cubic', 'M', 'W', 'L_up', and 'L_down'.
`beta`	vector specifying the appropriate parameters for the equation. In the case of 'linear', beta should be a two-dimensional vector specifying the intercept and slope. See details for the further explanation of the beta value for each form.
`IP`	vector specifying the inflection points where changes occur for functional forms M, W, and L trends.
`plot_trend`	logical value indicating whether a plot should be produced for the time trend. By default this is set to TRUE.

Details

Linear Form Notes:

$f(x)=\beta_0+\beta_{1}x+\beta_{2}x^2$

Sign of $\beta_1$ determines whether the trend is increasing (+) or decreasing (-)

Quadratic Form Notes:

$f(x)=\beta_0+\beta_{1}x+\beta_{2}x^2$

Critical point for quadratic function occurs at the point $\frac{-\beta_1}{2\beta_2}$
$\beta_2$ determines whether the quadratic is concave up (+) or concave down (-)

Cubic Form Notes:

$f(x)=\beta_0+\beta_{1}x+\beta_{2}x^2+\beta_{3}x^{3}$

Point of Inflection for cubic function occurs $\frac{-\beta_{2}}{(3\beta_{3})}$
Critical points for cubic function occur at $\frac{-\beta_{2}\pm\sqrt{\beta_2^{2}-3\beta_{1}\beta_{3}}}{3\beta_3}$
Can generate piecewise linear trends, i.e. 'V' form, by placing either one of the IP points outside of the timepoints specified

M/W Form Notes:

Must specify beta as ( $\beta_0$ , $\beta_1$ ) and IP as ( $IP_1$ , $IP_2$ , $IP_3$ )
This form should be specified with an initial intercept, $\beta_0$ , and slope, $\beta_1$ , that will connect to the first point of change (IP) specified.
Subsequent slopes are constructed such that the mean value at the second IP value and final timepoint are 0
The mean value at the third IP is set to be equal to the calculcated mean value at the first IP based on the specified intercept and slope.
$\beta_0$ =intercept, i.e. timepoint when y=0
$\beta_1$ =slope between $\beta_0$ and $IP_1$

L_up Form Notes:

The structure of this form assumes that there is no trend from $t_{1}$ to $IP_{1}$ . Then at the point of change specified, $IP_{1}$ , there occurs a linearly increasing trend with slope equal to $\beta_{slope}$ up to the last specified timepoint $t_{q}$ .

Must specify beta as ( $\beta_{slope}$ ), and must be positive
Specify a single point of change (IP) variable where positive trend will start
IP must be between [ $t_{1}$ , $t_{q}$ ]

L_down Form Notes:

Similarily, the L_down form assumes that there are two region within the range of timepoints. The first region is a decreasing trend and the second region has no trend. The decreasing trend must start with a Y intercept greater than zero, and the slope must be specified as negative. There is one point of change (IP), but this is calculated automatically based on the values of the Y intercept and slope provided, IP= $-\beta_{yintercept}/\beta_{slope}$ .

Must specify beta as ( $\beta_{yintercept}$ , $\beta_{slope}$ ) where $\beta_{yintercept}$ >0 and $\beta_{slope}$ <0
IP variable should be specified as NULL, if value is provided it will be ignored.

Value

This function returns a list of the following

form - character value repeating the form selected

trend - data.frame with the variables mu representing the estimated mean value at timepoints used for fitting the trend

beta - returning the numeric vector used to fit the functional form

Examples

#Quadratic Form
mean_trend(timepoints=seq(0, 6, length.out=20),
               form='quadratic', beta=1/4 * c(-1, 3, -0.5), plot_trend=TRUE)
#M Form
mean_trend(timepoints=seq(0, 10,length.out=100), form='M',
               beta=c(0, 5), IP=10 * c(1/4, 2/4, 3/4), plot_trend=TRUE)
#in this case the IP points are selected so that peaks are evenly
#distributed but this does not have to be true in general

#L_up Form
mean_trend(timepoints=seq(0, 10, length.out=100), form='L_up',
           beta=1, IP=5, plot_trend=TRUE)

#L_down Form
mean_trend(timepoints=seq(0, 10,length.out=100), form='L_down',
           beta=c(4, -0.5), IP=NULL, plot_trend=TRUE)

#Quadratic Form
mean_trend(timepoints=seq(0, 6, length.out=20),
               form='quadratic', beta=1/4 * c(-1, 3, -0.5), plot_trend=TRUE)
#M Form
mean_trend(timepoints=seq(0, 10,length.out=100), form='M',
               beta=c(0, 5), IP=10 * c(1/4, 2/4, 3/4), plot_trend=TRUE)
#in this case the IP points are selected so that peaks are evenly
#distributed but this does not have to be true in general

#L_up Form
mean_trend(timepoints=seq(0, 10, length.out=100), form='L_up',
           beta=1, IP=5, plot_trend=TRUE)

#L_down Form
mean_trend(timepoints=seq(0, 10,length.out=100), form='L_down',
           beta=c(4, -0.5), IP=NULL, plot_trend=TRUE)

Generate Multivariate Random Normal Longitudinal Data

Description

For this methodology we assume that we draw a set of n independent each with $q_{i}$ observations.

Usage

mvrnorm_corr_gen(
  n,
  obs,
  t,
  mu,
  sigma,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  zero_trunc = TRUE
)
mvrnorm_corr_gen(
  n,
  obs,
  t,
  mu,
  sigma,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  zero_trunc = TRUE
)

Arguments

`n`	integer scalar representing the total number of individuals
`obs`	vector of length `n` specifying the number of observations per indivdiual.
`t`	vector corresponding to the timepoints for each individual.
`mu`	vector specifying the mean value for individuals.
`sigma`	scalar specifying the standard deviation for all observations.
`rho`	numeric scalar value between [0, 1] specifying the amount of correlation between. assumes that the correlation is consistent for all subjects.
`corr_str`	character value specifying the correlation structure. Currently available methods are \'ar1\', \'compound\', and \'ind\' which correspond to first-order autoregressive, compound or equicorrelation, and independence respecitvely.
`zero_trunc`	logical value to specifying whether the generating distribution should come from a multivariate zero truncated normal or an untruncated multivariate normal. by default we assume that zero truncation occurs since this is assummed in our microbiome setting.

Value

This function returns a list with the following objects:

df - data.frame object with complete outcome Y, subject ID, time, group, and outcome with missing data

Y - vector of complete outcome

Mu - vector of complete mean specifications used during simulation

Sigma - block diagonal symmetric matrix of complete data used during simulation

N - total number of observations

Examples

size <- 15
reps <- 4
N <- size*reps
mvrnorm_corr_gen(n=size, obs=rep(reps, size), t=rep(seq_len(4), size),
mu=rep(1, N), sigma=2, rho=0.9, corr_str="ar1")

size <- 15
reps <- 4
N <- size*reps
mvrnorm_corr_gen(n=size, obs=rep(reps, size), t=rep(seq_len(4), size),
mu=rep(1, N), sigma=2, rho=0.9, corr_str="ar1")

Simulate Microbiome Longitudinal Data from Multivariate Random Normal

Description

This function is used in the gen_norm_microbiome call when the user specified the method as mvrnorm.

Usage

mvrnorm_sim(
  n_control,
  n_treat,
  control_mean,
  sigma,
  num_timepoints,
  t_interval,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  missing_pct,
  missing_per_subject,
  miss_val = NA,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE,
  asynch_time = FALSE
)
mvrnorm_sim(
  n_control,
  n_treat,
  control_mean,
  sigma,
  num_timepoints,
  t_interval,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  missing_pct,
  missing_per_subject,
  miss_val = NA,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE,
  asynch_time = FALSE
)

Arguments

`n_control`	integer value specifying the number of control individuals
`n_treat`	integer value specifying the number of treated individuals
`control_mean`	numeric value specifying the mean value for control subjects. all control subjects are assummed to have the same population mean value.
`sigma`	numeric value specifying the global population standard deviation for both control and treated individuals.
`num_timepoints`	either an integer value specifying the number of timepoints per subject or a vector of timepoints for each subject. If supplying a vector the lenght of the vector must equal the total number of subjects.
`t_interval`	numeric vector of length two specifying the interval of time from which to draw observatoins [t_1, t_q]. Assumed to be equally spaced over the interval unless `asynch_time` is set to TRUE.
`rho`	value for the correlation parameter. must be between [0, 1]. see `mvrnorm_corr_gen` for details.
`corr_str`	correlation structure selected. see `mvrnorm_corr_gen` for details.
`func_form`	character value specifying the functional form for the longitduinal mean trend. see `mean_trend` for details.
`beta`	vector value specifying the parameters for the differential abundance function. see `mean_trend` for details.
`IP`	vector specifying any inflection points. depends on the type of functional form specified. see `mean_trend` for details. by default this is set to NULL.
`missing_pct`	numeric value that must be between [0, \1] that specifies what percentage of the individuals will have missing values.
`missing_per_subject`	integer value specifying how many observations per subject should be dropped. note that we assume that all individuals must have baseline value, meaning that the maximum number of `missing_per_subject` is equal to `num_timepoints` - 1.
`miss_val`	value used to induce missingness from the simulated data. by default missing values are assummed to be NA but other common choices include 0.
`dis_plot`	logical argument on whether to plot the simulated data or not. by default plotting is turned off.
`plot_trend`	specifies whether to plot the true mean trend. see `mean_trend` for details.
`zero_trunc`	logical indicator designating whether simulated outcomes should be zero truncated. default is set to TRUE
`asynch_time`	logical indicator designed to randomly sample timepoints over a specified interval if set to TRUE. default is FALSE.

Value

This function returns a list with the following objects:

df - data.frame object with complete outcome Y, subject ID, time, group, and outcome with missing data

Y - vector of complete outcome

Mu - vector of complete mean specifications used during simulation

Sigma - block diagonal symmetric matrix of complete data used during simulation

N - total number of observations

miss_data - data.frame object that lists which ID's and timepoints were randomly selected to induce missingness

Y_obs - vector of outcome with induced missingness

Examples

num_subjects_per_group <- 20
sim_obj <- mvrnorm_sim(n_control=num_subjects_per_group,
                       n_treat=num_subjects_per_group,
                       control_mean=5, sigma=1, num_timepoints=5,
                       t_interval=c(0, 4), rho=0.95, corr_str='ar1',
                       func_form='linear', beta=c(0, 0.25),
                       missing_pct=0.6, missing_per_subject=2)
#checking the output
head(sim_obj$df)

#total number of observations is 2(num_subjects_per_group)(num_timeponts)
sim_obj$N

#there should be approximately 60% of the IDs with missing observations
length(unique(sim_obj$miss_data$miss_id))/length(unique(sim_obj$df$ID))

#checking the subject covariance structure
sim_obj$Sigma[seq_len(5), seq_len(5)]

num_subjects_per_group <- 20
sim_obj <- mvrnorm_sim(n_control=num_subjects_per_group,
                       n_treat=num_subjects_per_group,
                       control_mean=5, sigma=1, num_timepoints=5,
                       t_interval=c(0, 4), rho=0.95, corr_str='ar1',
                       func_form='linear', beta=c(0, 0.25),
                       missing_pct=0.6, missing_per_subject=2)
#checking the output
head(sim_obj$df)

#total number of observations is 2(num_subjects_per_group)(num_timeponts)
sim_obj$N

#there should be approximately 60% of the IDs with missing observations
length(unique(sim_obj$miss_data$miss_id))/length(unique(sim_obj$df$ID))

#checking the subject covariance structure
sim_obj$Sigma[seq_len(5), seq_len(5)]

Simulate Microbiome Longitudinal Data from Multivariate Random Normal with Observed Data

Description

This function is used in the gen_norm_microbiome_obs call.

Usage

mvrnorm_sim_obs(
  id,
  time,
  group,
  ref,
  control_mean,
  sigma,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE
)
mvrnorm_sim_obs(
  id,
  time,
  group,
  ref,
  control_mean,
  sigma,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE
)

Arguments

`id`	vector of length `N` that identifies repeated measurements for each unit
`time`	vector of length `N` that determines when values will be sampled for each unit
`group`	factor vector with two levels indicating the group assignment for each respective id
`ref`	character value identifying which group value to treat as control and which value to treat as treatment
`control_mean`	numeric value specifying the mean value for control subjects. all control subjects are assummed to have the same population mean value.
`sigma`	numeric value specifying the global population standard deviation for both control and treated individuals.
`rho`	value for the correlation parameter. must be between [0, 1]. see `mvrnorm_corr_gen` for details.
`corr_str`	correlation structure selected. see `mvrnorm_corr_gen` for details.
`func_form`	character value specifying the functional form for the longitduinal mean trend. see `mean_trend` for details.
`beta`	vector value specifying the parameters for the differential abundance function. see `mean_trend` for details.
`IP`	vector specifying any inflection points. depends on the type of functional form specified. see `mean_trend` for details. by default this is set to NULL.
`dis_plot`	logical argument on whether to plot the simulated data or not. by default plotting is turned off.
`plot_trend`	specifies whether to plot the true mean trend. see `mean_trend` for details.
`zero_trunc`	logical indicator designating whether simulated outcomes should be zero truncated. default is set to TRUE

Value

This function returns a list with the following objects:

df - data.frame object with complete outcome Y, subject ID, time, group, and outcome with missing data

Y - vector of complete outcome

Mu - vector of complete mean specifications used during simulation

Sigma - block diagonal symmetric matrix of complete data used during simulation

N - total number of observations

Examples

set.seed(011520)
id_list <- lapply(seq_len(30), function(i){
obs <- sample(seq_len(10), size=1)
id_rep <- rep(i, obs)
})

time_interval <- c(0, 10)
time_list <- lapply(id_list, function(x){
time_len <- length(x)
times <- runif(time_len, min=time_interval[1], max=time_interval[2])
times <- times[order(times)]
})

group_list <- lapply(id_list, function(x){
group_len <- length(x)
tx_ind <- sample(seq_len(2), 1)
tx_group <- ifelse(tx_ind==1, "Control", "Treatment")
groups <- rep(tx_group, group_len)
})
id <- unlist(id_list)
group <- factor(unlist(group_list), levels = c("Control", "Treatment"))
time <- unlist(time_list)

# N=173 total repeated measurements
length(id)

# 15 control and 15 treated subjects
table(group[unique(id)])

# control times
ct <- unlist(lapply(unique(id[group=="Control"]), function(x){
length(id[id==x])
}))

#treatment times
tt <- unlist(lapply(unique(id[group=="Treatment"]), function(x){
length(id[id==x])
}))

# on average the treatment group has one more observation than control
mean(ct)
mean(tt)

mvrnorm_sim_obs(id=id, time=time, group=group, ref="Control", control_mean=2,
               sigma=1, rho=0.7, corr_str="compound", func_form="L_up",
               beta=1, IP=5, plot_trend=TRUE, dis_plot=TRUE, zero_trunc=TRUE)

set.seed(011520)
id_list <- lapply(seq_len(30), function(i){
obs <- sample(seq_len(10), size=1)
id_rep <- rep(i, obs)
})

time_interval <- c(0, 10)
time_list <- lapply(id_list, function(x){
time_len <- length(x)
times <- runif(time_len, min=time_interval[1], max=time_interval[2])
times <- times[order(times)]
})

group_list <- lapply(id_list, function(x){
group_len <- length(x)
tx_ind <- sample(seq_len(2), 1)
tx_group <- ifelse(tx_ind==1, "Control", "Treatment")
groups <- rep(tx_group, group_len)
})
id <- unlist(id_list)
group <- factor(unlist(group_list), levels = c("Control", "Treatment"))
time <- unlist(time_list)

# N=173 total repeated measurements
length(id)

# 15 control and 15 treated subjects
table(group[unique(id)])

# control times
ct <- unlist(lapply(unique(id[group=="Control"]), function(x){
length(id[id==x])
}))

#treatment times
tt <- unlist(lapply(unique(id[group=="Treatment"]), function(x){
length(id[id==x])
}))

# on average the treatment group has one more observation than control
mean(ct)
mean(tt)

mvrnorm_sim_obs(id=id, time=time, group=group, ref="Control", control_mean=2,
               sigma=1, rho=0.7, corr_str="compound", func_form="L_up",
               beta=1, IP=5, plot_trend=TRUE, dis_plot=TRUE, zero_trunc=TRUE)

Convert simulated output to MRexperiment object

Description

In order to allow investigators to more easily incorporate simulated data, this package converts the raw output into an MRexperiment object used in the metagenomeSeq package.

Usage

simulate2MRexperiment(obj, missing = FALSE)
simulate2MRexperiment(obj, missing = FALSE)

Arguments

`obj`	output from either `gen_norm_microbiome` or `mvrnorm_sim`
`missing`	logical indicator for objects from `mvrnorm_sim`. If missing = TRUE then create MRexperiment object with `Y_obs` else use `Y`.

Value

An MRexperiment object

Examples

bug_gen <- gen_norm_microbiome(features=6, diff_abun_features=3,
                               n_control=30, n_treat=20, control_mean=2,
                               sigma=2, num_timepoints=4, t_interval=c(0, 3),
                               rho=0.9, corr_str="compound", func_form="M",
                               beta=c(4, 3), IP=c(2, 3.3, 6),
                               missing_pct=0.2, missing_per_subject=2,
                               miss_val=0, asynch_time=TRUE)
bug_gen_MR <- simulate2MRexperiment(bug_gen)
class(bug_gen_MR)

bug_gen <- gen_norm_microbiome(features=6, diff_abun_features=3,
                               n_control=30, n_treat=20, control_mean=2,
                               sigma=2, num_timepoints=4, t_interval=c(0, 3),
                               rho=0.9, corr_str="compound", func_form="M",
                               beta=c(4, 3), IP=c(2, 3.3, 6),
                               missing_pct=0.2, missing_per_subject=2,
                               miss_val=0, asynch_time=TRUE)
bug_gen_MR <- simulate2MRexperiment(bug_gen)
class(bug_gen_MR)

Convert simulated output to phyloseq object

Description

This function will convert simulated data into a phyloseq object.

Usage

simulate2phyloseq(obj, missing = FALSE)
simulate2phyloseq(obj, missing = FALSE)

Arguments

`obj`	output from either `gen_norm_microbiome` or `mvrnorm_sim`
`missing`	logical indicator for objects from `mvrnorm_sim`. If missing = TRUE then create MRexperiment object with `Y_obs` else use `Y`.

Value

A phyloseq object

Examples

bug_gen <- gen_norm_microbiome(features=6, diff_abun_features=3,
                               n_control=30, n_treat=20, control_mean=2,
                               sigma=2, num_timepoints=4, t_interval=c(0, 3),
                               rho=0.9, corr_str="compound", func_form="M",
                               beta=c(4, 3), IP=c(2, 3.3, 6),
                               missing_pct=0.2, missing_per_subject=2,
                               miss_val=0, asynch_time=TRUE)
bug_gen_phyloseq <- simulate2MRexperiment(bug_gen)
class(bug_gen_phyloseq)

bug_gen <- gen_norm_microbiome(features=6, diff_abun_features=3,
                               n_control=30, n_treat=20, control_mean=2,
                               sigma=2, num_timepoints=4, t_interval=c(0, 3),
                               rho=0.9, corr_str="compound", func_form="M",
                               beta=c(4, 3), IP=c(2, 3.3, 6),
                               missing_pct=0.2, missing_per_subject=2,
                               miss_val=0, asynch_time=TRUE)
bug_gen_phyloseq <- simulate2MRexperiment(bug_gen)
class(bug_gen_phyloseq)

Package 'microbiomeDASim'

Help Index

Beta Specification Check

Description

Usage

Arguments

Value

Generate Longitduinal Differential Abundance from Multivariate Normal

Description

Usage

Arguments

Value

Examples

Generate Longitduinal Differential Abundance from Multivariate Normal with Observed Data

Description

Usage

Arguments

Value

Examples

Spaghetti Plots using ggplot2

Description

Usage

Arguments

Details

Value

Examples

Function for Generating Various Longitudinal Mean Trends

Description

Usage

Arguments

Details

Value

Examples

Generate Multivariate Random Normal Longitudinal Data

Description

Usage

Arguments

Value

Examples

Simulate Microbiome Longitudinal Data from Multivariate Random Normal

Description

Usage

Arguments

Value

Examples

Simulate Microbiome Longitudinal Data from Multivariate Random Normal with Observed Data

Description

Usage

Arguments

Value

Examples

Convert simulated output to MRexperiment object

Description

Usage

Arguments

Value

Examples

Convert simulated output to phyloseq object

Description

Usage

Arguments

Value

Examples

Spaghetti Plots using `ggplot2`