Simulate NR-seq data for multiple replicates of multiple biological conditions
EZSimulate.Rd
EZSimulate()
is a user friendly wrapper to SimulateMultiCondition()
. It
sets convenient defaults so as to quickly generate easy to interpret output.
EZSimulate()
has all of the same parameters as SimulateMultiCondition()
,
but it also has a number of additional parameters that guide its default behavior
and allow you to simulate multi-condition data without specifying the multiple,
sometimes complex, arguments that you would need to specify in SimulateMultiCondition()
to get the same behavior. In particular, users only have to set a single parameter,
nfeatures
(number of features to simulate data for), by default. The EZSimulate()
-unique
parameters ntreatments
and nreps
have default values that guide the simulation in the
case where only nfeatures
is specified. In particular, nreps
of ntreatments
different
conditions will be simulated, with the assumed model log(kdeg) ~ treatment
and log(ksyn) ~ 1
.
In other words, Different kdeg values will be simulated for each treatment level, and ksyn
values will not differ across conditions.
Usage
EZSimulate(
nfeatures,
ntreatments = 2,
nreps = 3,
nctlreps = 1,
metadf = NULL,
mean_formula = NULL,
param_details = NULL,
seqdepth = nfeatures * 2500,
label_time = 2,
pnew = 0.05,
pold = 0.001,
readlength = 200,
Ucont = 0.25,
feature_prefix = "Gene",
dispslope = 5,
dispint = 0.01,
logkdegsdtrend_slope = -0.3,
logkdegsdtrend_intercept = -2.25,
logksynsdtrend_slope = -0.3,
logksynsdtrend_intercept = -2.25,
logkdeg_mean = -1.9,
logkdeg_sd = 0.7,
logksyn_mean = 2.3,
logksyn_sd = 0.7,
logkdeg_diff_avg = 0,
logksyn_diff_avg = 0,
logkdeg_diff_sd = 0.5,
logksyn_diff_sd = 0.5,
pdiff_kd = 0.1,
pdiff_ks = 0,
pdiff_both = 0,
pdo = 0
)
Arguments
- nfeatures
Number of "features" (e.g., genes) for which to simulated data.
- ntreatments
Number of distinct treatments to simulate. This parameter is only relevant if
metadf
is not provided.- nreps
Number of replicates of each treatment to simulate. This parameter is only relevant if
metadf
is not provided- nctlreps
Number of -s4U replicates of each treatment to simulate. This parameter is only relevant if
metadf
is not provided.- metadf
A data frame with the following columns:
sample: Names given to samples to simulate.
- : Any number of columns with any names (not taken by other metadf columns) storing factors by which the samples can be stratified. These can be referenced in
mean_formula
, described below.
These parameters (described more below) can also be included in metadf to specify sample-specific simulation parameter:
seqdepth
label_time
pnew
pold
readlength
Ucont
- mean_formula
A formula object that specifies the linear model used to relate the factors in the
columns ofmetadf
to average log(kdegs) and log(ksyns) in each sample.- param_details
A data frame with one row for each column of the design matrix obtained from
model.matrix(mean_formula, metadf)
that describes how to simulate the linear model parameters. The columns of this data frame are:param: Name of linear model parameter as it appears in the column names of the design matrix from
model.matrix(mean_formula, metadf)
.reference: Boolean; TRUE if you want to treat that parameter as a "reference". This means that all other parameter values that aren't global parameters are set equal to this unless otherwise determined (see
pdiff_*
parameters for how it is determined if a parameter will differ from the reference).global: Boolean; TRUE if you want to treat that parameter as a global parameter. This means that a single value is used for all features.
logkdeg_mean: If parameter is the reference, then its value for the log(kdeg) linear model will be drawn from a normal distribution with this mean. If it is a global parameter, then this value will be used. If it is neither of these, then its value in the log(kdeg) linear model will either be the reference (if there is no difference between this condition's value and the reference) or the reference's value + a normally distributed random variable centered on this value.
logkdeg_sd: sd used for draws from normal distribution as described for
logkdeg_mean
.logksyn_mean: Same as
logkdeg_mean
but for log(ksyn) linear model.logksyn_sd: Same as
logkdeg_sd
but for log(kdeg) linear model.pdiff_ks: Proportion of features whose value of this parameter in the log(ksyn) linear model will differ from the reference's. Should be a number between 0 and 1, inclusive. For example, if
pdiff_ks
is 0.1, then for 10% of features, this parameter will equal the reference parameter + a normally distributed random variable with meanlogksyn_mean
and sdlogksyn_sd
. For the other 90% of features, this parameter will equal the reference.pdiff_kd: Same as
pdiff_ks
but for log(kdeg) linear model.pdiff_both: Proportion of features whose value for this parameter in BOTH the log(kdeg) and log(ksyn) linear models will differ from the reference. Value must be between 0 and min(c(pdiff_kd, pdiff_ks)) in that row.
If param_details is not specified by the user, the first column of the design matrix is assumed to represent the reference parameter, all parameters are assumed to be non-global, logkdeg_mean and logksyn_mean are set to the equivalently named parameter values described below for the reference and
logkdeg_diff_avg
andlogksyn_diff_avg
for all other parameters, logkdeg_sd and logksyn_sd are set to the equivalently named parameter values described below for the reference andlogkdeg_diff_sd
andlogksyn_diff_sd
for all other parameters, and pdiff_kd, pdiff_ks, and pdiff_both are all set to the equivalently named parameter values.- seqdepth
Only relevant if
read_vect
is not provided; in that case, this is the total number of reads to simulate.- label_time
Length of s^4U feed to simulate.
- pnew
Probability that a T is mutated to a C if a read is new.
- pold
Probability that a T is mutated to a C if a read is old.
- readlength
Length of simulated reads. In this simple simulation, all reads are simulated as being exactly this length.
- Ucont
Probability that a nucleotide in a simulated read is a U.
- feature_prefix
Name given to the i-th feature is
paste0(feature_prefix, i)
. Shows up in thefeature
column of the output simulated data table.- dispslope
Negative binomial dispersion parameter "slope" with respect to read counts. See DESeq2 paper for dispersion model used.
- dispint
Negative binomial dispersion parameter "intercept" with respect to read counts. See DESeq2 paper for dispersion model used.
- logkdegsdtrend_slope
Slope for log10(read count) vs. log(kdeg) replicate variability trend
- logkdegsdtrend_intercept
Intercept for log10(read count) vs. log(kdeg) replicate variability trend
- logksynsdtrend_slope
Slope for log10(read count) vs. log(ksyn) replicate variability trend
- logksynsdtrend_intercept
Intercept for log10(read count) vs. log(ksyn) replicate variability trend
- logkdeg_mean
Mean of normal distribution from which reference log(kdeg) linear model parameter is drawn from for each feature if
param_details
is not provided.- logkdeg_sd
Standard deviation of normal distribution from which reference log(kdeg) linear model parameter is drawn from for each feature if
param_details
is not provided.- logksyn_mean
Mean of normal distribution from which reference log(ksyn) linear model parameter is drawn from for each feature if
param_details
is not provided.- logksyn_sd
Standard deviation of normal distribution from which reference log(ksyn) linear model parameter is drawn from for each feature if
param_details
is not provided.- logkdeg_diff_avg
Mean of normal distribution from which non-reference log(kdeg) linear model parameters are drawn from for each feature if
param_details
is not provided.- logksyn_diff_avg
Mean of normal distribution from which reference log(ksyn) linear model parameter are drawn from for each feature if
param_details
is not provided.- logkdeg_diff_sd
Standard deviation of normal distribution from which reference log(kdeg) linear model parameter are drawn from for each feature if
param_details
is not provided.- logksyn_diff_sd
Standard deviation of normal distribution from which reference log(ksyn) linear model parameter are drawn from for each feature if
param_details
is not provided.- pdiff_kd
Proportion of features for which non-reference log(kdeg) linear model parameters differ from the reference.
- pdiff_ks
Proportion of features for which non-reference log(ksyn) linear model parameters differ from the reference.
- pdiff_both
Proportion of features for which BOTH non-reference log(kdeg) and log(ksyn) linear model parameters differ from the reference. ksyns are simulated
- pdo
Dropout rate; think of this as the probability that a s4U containing molecule is lost during library preparation and sequencing. If
pdo
is 0 (default) then there is not dropout.