Differential analysis (abundance)
RunDAA.RdRuns differential analysis on abundance scores generated with the
Pixelator data processing pipeline. The function is a wrapper around
the FindMarkers function from Seurat but provides a similar
interface as RunDPA and RunDCA from pixelatorR.
Currently, RunDAA only has a method for Seurat objects.
Usage
RunDAA(object, ...)
# S3 method for class 'Seurat'
RunDAA(
object,
contrast_column,
reference,
targets = NULL,
assay = NULL,
group_vars = NULL,
mean_fxn = rowMeans,
fc_name = "difference",
p_adjust_method = c("bonferroni", "holm", "hochberg", "hommel", "BH", "BY", "fdr"),
verbose = TRUE,
...
)Arguments
- object
A Seurat object containing abundance data, such as counts or normalized counts
- ...
Parameters passed to
FindMarkers- contrast_column
The name of the meta data column where the group labels are stored. This column must include
targetsandreference.- reference
The name of the reference group
- targets
The names of the target groups. These groups will be compared to the reference group. If the value is set to
NULL(default), all groups available incontrast_columnexceptreferencewill be compared to thereferencegroup.- assay
Name of assay to use
- group_vars
An optional character vector with column names to group the tests by.
- mean_fxn
Function to use for fold change or average difference calculation in
FindMarkers. See documentation for themean.fxnparameter inFindMarkersfor more information.- fc_name
Name of the fold change, average difference, or custom function column in the output tibble. See documentation for the
fc.nameparameter inFindMarkersfor more information.- p_adjust_method
One of "bonferroni", "holm", "hochberg", "hommel", "BH", "BY" or "fdr". (see
?p.adjustfor details). The p values are adjusted across all tests and the replaces thep_val_adjcolumn generated byFindMarkers.- verbose
Print messages
Details
The input object should contain a contrast_column (character vector or factor)
that includes information about the groups to compare. A typical example is a column
with sample labels, for instance: "control", "stimulated1", "stimulated2". If the input
object is a Seurat object, the contrast_column should be available in
the meta.data slot. For those familiar with FindMarkers from Seurat,
contrast_column is equivalent to the group.by parameter.
The targets parameter specifies a character vector with the names of the groups
to compare reference. targets can be a single group name or a vector of
group names while reference can only refer to a single group. Both targets
and reference should be present in the contrast_column. These parameters
are similar to the ident.1 and ident.2 parameters in FindMarkers.
Additional groups
The test is always computed between targets and reference, but it is possible
to add additional grouping variables with group_vars. If group_vars is used,
each comparison is split into groups defined by the group_vars. For instance, if we
have annotated cells into cell type populations and saved these annotations in a meta.data
column called "cell_type", we can pass "cell_type" to group_vars="cell_type" to split
tests across each cell type.
Types of comparisons
Consider a scenario where we have a Seurat object (seurat_object) with MPX data.
seurat_object contains a meta.data column called "sampleID" that holds
information about what samples the MPX components originated from. This column could have
three sample IDs: "control", "stimulated1" and "stimulated2". In addition, we have a column
called "cell_type" that holds information about the cell type identity of each MPX component.
If we want to compare the "stimulated1" group to the "control" group:
daa_markers <- RunDAA(object = seurat_object, contrast_column = "sampleID", reference = "control", targets = "stimulated1")If we want to compare the "stimulated1" and "stimulated2" groups to the "control" group:
If we want to compare the "stimulated1" and "stimulated2" groups to the "control" group, and split the tests by cell type:
Examples
library(pixelatorR)
library(dplyr)
library(SeuratObject)
pxl_file <- system.file("extdata/five_cells",
"five_cells.pxl",
package = "pixelatorR"
)
# Seurat objects
se <- ReadMPX_Seurat(pxl_file)
#> ✔ Created a 'Seurat' object with 5 cells and 80 targeted surface proteins
se <- merge(se, rep(list(se), 9), add.cell.ids = LETTERS[1:10])
se$sample <- c("T", "C", "C", "C", "C") %>% rep(times = 10)
se <- Seurat::NormalizeData(se, normalization.method = "CLR", margin = 2)
#> Normalizing across cells
# Run DPA
daa_markers <- RunDAA(se,
contrast_column = "sample",
targets = "T", reference = "C"
)
#> For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
#> (default method for FindMarkers) please install the presto package
#> --------------------------------------------
#> install.packages('devtools')
#> devtools::install_github('immunogenomics/presto')
#> --------------------------------------------
#> After installation of presto, Seurat will automatically use the more
#> efficient implementation (no further action necessary).
#> This message will be shown once per session
daa_markers
#> # A tibble: 80 × 8
#> marker p p_adj difference pct_1 pct_2 target reference
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 CD137 0.000000407 0.0000326 0.0568 1 0.5 T C
#> 2 CD62P 0.000000792 0.0000633 -0.130 0 1 T C
#> 3 CD11b 0.000000792 0.0000633 0.0400 1 0.75 T C
#> 4 CD152 0.000000792 0.0000633 0.218 1 0.75 T C
#> 5 CD163 0.000000792 0.0000633 0.0641 1 0.75 T C
#> 6 CD200 0.000000792 0.0000633 0.236 1 0.75 T C
#> 7 CD154 0.000000792 0.0000633 0.111 1 0.75 T C
#> 8 CD328 0.000000792 0.0000633 0.161 1 0.75 T C
#> 9 CD158 0.000000792 0.0000633 0.215 1 0.75 T C
#> 10 CD279 0.000000792 0.0000633 0.843 1 1 T C
#> # ℹ 70 more rows