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Plot feature trajectories

Source: R/plot_sample_data.R
plot_feature_trajectories.Rd

Plot group means of a set of features from normalized sample-level data.

Usage

plot_feature_trajectories(
  features,
  training_regulated_only = FALSE,
  exclude_outliers = TRUE,
  center = TRUE,
  scale = TRUE,
  title = NULL,
  return_data = FALSE,
  scratchdir = "."
)

Arguments

features

character vector of features to plot in the format 'MotrpacRatTraining6moData::ASSAY_ABBREV;MotrpacRatTraining6moData::TISSUE_ABBREV;feature_ID'

training_regulated_only

bool, whether all input features are training-regulated at 5% FDR. FALSE by default. If TRUE, data is loaded from MotrpacRatTraining6moData::TRAINING_REGULATED_NORM_DATA instead of with load_sample_data(), which dramatically improves performance.

exclude_outliers

bool, whether to remove sample outliers specified by MotrpacRatTraining6moData::OUTLIERS. TRUE by default.

center

bool, whether to center the trajectories. TRUE by default.

scale

bool, whether to scale the trajectories. TRUE by default.

title

optional character, plot title

return_data

bool, whether to return the normalized sample-level data corresponding to features instead of a plot. FALSE by default.

scratchdir

character, local directory in which to download data from the web. Current working directory by default. Only relevant if features includes ATAC or METHYL features.

Value

a ggplot2::ggplot() object if return_data=FALSE or a data frame otherwise

Details

Note that while features in the format 'MotrpacRatTraining6moData::ASSAY_ABBREV;MotrpacRatTraining6moData::TISSUE_ABBREV;feature_ID' are only given for training-regulated features in the data objects provided by MotrpacRatTraining6moData, one could specify non-training-regulated features by concatenating MotrpacRatTraining6moData::ASSAY_ABBREV, MotrpacRatTraining6moData::TISSUE_ABBREV, and feature_ID for features of interest (semi-colon-separated).

Examples

# Pick largest cluster in gastrocnemius 
clust = extract_tissue_sets("SKM-GN", k=1, add_week8=FALSE)
# Extract features 
names(clust)
#> [1] "8w_F1_M1"                              
#> [2] "4w_F1_M1---8w_F1_M1"                   
#> [3] "1w_F1_M1->2w_F1_M1->4w_F1_M1->8w_F1_M1"
features = clust[["1w_F1_M1->2w_F1_M1->4w_F1_M1->8w_F1_M1"]]
plot_feature_trajectories(features)


# Since we're only considering training-regulated features in this example,
# set training_regulated_only to TRUE to make it slightly faster 
plot_feature_trajectories(features, training_regulated_only=TRUE)


# Plot a mix of training-regulated and non-training-regulated features
# Note this takes longer because the original datasets are downloaded 
features = c(features, "TRNSCRPT;SKM-GN;ENSRNOG00000000008")
plot_feature_trajectories(features)
#> Identifying data sets...
#> Compiling sample-level data...
#> ATAC_SKMGN_NORM_DATA_05FDR
#> METAB SKM-GN normalized data from METAB_NORM_DATA_FLAT
#> PHOSPHO_SKMGN_NORM_DATA
#> PROT_SKMGN_NORM_DATA
#> TRNSCRPT_SKMGN_NORM_DATA