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Get the top markers for fast annotation

Source: R/Find_Annotation_Markers.R
Find_Annotation_Markers.Rd

This function is a wrapper around FindMarkers that allows for parallelization and filtering of mitochondrial, ribosomal and non-coding RNA features in human, as well as filtering of pseudogenes in mouse. It may also directly return the top X markers for each identity.

Usage

Find_Annotation_Markers(
  seurat_object,
  ident.1 = NULL,
  ident.2 = NULL,
  min.pct = 0.25,
  top.markers = 5,
  unique.markers = TRUE,
  filter.mito = TRUE,
  filter.ribo = TRUE,
  filter.ncRNA = TRUE,
  species = "human",
  parallelized = FALSE,
  BPPARAM = NULL,
  name.markers = FALSE,
  output.df = FALSE,
  output.list = FALSE,
  verbose = TRUE,
  ...
)

Arguments

seurat_object

A Seurat object.

ident.1

Character. (from FindMarkers documentation) Identity class to define markers for; pass an object of class phylo or 'clustertree' to find markers for a node in a cluster tree; passing 'clustertree' requires BuildClusterTree to have been run. Leave NULL to find markers for all clusters.

ident.2

Character. (from FindMarkers documentation) A second identity class for comparison; if NULL, use all other cells for comparison; if an object of class phylo or 'clustertree' is passed to ident.1, must pass a node to find markers for.

min.pct

Numeric. (from FindMarkers documentation) Only test features that are detected in a minimum fraction of min.pct cells in either of the two populations. Meant to speed up the function by not testing features that are very infrequently expressed.

top.markers

Numeric. The number of top markers to return. If Inf, all markers will be returned.

unique.markers

Logical. If TRUE, unique markers will be returned for each identity in order to prevent features repeated multiple times.

filter.mito

Logical. If TRUE, mitochondrial features will be filtered out.

filter.ribo

Logical. If TRUE, ribosomal features will be filtered out.

filter.ncRNA

Logical. If TRUE, non-coding RNA features will be filtered out.

species

Character. The species name to filter out non-coding RNA features. If 'human', a dataset named ncRNA_human built from genenames database will be used as reference. If 'mouse', only pseudogenes will be filtered out based on a dataset named pseudogenes_mouse and built from dreamBase2 database. These datasets are loaded with RightOmicsTools and may be checked for more information.

parallelized

Logical. If TRUE, FindMarkers will be parallelized using BiocParallel. Please note that parallelization is complex and depends on your operating system (Windows users might not see a gain or might even experience a slowdown).

BPPARAM

A BiocParallelParam object to be used for parallelization. If NULL and parallelized = TRUE, the function will use a SerialParam object configured to use a single worker (core) and is therefore not parallelized, in order to prevent accidental use of large computation resources. Ignored if parallelized = FALSE.

name.markers

Logical. If TRUE, each marker will be named with its associated identity. Ignored if output.df = TRUE.

output.df

Logical. If TRUE, a data.frame object containing the FindMarkers results, ordered by decreasing log fold change will be returned. If FALSE, feature names will be returned.

output.list

Logical. If TRUE, and if output.df = TRUE, returns a list of data.frame objects containing FindMarkers results for each identity, or if output.df = FALSE, returns a list of feature names for each identity.

verbose

Logical. If FALSE, does not print progress messages and output, but warnings and errors will still be printed.

...

Additional arguments to be passed to FindMarkers, such as test.use, or passed to other methods and to specific DE methods.

Value

A data.frame object or a list of data.frame objects containing the FindMarkers results, or a list of features names, or feature names.

Examples

# Prepare data
pbmc3k <- Right_Data("pbmc3k")

# Example 1: default parameters and origin of markers
pbmc3k.markers <- Find_Annotation_Markers(pbmc3k,
                                          name.markers = TRUE)
#> Finding markers for cluster Naive CD4 T against all other clusters
#> Finding markers for cluster CD14+ Mono against all other clusters
#> Finding markers for cluster Memory CD4 T against all other clusters
#> Finding markers for cluster B against all other clusters
#> Finding markers for cluster CD8 T against all other clusters
#> Finding markers for cluster FCGR3A+ Mono against all other clusters
#> Finding markers for cluster NK against all other clusters
#> Finding markers for cluster DC against all other clusters
#> Finding markers for cluster Platelets against all other clusters
pbmc3k.markers
#>     Naive CD4 T     Naive CD4 T     Naive CD4 T     Naive CD4 T     Naive CD4 T 
#>          "CCR7"          "LEF1"           "MAL"       "PIK3IP1"          "TCF7" 
#>      CD14+ Mono      CD14+ Mono      CD14+ Mono      CD14+ Mono      CD14+ Mono 
#>         "FOLR3"       "S100A12"        "S100A8"        "S100A9"          "CD14" 
#>    Memory CD4 T    Memory CD4 T    Memory CD4 T    Memory CD4 T    Memory CD4 T 
#>        "CD40LG"          "AQP3"         "SUSD3"           "CD2"         "TRAT1" 
#>               B               B               B               B               B 
#>        "VPREB3"         "CD79A"         "FCRLA"         "TCL1A"         "FCER2" 
#>           CD8 T           CD8 T           CD8 T           CD8 T           CD8 T 
#>          "GZMK"          "GZMH"          "CD8A"          "CCL5"         "KLRG1" 
#>    FCGR3A+ Mono    FCGR3A+ Mono    FCGR3A+ Mono    FCGR3A+ Mono    FCGR3A+ Mono 
#>           "CKB"        "CDKN1C"        "MS4A4A"          "HES4"         "BATF3" 
#>              NK              NK              NK              NK              NK 
#>        "AKR1C3"          "GZMB"        "SH2D1B"         "SPON2"        "FGFBP2" 
#>              DC              DC              DC              DC              DC 
#>      "SERPINF1"        "FCER1A"         "CLIC2"       "CLEC10A"          "ENHO" 
#>       Platelets       Platelets       Platelets       Platelets       Platelets 
#>        "LY6G6F" "RP11-879F14.2"         "CLDN5"           "GP9"        "ITGA2B" 

# Example 2: parallelized FindAllMarkers
BPPARAM <- BiocParallel::registered()[[1]]
if (BPPARAM$workers > 4) BPPARAM$workers <- 4
pbmc3k.markers <- Find_Annotation_Markers(pbmc3k,
                                          min.pct = 0.01,
                                          top.markers = Inf,
                                          unique.markers = FALSE,
                                          filter.mito = FALSE,
                                          filter.ribo = FALSE,
                                          filter.ncRNA = FALSE,
                                          parallelized = TRUE,
                                          BPPARAM = BPPARAM,
                                          output.df = TRUE)
#> Finding markers for cluster FCGR3A+ Mono against all other clustersFinding markers for cluster NK against all other clustersFinding markers for cluster DC against all other clustersFinding markers for cluster Platelets against all other clusters
#> Finding markers for cluster Naive CD4 T against all other clustersFinding markers for cluster CD14+ Mono against all other clustersFinding markers for cluster Memory CD4 T against all other clustersFinding markers for cluster B against all other clustersFinding markers for cluster CD8 T against all other clusters
head(pbmc3k.markers)
#>                p_val avg_log2FC pct.1 pct.2    p_val_adj     cluster feature
#> GTSCR1  1.720280e-08   7.163733 0.016 0.000 2.359193e-04 Naive CD4 T  GTSCR1
#> REG4    4.484775e-10   5.902153 0.022 0.000 6.150421e-06 Naive CD4 T    REG4
#> C2orf40 1.024936e-10   5.644114 0.023 0.000 1.405597e-06 Naive CD4 T C2orf40
#> MMP28   1.729283e-07   4.380462 0.016 0.000 2.371539e-03 Naive CD4 T   MMP28
#> NOG     5.025979e-10   4.148065 0.032 0.003 6.892627e-06 Naive CD4 T     NOG
#> FAM153A 8.848417e-08   3.880304 0.020 0.001 1.213472e-03 Naive CD4 T FAM153A