Introduction

lipidr implements a series of functions to facilitate inspection, analysis and visualization of targeted lipidomics datasets. lipidr takes exported Skyline CSV as input, allowing for multiple methods to be analyzed together.

Input

lipidr represents Skyline files as SummarizedExperiment objects, which can easily be integrated with a wide variety of Bioconductor packages. Sample annotations, such as sample group or other clinical information can be loaded.

Quality control & visualization

lipidr generates various plots, such as PCA score plots and box plots, for quality control of samples and measured lipids. Normalization methods with and without internal standards are also supported.

Differential Analysis

Differential analysis can be performed using any of the loaded clinical variables, which can be readily visualized as volcano plots. A novel lipid set enrichment analysis (LSEA) is implemented to detect preferential enrichment of certain lipid classes, chain lengths or saturation patterns. Plots for the visualization of enrichment results are also implemented.

This vignette provides a step by step guide for downstream analysis of targeted lipidomics data, exported from Skyline.

Installation

From Bioconductor

In R console, type:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("lipidr")

From GitHub using devtools

In R console, type:

library(devtools)   
install_github("ahmohamed/lipidr")

Analysis Workflow

Example Study

In this workflow, we will use serum lipidomics data from mice fed a normal or high-fat diet. Mice were fed a normal or high-fat diet (Diet column) and had access to normal drinking water or drinking water containing the bile acid deoxycholic acid (BileAcid column). Lipid peaks were integrated using Skyline and exported as CSV files.

Data Import & manipulation

Exporting files from Skyline

Integrated peaks should be exported from each Skyline file through File => Export => Report. Selecting Transition Results ensures that necessary information is exported from Skyline. Otherwise, you should ensure that peak Area or Height or a similar measure is exported. Regardless of the measure you choose for intensity, you can use lipidr workflow.

Replicates should either be exported, or the Pivot Replicate Name option must be used.

Reading files into R

lipidr can read multiple CSV files from different analysis methods together. Using our example dataset, three Skyline CSV files are used as input to read.skyline.

datadir = system.file("extdata", package="lipidr")
filelist = list.files(datadir, "data.csv", full.names = TRUE) # all csv files

d = read_skyline(filelist)
print(d)
## class: SkylineExperiment 
## dim: 279 58 
## metadata(2): summarized dimnames
## assays(3): Retention.Time Area Background
## rownames(279): 1 2 ... 278 279
## rowData names(23): filename Molecule ... total_cs Class
## colnames(58): S1A S2A ... TQC_11 TQC_12
## colData names(0):

Datasets are represented in R as SummarizedExperiments to facilitate integration other Bioconductor packages.

Adding sample annotation

Sample annotation can be prepared in Excel and saved as CSV. The table should have at least two columns, first indicating sample names and other columns indicating clinical variables.

clinical_file = system.file("extdata", "clin.csv", package="lipidr")

d = add_sample_annotation(d, clinical_file)
colData(d)
## DataFrame with 58 rows and 3 columns
##                   group     Diet BileAcid
##                <factor> <factor> <factor>
## S1A    NormalDiet_water   Normal    water
## S2A    NormalDiet_water   Normal    water
## S3A    NormalDiet_water   Normal    water
## S4A    NormalDiet_water   Normal    water
## S5A    NormalDiet_water   Normal    water
## ...                 ...      ...      ...
## TQC_8                QC       QC       QC
## TQC_9                QC       QC       QC
## TQC_10               QC       QC       QC
## TQC_11               QC       QC       QC
## TQC_12               QC       QC       QC

Data subsetting

It is helpful to imagine SkylineExperiment object as a table with lipid molecules as rows and samples as columns. We can subset this table by selecting specific rows and columns. The general syntax is d[rows, cols].

In the example below we select the first 10 transitions and 10 samples. We can check the rowData and colData.

d_subset = d[1:10, 1:10]
rowData(d_subset)
## DataFrame with 10 rows and 23 columns
##       filename    Molecule Precursor.Mz Precursor.Charge Product.Mz
##    <character> <character>    <numeric>        <integer>  <numeric>
## 1  A1_data.csv     PE 32:0        692.5                1      551.5
## 2  A1_data.csv     PE 32:1        690.5                1      549.5
## 3  A1_data.csv     PE 32:2        688.5                1      547.5
## 4  A1_data.csv     PE 34:1        718.5                1      577.5
## 5  A1_data.csv PE 34:1 NEG        716.5                1        196
## 6  A1_data.csv     PE 34:2        716.5                1      575.5
## 7  A1_data.csv     PE 34:3        714.5                1      573.5
## 8  A1_data.csv     PE 36:0        748.6                1      607.6
## 9  A1_data.csv     PE 36:1        746.6                1      605.6
## 10 A1_data.csv PE 36:1 NEG        744.6                1        196
##    Product.Charge  clean_name     ambig not_matched      istd  class_stub
##         <integer> <character> <logical>   <logical> <logical> <character>
## 1               1     PE 32:0     FALSE       FALSE     FALSE          PE
## 2               1     PE 32:1     FALSE       FALSE     FALSE          PE
## 3               1     PE 32:2     FALSE       FALSE     FALSE          PE
## 4               1     PE 34:1     FALSE       FALSE     FALSE          PE
## 5               1     PE 34:1     FALSE       FALSE     FALSE          PE
## 6               1     PE 34:2     FALSE       FALSE     FALSE          PE
## 7               1     PE 34:3     FALSE       FALSE     FALSE          PE
## 8               1     PE 36:0     FALSE       FALSE     FALSE          PE
## 9               1     PE 36:1     FALSE       FALSE     FALSE          PE
## 10              1     PE 36:1     FALSE       FALSE     FALSE          PE
##         chain1       l_1       s_1      chain2       l_2       s_2
##    <character> <integer> <integer> <character> <integer> <integer>
## 1         32:0        32         0                    NA        NA
## 2         32:1        32         1                    NA        NA
## 3         32:2        32         2                    NA        NA
## 4         34:1        34         1                    NA        NA
## 5         34:1        34         1                    NA        NA
## 6         34:2        34         2                    NA        NA
## 7         34:3        34         3                    NA        NA
## 8         36:0        36         0                    NA        NA
## 9         36:1        36         1                    NA        NA
## 10        36:1        36         1                    NA        NA
##         chain3       l_3       s_3  total_cl  total_cs       Class
##    <character> <logical> <logical> <integer> <integer> <character>
## 1           NA        NA        NA        32         0          PE
## 2           NA        NA        NA        32         1          PE
## 3           NA        NA        NA        32         2          PE
## 4           NA        NA        NA        34         1          PE
## 5           NA        NA        NA        34         1          PE
## 6           NA        NA        NA        34         2          PE
## 7           NA        NA        NA        34         3          PE
## 8           NA        NA        NA        36         0          PE
## 9           NA        NA        NA        36         1          PE
## 10          NA        NA        NA        36         1          PE
colData(d)
## DataFrame with 58 rows and 3 columns
##                   group     Diet BileAcid
##                <factor> <factor> <factor>
## S1A    NormalDiet_water   Normal    water
## S2A    NormalDiet_water   Normal    water
## S3A    NormalDiet_water   Normal    water
## S4A    NormalDiet_water   Normal    water
## S5A    NormalDiet_water   Normal    water
## ...                 ...      ...      ...
## TQC_8                QC       QC       QC
## TQC_9                QC       QC       QC
## TQC_10               QC       QC       QC
## TQC_11               QC       QC       QC
## TQC_12               QC       QC       QC

We can also apply conditional selections (indexing). For example, we can select all quality control samples.

d_qc = d[, d$group == "QC"]
rowData(d_qc)
## DataFrame with 279 rows and 23 columns
##        filename         Molecule Precursor.Mz Precursor.Charge Product.Mz
##     <character>      <character>    <numeric>        <integer>  <numeric>
## 1   A1_data.csv          PE 32:0        692.5                1      551.5
## 2   A1_data.csv          PE 32:1        690.5                1      549.5
## 3   A1_data.csv          PE 32:2        688.5                1      547.5
## 4   A1_data.csv          PE 34:1        718.5                1      577.5
## 5   A1_data.csv      PE 34:1 NEG        716.5                1        196
## ...         ...              ...          ...              ...        ...
## 275 F2_data.csv       PC(P-40:3)        824.6                1      184.1
## 276 F2_data.csv       PC(P-40:4)        822.6                1      184.1
## 277 F2_data.csv       PC(P-40:5)        820.6                1      184.1
## 278 F2_data.csv       PC(P-40:6)        818.6                1      184.1
## 279 F2_data.csv 15:0-18:1(d7) PC      753.615                1     184.07
##     Product.Charge       clean_name     ambig not_matched      istd
##          <integer>      <character> <logical>   <logical> <logical>
## 1                1          PE 32:0     FALSE       FALSE     FALSE
## 2                1          PE 32:1     FALSE       FALSE     FALSE
## 3                1          PE 32:2     FALSE       FALSE     FALSE
## 4                1          PE 34:1     FALSE       FALSE     FALSE
## 5                1          PE 34:1     FALSE       FALSE     FALSE
## ...            ...              ...       ...         ...       ...
## 275              1         PCP-40:3     FALSE       FALSE     FALSE
## 276              1         PCP-40:4     FALSE       FALSE     FALSE
## 277              1         PCP-40:5     FALSE       FALSE     FALSE
## 278              1         PCP-40:6     FALSE       FALSE     FALSE
## 279              1 PC 15:0-18:1(d7)     FALSE       FALSE      TRUE
##      class_stub      chain1       l_1       s_1      chain2       l_2
##     <character> <character> <integer> <integer> <character> <integer>
## 1            PE        32:0        32         0                    NA
## 2            PE        32:1        32         1                    NA
## 3            PE        32:2        32         2                    NA
## 4            PE        34:1        34         1                    NA
## 5            PE        34:1        34         1                    NA
## ...         ...         ...       ...       ...         ...       ...
## 275         PCP        40:3        40         3                    NA
## 276         PCP        40:4        40         4                    NA
## 277         PCP        40:5        40         5                    NA
## 278         PCP        40:6        40         6                    NA
## 279          PC        15:0        15         0        18:1        18
##           s_2      chain3       l_3       s_3  total_cl  total_cs
##     <integer> <character> <logical> <logical> <integer> <integer>
## 1          NA          NA        NA        NA        32         0
## 2          NA          NA        NA        NA        32         1
## 3          NA          NA        NA        NA        32         2
## 4          NA          NA        NA        NA        34         1
## 5          NA          NA        NA        NA        34         1
## ...       ...         ...       ...       ...       ...       ...
## 275        NA          NA        NA        NA        40         3
## 276        NA          NA        NA        NA        40         4
## 277        NA          NA        NA        NA        40         5
## 278        NA          NA        NA        NA        40         6
## 279         1          NA        NA        NA        33         1
##           Class
##     <character>
## 1            PE
## 2            PE
## 3            PE
## 4            PE
## 5            PE
## ...         ...
## 275          PC
## 276          PC
## 277          PC
## 278          PC
## 279          PC
colData(d_qc)
## DataFrame with 12 rows and 3 columns
##           group     Diet BileAcid
##        <factor> <factor> <factor>
## TQC_1        QC       QC       QC
## TQC_2        QC       QC       QC
## TQC_3        QC       QC       QC
## TQC_4        QC       QC       QC
## TQC_5        QC       QC       QC
## ...         ...      ...      ...
## TQC_8        QC       QC       QC
## TQC_9        QC       QC       QC
## TQC_10       QC       QC       QC
## TQC_11       QC       QC       QC
## TQC_12       QC       QC       QC

Note that we leave rows index empty (d[,cols]) to select all lipids. We can also subset based on lipid annotations, selecting a specific class for example.

pc_lipids = rowData(d)$Class %in% c("PC", "PCO", "PCP")
d_pc = d[pc_lipids,]
rowData(d_pc)
## DataFrame with 82 rows and 23 columns
##        filename         Molecule Precursor.Mz Precursor.Charge Product.Mz
##     <character>      <character>    <numeric>        <integer>  <numeric>
## 160 F1_data.csv          PC 30:0        706.5                1      184.1
## 161 F1_data.csv          PC 30:1        704.5                1      184.1
## 162 F1_data.csv          PC 30:2        702.5                1      184.1
## 163 F1_data.csv          PC 32:0        734.6                1      184.1
## 164 F1_data.csv          PC 32:1        732.6                1      184.1
## ...         ...              ...          ...              ...        ...
## 275 F2_data.csv       PC(P-40:3)        824.6                1      184.1
## 276 F2_data.csv       PC(P-40:4)        822.6                1      184.1
## 277 F2_data.csv       PC(P-40:5)        820.6                1      184.1
## 278 F2_data.csv       PC(P-40:6)        818.6                1      184.1
## 279 F2_data.csv 15:0-18:1(d7) PC      753.615                1     184.07
##     Product.Charge       clean_name     ambig not_matched      istd
##          <integer>      <character> <logical>   <logical> <logical>
## 160              1          PC 30:0     FALSE       FALSE     FALSE
## 161              1          PC 30:1     FALSE       FALSE     FALSE
## 162              1          PC 30:2     FALSE       FALSE     FALSE
## 163              1          PC 32:0     FALSE       FALSE     FALSE
## 164              1          PC 32:1     FALSE       FALSE     FALSE
## ...            ...              ...       ...         ...       ...
## 275              1         PCP-40:3     FALSE       FALSE     FALSE
## 276              1         PCP-40:4     FALSE       FALSE     FALSE
## 277              1         PCP-40:5     FALSE       FALSE     FALSE
## 278              1         PCP-40:6     FALSE       FALSE     FALSE
## 279              1 PC 15:0-18:1(d7)     FALSE       FALSE      TRUE
##      class_stub      chain1       l_1       s_1      chain2       l_2
##     <character> <character> <integer> <integer> <character> <integer>
## 160          PC        30:0        30         0                    NA
## 161          PC        30:1        30         1                    NA
## 162          PC        30:2        30         2                    NA
## 163          PC        32:0        32         0                    NA
## 164          PC        32:1        32         1                    NA
## ...         ...         ...       ...       ...         ...       ...
## 275         PCP        40:3        40         3                    NA
## 276         PCP        40:4        40         4                    NA
## 277         PCP        40:5        40         5                    NA
## 278         PCP        40:6        40         6                    NA
## 279          PC        15:0        15         0        18:1        18
##           s_2      chain3       l_3       s_3  total_cl  total_cs
##     <integer> <character> <logical> <logical> <integer> <integer>
## 160        NA          NA        NA        NA        30         0
## 161        NA          NA        NA        NA        30         1
## 162        NA          NA        NA        NA        30         2
## 163        NA          NA        NA        NA        32         0
## 164        NA          NA        NA        NA        32         1
## ...       ...         ...       ...       ...       ...       ...
## 275        NA          NA        NA        NA        40         3
## 276        NA          NA        NA        NA        40         4
## 277        NA          NA        NA        NA        40         5
## 278        NA          NA        NA        NA        40         6
## 279         1          NA        NA        NA        33         1
##           Class
##     <character>
## 160          PC
## 161          PC
## 162          PC
## 163          PC
## 164          PC
## ...         ...
## 275          PC
## 276          PC
## 277          PC
## 278          PC
## 279          PC
colData(d_pc)
## DataFrame with 58 rows and 3 columns
##                   group     Diet BileAcid
##                <factor> <factor> <factor>
## S1A    NormalDiet_water   Normal    water
## S2A    NormalDiet_water   Normal    water
## S3A    NormalDiet_water   Normal    water
## S4A    NormalDiet_water   Normal    water
## S5A    NormalDiet_water   Normal    water
## ...                 ...      ...      ...
## TQC_8                QC       QC       QC
## TQC_9                QC       QC       QC
## TQC_10               QC       QC       QC
## TQC_11               QC       QC       QC
## TQC_12               QC       QC       QC

For demonstration purposes, we select only 3 lipids classes, Ceramides (Cer), PhosphatidylCholines (PC) and LysoPhosphatidylCholines (LPC). We also BileAcid treated group from our dataset.

lipid_classes = rowData(d)$Class %in% c("Cer", "PC", "LPC")
groups = d$BileAcid != "DCA"
d = d[lipid_classes, groups]

#QC sample subset
d_qc = d[, d$group == "QC"]

Raw Data Quality Check

To ensure data quality, we can look at total lipid intensity as bar chart or distribution of samples as a boxplot.

plot_samples(d, type = "tic", log = TRUE)

We can also look at intensity and retention time distributions for each lipid molecule using plot_molecules(type = boxplot). It is recommended to assess the variation across quality control samples.

plot_molecules(d_qc, "sd", measure = "Retention.Time", log = FALSE)

plot_molecules(d_qc, "cv", measure = "Area")

Or intensity distribution within different lipid classes.

plot_lipidclass(d, "boxplot")

Interactive plots

All lipidr plots can be displayed interactive mode if plotly package is installed. Plot interactivity is disabled by default. To enable interactivity, simple call use_interactive_graphics(). You can turn interactivity back off by use_interactive_graphics(interactive=FALSE).

Summarizing transitions

This step is important when more than one transition is measured per lipid molecule. Multiple transitions are summarized into a single value by either taking the average intensity or the one with highest intensity.

d_summarized = summarize_transitions(d, method = "average")

Normalization

Probabilistic Quotient Normalization (PQN)

The PQN method determines a dilution factor for each sample by comparing the distribution of quotients between samples and a reference spectrum, followed by sample normalization using this dilution factor.

d_normalized = normalize_pqn(d_summarized, measure = "Area", exclude = "blank", log = TRUE)
plot_samples(d_normalized, "boxplot")

By specifying exclude = "blank", blank runs are automatically detected and excluded from the normalization process.

Internal standard normalization

Internal standard normalization corrects lipid class-specific variations between samples. Lipid classes are normalized using corresponding internal standard(s) of the same lipid class. If no corresponding internal standard is found the average of all measured internal standards is used instead.

d_normalized_istd = normalize_istd(d_summarized, measure = "Area", exclude = "blank", log = TRUE)

Multivariate analysis

You can investigate sample variation using either PCA or PCoA (classical MDS).

mvaresults = mva(d_normalized, measure="Area", method="PCA")
plot_mva(mvaresults, color_by="group", components = c(1,2))

Plotting other components is possible by specifying components argument. For example components = c(2,3) plots second and third components.

Supervised multivariate analysis

Supervised multivariate analyses, such as OPLS and OPLS-DA can be performed to determine which lipids are associated with a group (y-variable) of interest. In this example we use “Diet” as grouping, and display the results in a scores plot.

mvaresults = mva(d_normalized, method = "OPLS-DA", group_col = "Diet", groups=c("HighFat", "Normal"))
## OPLS-DA
## 22 samples x 131 variables and 1 response
## standard scaling of predictors and response(s)
##       R2X(cum) R2Y(cum) Q2(cum)  RMSEE pre ort pR2Y  pQ2
## Total    0.658    0.985   0.969 0.0661   1   1 0.05 0.05
plot_mva(mvaresults, color_by="group")

We can also plot the loadings and display important lipids contributing to the separation between different (Diet) groups.

plot_mva_loadings(mvaresults, color_by="Class", top.n=10)

Alternatively, we can extract top N lipids along with their annotations.

top_lipids(mvaresults, top.n=10)
##       filename        Molecule Precursor.Mz Precursor.Charge    clean_name
## 1  F1_data.csv Cer d18:1/C20:0        594.7                1 Cer 18:1/20:0
## 2  F1_data.csv Cer d18:0/C20:0        596.7                1 Cer 18:0/20:0
## 3  F1_data.csv Cer d18:1/C16:0        538.7                1 Cer 18:1/16:0
## 4  F1_data.csv Cer d18:1/C18:0        566.7                1 Cer 18:1/18:0
## 5  F1_data.csv Cer d18:1/C22:3        616.7                1 Cer 18:1/22:3
## 6  F1_data.csv Cer d18:1/C20:1        592.7                1 Cer 18:1/20:1
## 7  F2_data.csv      PC(O-38:2)        800.6                1      PCO-38:2
## 8  F2_data.csv      PC(P-34:2)        742.5                1      PCP-34:2
## 9  F1_data.csv Cer d18:1/C20:2        590.7                1 Cer 18:1/20:2
## 10 F1_data.csv Cer d18:1/C24:1        648.7                1 Cer 18:1/24:1
##    ambig not_matched  istd class_stub chain1 l_1 s_1 chain2 l_2 s_2 chain3
## 1  FALSE       FALSE FALSE        Cer   18:1  18   1   20:0  20   0   <NA>
## 2  FALSE       FALSE FALSE        Cer   18:0  18   0   20:0  20   0   <NA>
## 3  FALSE       FALSE FALSE        Cer   18:1  18   1   16:0  16   0   <NA>
## 4  FALSE       FALSE FALSE        Cer   18:1  18   1   18:0  18   0   <NA>
## 5  FALSE       FALSE FALSE        Cer   18:1  18   1   22:3  22   3   <NA>
## 6  FALSE       FALSE FALSE        Cer   18:1  18   1   20:1  20   1   <NA>
## 7  FALSE       FALSE FALSE        PCO   38:2  38   2         NA  NA   <NA>
## 8  FALSE       FALSE FALSE        PCP   34:2  34   2         NA  NA   <NA>
## 9  FALSE       FALSE FALSE        Cer   18:1  18   1   20:2  20   2   <NA>
## 10 FALSE       FALSE FALSE        Cer   18:1  18   1   24:1  24   1   <NA>
##    l_3 s_3 total_cl total_cs Class molrank
## 1   NA  NA       38        1   Cer       1
## 2   NA  NA       38        0   Cer       2
## 3   NA  NA       34        1   Cer       3
## 4   NA  NA       36        1   Cer       4
## 5   NA  NA       40        4   Cer       5
## 6   NA  NA       38        2   Cer       6
## 7   NA  NA       38        2    PC       7
## 8   NA  NA       34        2    PC       8
## 9   NA  NA       38        3   Cer       9
## 10  NA  NA       42        2   Cer      10

Differential analysis

This step of the workflow requires the limma package to be installed.

Normalized and log transformed data should be used.

de_results = de_analysis(
  data=d_normalized, 
  HighFat_water - NormalDiet_water,
  measure="Area"
)
head(de_results)
##           Molecule Class total_cl total_cs  istd MoleculeId
## 1 15:0-18:1(d7) PC    PC       33        1  TRUE          1
## 2  Cer d18:0/C16:0   Cer       34        0 FALSE          2
## 3  Cer d18:0/C18:0   Cer       36        0 FALSE          3
## 4  Cer d18:0/C20:0   Cer       38        0 FALSE          4
## 5  Cer d18:0/C22:0   Cer       40        0 FALSE          5
## 6  Cer d18:0/C24:0   Cer       42        0 FALSE          6
##                           contrast      logFC   AveExpr         t
## 1 HighFat_water - NormalDiet_water -0.4677173 20.835542 -4.774214
## 2 HighFat_water - NormalDiet_water  1.6869029 12.000945  9.712264
## 3 HighFat_water - NormalDiet_water  2.1386448 10.429107 13.701470
## 4 HighFat_water - NormalDiet_water  2.0134743  9.874512 21.535777
## 5 HighFat_water - NormalDiet_water  1.4381768 12.546643 12.368898
## 6 HighFat_water - NormalDiet_water  1.1359000 11.931540  9.177110
##        P.Value    adj.P.Val          B
## 1 5.469750e-05 9.005076e-05  0.8063868
## 2 2.407788e-10 8.126286e-10 13.2791591
## 3 9.661587e-14 6.863533e-13 21.2448643
## 4 1.206308e-18 8.744793e-17 32.6618552
## 5 1.080841e-12 5.404203e-12 18.7869299
## 6 7.953821e-10 2.334274e-09 12.0622733
significant_molecules(de_results)
## $`HighFat_water - NormalDiet_water`
##  [1] "Cer d18:0/C16:0" "Cer d18:0/C18:0" "Cer d18:0/C20:0"
##  [4] "Cer d18:0/C22:0" "Cer d18:0/C24:0" "Cer d18:0/C24:1"
##  [7] "Cer d18:0/C24:2" "Cer d18:1/C16:0" "Cer d18:1/C16:1"
## [10] "Cer d18:1/C18:0" "Cer d18:1/C18:1" "Cer d18:1/C20:0"
## [13] "Cer d18:1/C20:1" "Cer d18:1/C20:2" "Cer d18:1/C22:0"
## [16] "Cer d18:1/C22:1" "Cer d18:1/C22:2" "Cer d18:1/C22:3"
## [19] "Cer d18:1/C22:5" "Cer d18:1/C22:6" "Cer d18:1/C24:1"
## [22] "Cer d18:1/C26:0" "Cer d18:2/C20:0" "PC 36:5"        
## [25] "PC 36:6"         "PC 40:2"         "PC 40:3"        
## [28] "PC 40:8"         "LPC 20:0"        "LPC 20:4"       
## [31] "LPC 22:0"        "LPC 26:0"        "PC(O-34:2)"     
## [34] "PC(O-34:3)"      "PC(O-38:2)"      "PC(O-40:1)"     
## [37] "PC(P-34:1)"      "PC(P-34:2)"      "PC(P-34:3)"     
## [40] "PC(P-36:5)"      "PC(P-38:0)"      "PC(P-38:1)"     
## [43] "PC(P-40:0)"

We can visualize differential analysis results using volcano plots. In this instance we turn off labeling, due to the large number of significantly altered lipids between conditions.

plot_results_volcano(de_results, show.labels = FALSE)

Complex experimental designs

For more complex experimental designs, where one might deal with factor adjustment or interactions, it is recommended to use the de_design function. Users can either provide a design matrix, or a formula to create one.

# Using formula
de_design(
  data=d_normalized,
  design = ~ group, 
  coef="groupHighFat_water", 
  measure="Area")

# Using design matrix
design = model.matrix(~ group, data=colData(d_normalized))
de_design(
  data=d_normalized,
  design = design,
  coef="groupHighFat_water",
  measure="Area")

For more details on creating design matrices for different experimental designs, refer to (Limma User Guide)[https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf] and (edgeR tutorial)[https://www.bioconductor.org/packages/devel/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf].

Enrichment analysis

lipidr automatically generates sets of lipids based on lipid class, chain length and saturation. Measured lipids are then ranked by their fold change, or p-value using results from differential analysis.

enrich_results = lsea(de_results, rank.by = "logFC")
significant_lipidsets(enrich_results)
## $`HighFat_water - NormalDiet_water`
## [1] "Class_Cer" "Class_PC"

Visualization of enrichment analysis results. The enriched lipid classes and chain lengths are highlighted.

plot_class_enrichment(de_results, significant_lipidsets(enrich_results))

lipidr can also plot fold changes of lipids per class showing chain lengths and saturations. Number on the plot indicate multiple lipids measured with the same chain properties.

plot_chain_distribution(de_results)

Session information

sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] ggplot2_3.1.1               lipidr_1.0.0               
##  [3] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
##  [5] BiocParallel_1.18.0         matrixStats_0.54.0         
##  [7] Biobase_2.44.0              GenomicRanges_1.36.0       
##  [9] GenomeInfoDb_1.20.0         IRanges_2.18.0             
## [11] S4Vectors_0.22.0            BiocGenerics_0.30.0        
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_0.2.5       fgsea_1.10.0           xfun_0.6              
##  [4] purrr_0.3.2            lattice_0.20-38        colorspace_1.4-1      
##  [7] htmltools_0.3.6        yaml_2.2.0             rlang_0.3.4           
## [10] pillar_1.3.1           glue_1.3.1             withr_2.1.2           
## [13] GenomeInfoDbData_1.2.1 plyr_1.8.4             stringr_1.4.0         
## [16] zlibbioc_1.30.0        munsell_0.5.0          gtable_0.3.0          
## [19] evaluate_0.13          labeling_0.3           knitr_1.22            
## [22] forcats_0.4.0          Rcpp_1.0.1             scales_1.0.0          
## [25] limma_3.40.0           XVector_0.24.0         gridExtra_2.3         
## [28] fastmatch_1.1-0        digest_0.6.18          stringi_1.4.3         
## [31] dplyr_0.8.0.1          ropls_1.16.0           grid_3.6.0            
## [34] tools_3.6.0            bitops_1.0-6           magrittr_1.5          
## [37] RCurl_1.95-4.12        lazyeval_0.2.2         tibble_2.1.1          
## [40] crayon_1.3.4           tidyr_0.8.3            pkgconfig_2.0.2       
## [43] Matrix_1.2-17          data.table_1.12.2      assertthat_0.2.1      
## [46] rmarkdown_1.12         R6_2.4.0               compiler_3.6.0