Creates a SplineOmics object containing variables that are commonly used across multiple functions in the package. This object is then passed as an argument to the other functions of this package.
Usage
create_splineomics(
data,
meta,
condition,
rna_seq_data = NULL,
annotation = NULL,
report_info = NULL,
meta_batch_column = NULL,
meta_batch2_column = NULL,
feature_name_columns = NULL,
design = NULL,
robust_fit = NULL,
dream_params = NULL,
mode = NULL,
spline_params = NULL,
padjust_method = "BH"
)Arguments
- data
The actual omics data. In the case the rna_seq_data argument is used, still provide this argument. In that case, input the data matrix in here (for example the $E part of the voom object). Assign your feature names as row headers (otherwise, just numbers will be your feature names).
- meta
Metadata associated with the omics data.
- condition
A condition variable.
- rna_seq_data
An object containing the preprocessed RNA-seq data, such as the output from `limma::voom` or a similar preprocessing pipeline. This argument is not subjected to input control. Rather, in that regard it relies on the input control from the `limma::lmfit` function.
- annotation
A dataframe with the feature descriptions of data (optional).
- report_info
A list containing report information such as omics data type, data description, data collection date, analyst name, contact info, and project name (optional).
- meta_batch_column
Column for meta batch information (optional).
- meta_batch2_column
Column for secondary meta batch information (optional).
- feature_name_columns
Character vector containing the column names of the annotation info that describe the features. This argument is used to specify in the HTML report how exactly the feature names displayed above each individual spline plot have been created. Use the same vector that was used to create the row headers for the data matrix!
- design
A design matrix or similar object (optional).
- robust_fit
Boolean flag indicating if the robust fit strategy to deal with heteroscedasticity should be used or not. If ommited (parameter value is NULL) then this is handeled implicitly based on the result of the Wilcoxon signed exact test (tests if there is a significant differene in the variance across samples between at least two levels of the experiment). If this test is significant for at least one pairwise comparison, then the robust strategy is applied. The robust strategy uses the function voomWithQualityWeights for RNA-seq data instead of the normal voom function. For other, non-count-based data, the function limma::arrayWeights is used instead, combined with setting the robust argument to TRUE for the limma::eBayes function. In summary, the strategy employed by those functions is to downweights samples with higher variance. This can be neccessary, because linear models have the assumption of homoscedasticity, which means that the variance is (approx.) the same across all datapoints where the linear model is fitted. If this is violated, then the resulting p-values cannot be trusted (common statistical wisdom).
- dream_params
#' A named list or NULL. When not NULL, it can contain the following named elements: - `dof`: An integer greater than 1, specifying the degrees of freedom for the dream topTable. - `KenwardRoger`: A boolean indicating whether to use the Kenward-Roger approximation for mixed models. Note that random effects are now directly specified in the design formula and not in `dream_params`.
- mode
For the design formula, you must specify either 'isolated' or 'integrated'. Isolated means limma determines the results for each level using only the data from that level. Integrated means limma determines the results for all levels using the full dataset (from all levels).
- spline_params
Parameters for spline functions (optional). Must contain the named elements spline_type, which must contain either the string "n" for natural cubic splines, or "b", for B-splines, the named element degree in the case of B-splines, that must contain only an integer, and the named element dof, specifying the degree of freedom, containing an integer and required both for natural and B-splines.
- padjust_method
Method for p-value adjustment, one of "none", "BH", "BY", "holm", "bonferroni", "hochberg", or "hommel". Defaults to "BH" (Benjamini-Hochberg).