Visualize GSEA result with input list of gene symbols.

Visualize GSEA result with multiple lists of genes by using clusterProfiler.

sig_gseaplot(
  data,
  sigs,
  group_col,
  target_group,
  gene_id = "SYMBOL",
  slot = "counts",
  method = c("dotplot", "gseaplot"),
  col = "-log10(p.adjust)",
  size = "enrichmentScore",
  pvalue_table = FALSE,
  digits = 2,
  rank_stat = "logFC",
  ...
)

# S4 method for MArrayLM,vector
sig_gseaplot(
  data,
  sigs,
  group_col,
  target_group,
  gene_id = "SYMBOL",
  slot = "counts",
  method = c("dotplot", "gseaplot"),
  col = "-log10(p.adjust)",
  size = "enrichmentScore",
  pvalue_table = FALSE,
  digits = 2,
  rank_stat = "logFC",
  ...
)

# S4 method for MArrayLM,list
sig_gseaplot(
  data,
  sigs,
  group_col,
  target_group,
  gene_id = "SYMBOL",
  slot = "counts",
  method = c("dotplot", "gseaplot"),
  col = "-log10(p.adjust)",
  size = "enrichmentScore",
  pvalue_table = FALSE,
  digits = 2,
  rank_stat = "logFC",
  ...
)

# S4 method for DGEList,ANY
sig_gseaplot(
  data,
  sigs,
  group_col,
  target_group,
  gene_id = "SYMBOL",
  slot = "counts",
  method = c("dotplot", "gseaplot"),
  col = "-log10(p.adjust)",
  size = "enrichmentScore",
  pvalue_table = FALSE,
  digits = 2,
  rank_stat = "logFC",
  ...
)

# S4 method for ANY,ANY
sig_gseaplot(
  data,
  sigs,
  group_col,
  target_group,
  gene_id = "SYMBOL",
  slot = "counts",
  method = c("dotplot", "gseaplot"),
  col = "-log10(p.adjust)",
  size = "enrichmentScore",
  pvalue_table = FALSE,
  digits = 2,
  rank_stat = "logFC",
  ...
)

# S4 method for list,ANY
sig_gseaplot(
  data,
  sigs,
  group_col,
  target_group,
  gene_id = "SYMBOL",
  slot = "counts",
  method = c("dotplot", "gseaplot"),
  col = "-log10(p.adjust)",
  size = "enrichmentScore",
  pvalue_table = FALSE,
  digits = 2,
  rank_stat = "logFC",
  ...
)

Arguments

data

expression data, can be matrix, DGEList, eSet, seurat, sce...

sigs

a vector of signature (Symbols) or a list of signatures

group_col

character or vector, specify the column name to compare in coldata

target_group

pattern, specify the group of interest as reference

gene_id

character, indicate the ID type of rowname of expression data's , could be one of 'ENSEMBL', 'SYMBOL', ... default 'SYMBOL'

slot

character, indicate which slot used as expression, optional

method

one of "gseaplot" and "dotplot", how to plot GSEA result

col

column name of clusterProfiler::GSEA() result, used for dot col when method = "dotplot"

size

column name of clusterProfiler::GSEA() result, used for dot size when method = "dotplot"

pvalue_table

logical, if to add p value table if method = "gseaplot"

digits

num, specify the number of significant digits of pvalue table

rank_stat

character, specify which metric used to rank for GSEA, default "logFC"

...

params for function get_de_table() and function enrichplot::gseaplot2()

Value

patchwork object for all comparisons

Examples

data("im_data_6", "nk_markers")
sig_gseaplot(
  sigs = list(
    A = nk_markers$HGNC_Symbol[1:15],
    B = nk_markers$HGNC_Symbol[20:40],
    C = nk_markers$HGNC_Symbol[60:75]
  ),
  data = im_data_6, group_col = "celltype:ch1",
  target_group = "NK", gene_id = "ENSEMBL"
)
#> 'select()' returned 1:many mapping between keys and columns
#>        NK-Neutrophils NK-Monocytes NK-B.cells NK-CD4 NK-CD8
#> Down             4009         3946       3143   2698   2153
#> NotSig           1486         2683       4418   4991   6191
#> Up               4926         3792       2860   2732   2077
#> 'select()' returned 1:many mapping between keys and columns
#> 
#> preparing geneSet collections...
#> GSEA analysis...
#> Warning: There are ties in the preranked stats (0.88% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> Warning: There are ties in the preranked stats (1.05% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> Warning: There are ties in the preranked stats (1.35% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> Warning: There are ties in the preranked stats (2.2% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> Warning: There are ties in the preranked stats (2.31% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> leading edge analysis...
#> done...