Plot sample-level data for a feature — plot_feature_normalized

Plot normalized sample-level data for a single feature. Points are mean values across samples in each group, and error bars indicate standard deviations.

Usage

plot_feature_normalized_data(
  assay = NULL,
  tissue = NULL,
  feature_ID = NULL,
  feature = NULL,
  title = NULL,
  add_gene_symbol = FALSE,
  facet_by_sex = FALSE,
  scale_x_by_time = TRUE,
  return_data = FALSE,
  exclude_outliers = TRUE,
  add_adj_p = FALSE,
  ...
)

Arguments

assay: NULL or character, assay abbreviation, one of MotrpacRatTraining6moData::ASSAY_ABBREV
tissue: NULL or character, tissue abbreviation, one of MotrpacRatTraining6moData::TISSUE_ABBREV
feature_ID: NULL or character, MoTrPAC feature identifier or metabolite RefMet ID
feature: NULL or character, unique feature identifier in the format 'MotrpacRatTraining6moData::ASSAY_ABBREV;MotrpacRatTraining6moData::TISSUE_ABBREV;feature_ID' only for training-regulated features at 5% IHW FDR. For redundant differential features, 'feature_ID' is prepended with the specific platform to make unique identifiers. See MotrpacRatTraining6moData::REPEATED_FEATURES for details.. If NULL, assay, tissue, and feature_ID must all be specified.
title: character, plot title. By default, the plot ID is feature. If add_gene_symbol = TRUE, the gene symbol is also added to the plot title.
add_gene_symbol: bool, whether to add corresponding gene symbol to plot title. Default: FALSE
facet_by_sex: bool, whether to facet the plot by sex. If TRUE, lines are colored by tissue. If FALSE, lines are colored by sex. Default: FALSE
scale_x_by_time: bool, whether to scale the x-axis by time. If FALSE, space the time points (0w, 1w, 2w, 4w, 8w) evenly. Default: TRUE
return_data: bool, whether to return data instead of plot. Default: FALSE
exclude_outliers: bool, whether to exclude data from sample outliers. Default: TRUE (see MotrpacRatTraining6moData::OUTLIERS)
add_adj_p: bool, whether to include the training adjusted p-value (AKA selection FDR) in the plot subtitle. Default: TRUE
...: additional arguments passed to load_sample_data()

Value

a ggplot2::ggplot() object or a data frame if return_data = TRUE or NULL if the data cannot be found

Examples

# Plot a differential feature and add gene symbol
plot_feature_normalized_data(feature = "ACETYL;HEART;NP_001003673.1_K477k",
                             add_gene_symbol = TRUE)

                             
# Plot a differential epigenetic feature and facet by sex
plot_feature_normalized_data(feature = "METHYL;HEART;chr20-38798_cluster11",
                             add_gene_symbol = TRUE,
                             facet_by_sex = TRUE)


# Plot a redundant differential feature
plot_feature_normalized_data(assay = "IMMUNO",
                             tissue = "PLASMA",
                             feature_ID = "BDNF",
                             facet_by_sex = TRUE)
#> Multiple features correspond to 'IMMUNO;PLASMA;BDNF'. Plotting them together.

                             
# Plot one measurement of a redundant feature
plot_feature_normalized_data(assay = "IMMUNO",
                             tissue = "PLASMA",
                             feature_ID = "rat-myokine:BDNF",
                             facet_by_sex = TRUE)

                             
# Plot a non-differential feature
plot_feature_normalized_data(assay = "PROT",
                             tissue = "SKM-GN",
                             feature_ID = "YP_665629.1")
#> 'PROT;SKM-GN;YP_665629.1' is not a training-regulated feature. Looking in all sample-level data.
#> PROT_SKMGN_NORM_DATA

                             
# Plot a merged feature from meta-regression,
# don't scale the x-axis, facet by sex, and include the training p-value
plot_feature_normalized_data(assay = "METAB",
                             tissue = "PLASMA",
                             feature_ID = "glucose",
                             scale_x_by_time = FALSE,
                             add_adj_p = TRUE,
                             facet_by_sex = TRUE)
#> Multiple features correspond to 'METAB;PLASMA;glucose'. Plotting them together.
#> Adding differential analysis p-value...
#> METAB_PLASMA_DA_METAREG