Description Usage Arguments Details Value Examples
View source: R/createDesignMatrix.R
Create design matrix for model fitting
1 | createDesignMatrix(experiment_info, cols_design = NULL)
|
experiment_info |
|
cols_design |
Argument specifying the columns of |
Creates a design matrix specifying the models to be fitted. (Alternatively,
createFormula
can be used to generate a model formula instead of a design
matrix.)
The design matrix can then be provided to the differential testing functions, together with the data object and contrast matrix.
The experiment_info
input (which was also previously provided to
prepareData
) should be a data frame containing all factors and covariates
of interest. For example, depending on the experimental design, this may include the
following columns:
group IDs (e.g. groups for differential testing)
block IDs (e.g. patient IDs in a paired design)
batch IDs (batch effects)
continuous covariates
The argument cols_design
specifies which columns in experiment_info
to
include in the design matrix. (For example, there may be an additional column of sample
IDs, which should not be included.) This can be provided as a character vector of
column names, a numeric vector of column indices, or a logical vector. By default, all
columns are included.
Columns of indicator variables (e.g. group IDs, block IDs, and batch IDs) in
experiment_info
must be formatted as factors (otherwise they will be treated as
numeric values). The indicator columns will be expanded into the design matrix format.
The names for each parameter are taken from the column names of experiment_info
.
All factors provided here will be included as fixed effect terms in the design matrix.
Alternatively, to use random effects for some factors (e.g. for block IDs), see
createFormula
; or, depending on the method used, provide them directly to
the differential testing function (testDA_voom
and
testDS_limma
).
design
: Returns a design matrix (numeric matrix), with one row per
sample, and one column per model parameter.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # For a complete workflow example demonstrating each step in the 'diffcyt' pipeline,
# see the package vignette.
# Example: simple design matrix
experiment_info <- data.frame(
sample_id = factor(paste0("sample", 1:4)),
group_id = factor(c("group1", "group1", "group2", "group2")),
stringsAsFactors = FALSE
)
createDesignMatrix(experiment_info, cols_design = "group_id")
# Example: more complex design matrix: patient IDs and batch IDs
experiment_info <- data.frame(
sample_id = factor(paste0("sample", 1:8)),
group_id = factor(rep(paste0("group", 1:2), each = 4)),
patient_id = factor(rep(paste0("patient", 1:4), 2)),
batch_id = factor(rep(paste0("batch", 1:2), 4)),
stringsAsFactors = FALSE
)
createDesignMatrix(experiment_info, cols_design = c("group_id", "patient_id", "batch_id"))
# Example: more complex design matrix: continuous covariate
experiment_info <- data.frame(
sample_id = factor(paste0("sample", 1:4)),
group_id = factor(c("group1", "group1", "group2", "group2")),
age = c(52, 35, 71, 60),
stringsAsFactors = FALSE
)
createDesignMatrix(experiment_info, cols_design = c("group_id", "age"))
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