Load Summary Statistics for Normalized Expression Data

Loads group- and timepoint-level summary statistics for normalized expression data across tissues, molecular assays, and analytical platforms. Summary statistics consist of means and standard deviations computed within randomization groups and timepoints. Limited to the initial acute bout, training data is excluded here.

These datasets are intended for descriptive and exploratory analyses. Sample-level data are not distributed with this package and are available upon request via https://motrpac-data.org.

For metabolomics assays, summary statistics are computed after filtering redundant metabolites; see the Methods section of the associated documentation for details.

Usage

load_summary_stats(
  selected_tissues = "all",
  selected_omes = "all",
  single_matrix = FALSE,
  verbose = TRUE,
  load_clinical = FALSE
)

Arguments

selected_tissues

character; tissues to include. One or more of "adipose", "blood", "muscle", or "all".

selected_omes

character; molecular assays to include. One or more of "transcript-rna-seq", "prot-pr", "prot-ph", "prot-ol", "epigen-atac-seq", "epigen-methylcap-seq", "metab", or "all". Naming any single metabolomics platform is the same as naming "metab": the research platforms live in one stacked object per tissue, so all of them are loaded and the platform is read off the platform column. "metab-t-clinical" is the exception — it is clinical chemistry, is not in the stack, and is gated by load_clinical.

single_matrix

logical; if TRUE, returns a single combined data.frame across all selected tissues and assays. If FALSE (default), returns a nested list.

verbose

logical; toggle verbosity.

load_clinical

logical; whether to include the clinical chemistry omes (clinical_ome_list(): "prot-clinical" and "metab-t-clinical"). FALSE by default, so "all" returns the research omes and nothing changes for callers written before c2.0 split clinical chemistry out. Set TRUE to include them; they are dropped even when named unless it is set.

Value

If single_matrix = FALSE, a nested list of data.frame objects. The top-level names correspond to tissues and the second-level names to assays — the same nesting load_differential_analysis returns, so the two tiers can be walked together. The research metabolomics platforms arrive as a single "metab" element per tissue rather than one element per platform.

If single_matrix = TRUE, a single data.frame containing all selected summary statistics, with missing columns filled as NA.

Each table carries tissue, assay, randomGroupCode, Timepoint, feature_id, Count, Mean and SD. The metabolomics tables carry assay = "metab" and one further column, platform, naming the platform the row was measured on. That is how the *_DA objects are labelled, so a join between the two tiers no longer has to translate between two names for the same ome.

Details

Summary statistics were filtered to only those that qualified for differential analysis. This means for proteomics/phosphoproteomics, samples required a paired n>=3 to be included. See the methods in the manuscript for more information. In epigenetic assays, features were filtered for significant features (FDR<0.05) for file size purposes.

Author

Christopher Jin

Examples

if (FALSE) { # \dontrun{
## Load all summary statistics
sum_stats = load_summary_stats()

## Load metabolomics only. One table per tissue, every platform in it.
metab_stats = load_summary_stats(selected_omes = "metab")
unique(metab_stats[["blood"]][["metab"]][["platform"]])

## Load adipose transcriptomics as a single table
adipose_rna = load_summary_stats(
  selected_tissues = "adipose",
  selected_omes = "transcript-rna-seq",
  single_matrix = TRUE
)
} # }