cancerlipidome: Levels of lipids in benign and malignant breast tumors in...
In lipidomeR: Integrative Visualizations of the Lipidome

Description Usage Format Source References Examples

This data set contains levels of 409 named lipids in 118 human breast tumor tissue samples.

1	data( cancerlipidome )

A long-format data frame with 48262 rows and 7 variables:

ID: Participant number
Group: Diagnosis of the type tumor: benign, cancer, or metastasis
Race: Ethnic background of the participant
Stage: Diagnosis of the stage of the tumor
Type: Sub-type of the breast tumor. IDC: Invasive Ductal Carcinoma
Lipid_Name: Name of the lipid. The names are in the format 'XY(C:D)', where 'XY' is the abbreviation of the lipid class, 'C' is the total number of carbon atoms in the fatty-acid chains, and 'D' is the total number of double-bonds in the fatty acid chains.
Lipid_Level: Measured level of the lipid.

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000742. The data can be accessed directly via its Project DOI: 10.21228/M8RX01. This work was supported by NIH grant, U2C- DK119886.

Purwaha, P., et al. Unbiased lipidomic profiling of triple-negative breast cancer tissues reveals the association of sphingomyelin levels with patient disease-free survival. Metabolites 8, 41 (2018) (doi: 10.3390/metabo8030041)

# Import the data set.
data( cancerlipidome )

# Convert the data into wide format, where each lipid is one column and
# each sample is one row.
cancerlipidome.wide <-
   tidyr::pivot_wider(
       data = cancerlipidome,
       names_from = Lipid_Name,
       values_from = Lipid_Level
   )
# Inspect the data frame.
# View( cancerlipidome.wide )
# Create a mapping of the lipid names.
names.mapping <-
   map_lipid_names( x = unique( cancerlipidome$"Lipid_Name" ) )
# Compute the regression models.
result.limma <-
   compute_models_with_limma(
       x = cancerlipidome.wide,
       dependent.variables = names.mapping$"Name",
       independent.variables = c( "Group" )
   )

# Create a figure of all lipids and factors.
figure.output <-
  heatmap_lipidome_from_limma(
    x = result.limma$"model",
    names.mapping = names.mapping,
    axis.x.carbons = FALSE,
    class.facet = "row",
    plot.all = TRUE,
    plot.individual = FALSE,
    print.figure = TRUE,
    scales = "free",
    space = "free"
  )

# Create individual figures for each factor.
figure.output <-
   heatmap_lipidome_from_limma(
       x = result.limma$"model",
       names.mapping = names.mapping,
       axis.x.carbons = FALSE,
       class.facet = "wrap",
       omit.class = "PA",
       plot.all = FALSE,
       plot.individual = TRUE,
       print.figure = FALSE,
       scales = "free",
       space = "free"
   )
# Print the figure of differences between cancer and benign tumors.
print( figure.output[[ "GroupCancer" ]] )