Enrichcment

Enrichment analysis provides two main approaches: ‘Over Representation Analysis (ORA)’ and ‘Lipid Set Enrichment Analysis (LSEA)’. ORA analysis illustrates significant lipid species enriched in the categories of lipid class. LSEA analysis is a computational method determining whether an a priori-defined set of lipids shows statistically significant, concordant differences between two biological states (e.g., phenotypes).

The input data must be a SummarizedExperiment object deSp_se generated by LipidSigR::deSp_twoGroup or LipidSigR::deSp_multiGroup. Please read lipid species differential expression analysis section in vignette("3_de").

To use our data as an example, follow the steps below.

# load package
library(LipidSigR)

# load the example SummarizedExperiment
data("de_data_twoGroup")

# data processing
processed_se <- data_process(
    de_data_twoGroup, exclude_missing=TRUE, exclude_missing_pct=70, 
    replace_na_method='min', replace_na_method_ref=0.5, 
    normalization='Percentage')

# conduct differential expression analysis of lipid species
deSp_se <- deSp_twoGroup(
    processed_se, ref_group='ctrl', test='t-test',
    significant='pval', p_cutoff=0.05, FC_cutoff=1, transform='log10')

Over Representation Analysis (ORA)

The Over-Representation analysis provides whether significant lipid species are enriched in the categories of lipid class. Results are presented in tables and bar plots categorizing lipid species into ‘up-regulated’ or ‘down-regulated’ groups based on log2 fold change.

Here, we use two-group data as an example.

# conduct ORA 
ora_all <- enrichment_ora(
    deSp_se, char=NULL, significant='pval', p_cutoff=0.05)

# result summary 
summary(ora_all)
#>                     Length Class      Mode
#> enrich_result       14     tbl_df     list
#> static_barPlot       9     gg         list
#> interactive_barPlot  8     plotly     list
#> table_barPlot       10     grouped_df list

# view result: ORA bar plot
ora_all$static_barPlot 

ORA bar plot of all characteristics The bar plot shows the top 10 significant up-regulated and down-regulated terms.

• You can obtain the selectable lipid characteristics for the char input using LipidSigR::list_lipid_char. Please read vignette("1_tool_function").

Here, we use class as the char input for an example.

# conduct ORA of a specific `char`
ora_one <- enrichment_ora(
    deSp_se, char='class', significant='pval', p_cutoff=0.05)

# result summary 
summary(ora_one)
#>                     Length Class      Mode
#> enrich_result       14     tbl_df     list
#> static_barPlot       9     gg         list
#> interactive_barPlot  8     plotly     list
#> table_barPlot       11     grouped_df list

# view result: ORA bar plot
ora_one$static_barPlot 

ORA bar plot of specific characteristics The bar plot classifies significant lipid species into ‘up-regulated’ or ‘down-regulated’ categories based on their log2 fold change, according to a selected characteristic. Red bars indicate up-regulated, blue bars represent down-regulated, and grey bars signify non-significant.

Lipid Set Enrichment Analysis (LSEA)

Lipid Set Enrichment Analysis (LSEA) is a computational method determining whether an a priori-defined set of lipids shows statistically significant, concordant differences between two biological states (e.g., phenotypes). Results are presented in tables and bar plots categorizing lipid species into ‘up-regulated’ or ‘down-regulated’ groups based on NES (Normalized Enrichment Score), and a table.

# conduct LSEA
lsea_all <- enrichment_lsea(
    deSp_se, char=NULL, rank_by='statistic', significant='pval', 
    p_cutoff=0.05)

# result summary 
summary(lsea_all)
#>                     Length Class  Mode   
#> enrich_result        11    tbl_df list   
#> static_barPlot        9    gg     list   
#> interactive_barPlot   8    plotly list   
#> table_barPlot         8    tbl_df list   
#> lipid_set           167    -none- list   
#> ranked_list         182    -none- numeric

# view result: LSEA bar plot
lsea_all$static_barPlot

LSEA bar plot of all characteristics The bar plot shows the top 10 significant up-regulated and down-regulated terms.

• You can obtain the selectable lipid characteristics for the char input using LipidSigR::list_lipid_char. Please read vignette("1_tool_function").

Here, we use class as the char input for an example.

# conduct LSEA of a specific `char`
lsea_one <- enrichment_lsea(
    deSp_se, char='class', rank_by='statistic', 
    significant='pval', p_cutoff=0.05)

# result summary 
summary(lsea_one)
#>                     Length Class  Mode   
#> enrich_result        11    tbl_df list   
#> static_barPlot        9    gg     list   
#> interactive_barPlot   8    plotly list   
#> table_barPlot         9    tbl_df list   
#> lipid_set            11    -none- list   
#> ranked_list         182    -none- numeric

# view result: LSEA bar plot
lsea_one$static_barPlot

LSEA bar plot of a specific char The bar plot classifies significant lipid species into ‘up-regulated’ or ‘down-regulated’ categories based on their log2 fold change, according to a selected characteristic. Red bars indicate up-regulated, blue bars represent down-regulated, and grey bars signify non-significant.

After running enrichment_lsea, you can continue executing plot_enrichment_lsea to plot the enrichment plot further. Please use the whole output of enrichment_lsea as the input for plotting.

# plot LSEA results
lsea_plot <- plot_enrichment_lsea(
    lsea_res=lsea_one, char='class', char_feature='TG')

# view result: enrichment plot
lsea_plot