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run_limma_splines()

Source: R/run_limma_splines.R
run_limma_splines.Rd

This is the core function, which performs a limma spline analysis to identify significant time-dependent changes in features (e.g., proteins) within an omics time-series dataset. It evaluates features within each condition level and between levels by comparing average differences and interactions between time and condition.

Usage

run_limma_splines(splineomics)

Arguments

splineomics

An S3 object of class `SplineOmics` that contains the following elements:

  • data: The matrix of the omics dataset, with the feature names optionally as row headers.

  • rna_seq_data: An optional object containing the preprocessed RNA-seq data, such as the output from `limma::voom` or a similar preprocessing pipeline. This must only be provided when the input is RNA-seq data.

  • meta: A dataframe containing metadata corresponding to the data, must include a 'Time' column and the column specified by condition. The columns of meta describe the meta info, such as the time and condition, and each row corresponds to a column in data, and therefore, contains the meta info for that data column. It is important that meta and data are matched in this way.

  • padjust_method: Statistical method that is used for multiple hypothesis correction. Supported methods include all that are included in the p.adjust() function in R: "holm", "hochberg", "hommel", "bonferroni", "BH" or "fdr", "BY", or "none" for no correction. Default of this package is "BH".

  • design: A character string representing the limma design formula, such as "~ 1 + Phase*Time + Reactor" for an integrated design, or "~ 1 + Time + Reactor" for an isolated design.

  • 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. If set to 0, then the best dof is automatically found with the help of leave-one-out-crossvalidation (loocv). The dof with the lowest error on the loocv is chosen. - `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: Specifies how the design formula is constructed: either `"isolated"` or `"integrated"`.

    - `"isolated"`: Each level is analyzed independently, using only the subset of data corresponding to that level. The design formula does not include the condition variable, since only one condition is present in each subset.

    - `"integrated"`: All levels are analyzed together in a single model, using the full dataset. The design formula includes the condition variable (and optionally interaction terms with it) so that results are estimated jointly across all levels.

  • condition: A character string specifying the column name in meta used to define groups for analysis. The condition column contains the levels of the experiment (such as control and treatment).

  • spline_params: A list of spline parameters used in the analysis, including:

    • spline_type: The type of spline (e.g., "n" for natural splines or "b" for B-splines).

    • dof: Degrees of freedom for the spline.

    • knots: Positions of the internal knots (for B-splines).

    • bknots: Boundary knots (for B-splines).

    • degree: Degree of the spline (for B-splines only).

  • use_array_weights: Boolean flag indicating if the robust fit strategy to deal with heteroscedasticity should be used or not. If set to NULL, then this is handeled implicitly based on the result of the Levene test. If this test is significant for at least 10 then the robust strategy is used. 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).

  • bp_cfg: A named numeric vector specifying the parallelization configuration, with expected names `"n_cores"` and `"blas_threads"`.

    This controls how many **R worker processes** (`n_cores`) and how many **BLAS/OpenBLAS threads per process** (`blas_threads`) should be used during parallel computation.

    If `bp_cfg` is `NULL`, missing, or any of its required fields is `NA`, both `n_cores` and `blas_threads` default to `1`. This effectively disables parallelization and avoids oversubscription of CPU threads.

Value

The SplineOmics object, updated with a list with three elements: - `time_effect`: A list of top tables for each level with the time effect. - `avrg_diff_conditions`: A list of top tables for each comparison between the levels. The comparison is the average difference of the values. - `interaction_condition_time`: A list of top tables for each comparison between levels. The comparison is the interaction between the condition and the time.