liverlipidome: Levels of lipids in the human liver with or without...

In lipidomeR: Integrative Visualizations of the Lipidome

Description

This data set contains levels of 383 named lipids in 88 liver tissue samples.

Usage

data( liverlipidome )

Format

A long-format data frame with 33704 rows and 13 variables:

ID

Participant number

Diagnosis

Diagnosis of the liver: normal, steatosis, non-alcoholic steatohepatitis (NASH), or cirrhosis

Gender

Gender of the participant

BMI

Body-mass-index (BMI) of the participant

Ethnicity

Ethnicity of the participant

Age

Age of the participant

AST

Aspartate aminotransferase blood test (U/l)

ALT

Alanine aminotransferase blood test (U/l)

ALKP

Alkaline phosphatase blood test (U/l)

TBIL

Total bilirubin blood test (mg/dl)

Glucose

Glucose blood test (mg/dl)

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.

Source

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 PR000633. The data can be accessed directly via its Project DOI: 10.21228/M8MW26. This work was supported by NIH grant, U2C- DK119886.

References

Gorden, D. Lee, et al. Biomarkers of NAFLD Progression: a Lipidomics Approach to an Epidemic. J Lip Res. 56(3) 722-36 (2015) (doi: 10.1194/jlr.P056002

Examples

# Load the data set.
data( liverlipidome )
# Convert the data into wide format, where each lipid is one column and
# each sample is one row.
liverlipidome.wide <-
   tidyr::pivot_wider(
       data = liverlipidome,
       names_from = Lipid_Name,
       values_from = Lipid_Level
   )
# Create a mapping of the lipid names.
names.mapping <-
   map_lipid_names( x = unique( liverlipidome$"Lipid_Name" ) )
# Compute the regression models.
result.limma <-
   compute_models_with_limma(
       x = liverlipidome.wide,
       dependent.variables = names.mapping$"Name",
       independent.variables = c( "Diagnosis" ),
       F.test = TRUE # Compute an F-test for a factor variable.
   )
# Compute the F-test.
result.limma <- compute_F_test_with_limma( x = result.limma )
# Print a figure of the F-test.

figure.output <-
  heatmap_lipidome_from_limma(
      x = result.limma,
      names.mapping = names.mapping,
      F.test = TRUE,
      axis.x.carbons = FALSE,
      class.facet = "wrap",
      plot.all = FALSE,
      plot.individual = TRUE,
      scales = "free",
      space = "free"
  )

# Compute pairwise post-hoc comparisons between the factor levels for
# the dependent variables (i.e., lipids) with a significant F-test result.
result.limma <-
   compute_post_hoc_test_with_limma(
       x = result.limma,
       remap.level.names = TRUE
   )
# Print a figure of all post-hoc comparisons.

figure.output <-
    heatmap_lipidome_from_limma(
    x = result.limma$"result.post.hoc.test",
    names.mapping = names.mapping,
    axis.x.carbons = FALSE,
    plot.all = TRUE,
    plot.individual = FALSE,
    scales = "free",
    space = "free"
)

# Specify the contrasts of the post-hoc comparison that will be included
# in the figure.
contrasts.included <-
   c( "DiagnosisSteatosis", "DiagnosisNASH", "DiagnosisCirrhosis" )
# Get the omitted contrasts based on the above definition.
contrasts.omitted <-
   colnames( result.limma$"result.post.hoc.test"$"p.value" )[
       !(
           colnames( result.limma$"result.post.hoc.test"$"p.value" ) %in%
           contrasts.included
       )
   ]
# Find dependent variables (i.e., lipids) that have any significant
# difference.
has.any.significant <-
   apply(
       X =
           result.limma$"result.post.hoc.test"$"p.value"[
               ,
               contrasts.included
           ],
       MAR = 2,
       FUN = p.adjust,
       method = "BH"
   )
has.any.significant <-
   rownames(
       has.any.significant[
           apply(
               X = has.any.significant < 0.05,
               MAR = 1,
               FUN = any
           ),
       ]
   )
# Include in the figure only lipid classes that have at least four
# significant differences.
classes.included <-
   names(
       which(
           table(
               names.mapping[
                   make.names( has.any.significant ), "Class"
               ]
           ) > 4
       )
   )
classes.omitted <- unique( names.mapping$"Class" )
classes.omitted <-
   classes.omitted[ !( classes.omitted ) %in% classes.included ]
# Print a figure of the selected post-hoc-comparisons.
figure.output <-
   heatmap_lipidome_from_limma(
       x = result.limma$"result.post.hoc.test",
       names.mapping = names.mapping,
       axis.x.carbons = FALSE,
       omit.class = classes.omitted,
       omit.factor = contrasts.omitted,
       plot.all = TRUE,
       plot.individual = FALSE,
       scales = "free",
       space = "free"
   )

lipidomeR documentation built on March 26, 2020, 5:32 p.m.