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Differential analysis (colocalization)

RunDCA.Rd

Runs differential analysis on colocalization scores generated with the Pixelator data processing pipeline.

Usage

RunDCA(object, ...)

# S3 method for class 'data.frame'
RunDCA(
  object,
  contrast_column,
  reference,
  targets = NULL,
  group_vars = NULL,
  coloc_metric = "pearson_z",
  min_n_obs = 0,
  alternative = c("two.sided", "less", "greater"),
  conf_int = TRUE,
  p_adjust_method = c("bonferroni", "holm", "hochberg", "hommel", "BH", "BY", "fdr"),
  cl = NULL,
  verbose = TRUE,
  ...
)

# S3 method for class 'Seurat'
RunDCA(
  object,
  contrast_column,
  reference,
  targets = NULL,
  assay = NULL,
  group_vars = NULL,
  coloc_metric = "pearson_z",
  min_n_obs = 0,
  alternative = c("two.sided", "less", "greater"),
  conf_int = TRUE,
  p_adjust_method = c("bonferroni", "holm", "hochberg", "hommel", "BH", "BY", "fdr"),
  cl = NULL,
  verbose = TRUE,
  ...
)

Arguments

object

An object containing colocalization scores

...

Not yet implemented

contrast_column

The name of the column where the group labels are stored. This column must include target and reference.

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 in contrast_column except reference will be compared to the reference group.

group_vars

An optional character vector with column names to group the tests by.

coloc_metric

The colocalization metric to use. Any numeric data column in the colocalization score table can be selected. The default is "pearson_z".

min_n_obs

Minimum number of observations allowed in a group. Target groups with less observations than min_n_obs will be skipped.

alternative

One of 'two.sided', 'less' or 'greater' (see ?wilcox.test for details)

conf_int

Should confidence intervals be computed? (see ?wilcox.test for details)

p_adjust_method

One of "bonferroni", "holm", "hochberg", "hommel", "BH", "BY" or "fdr". (see ?p.adjust for details)

cl

A cluster object created by makeCluster, or an integer to indicate number of child-processes (integer values are ignored on Windows) for parallel evaluations. See Details on performance in the documentation for pbapply. The default is NULL, which means that no parallelization is used. Note that warnings are not caught when using parallel processing.

verbose

Print messages

assay

Name of assay to use

Value

A tbl_df object with test results

Details

If you are working with a Seurat object created with pixelatorR that contains a CellGraphAssay, the colocalization scores are accessed directly from the CellGraphAssay (see ColocalizationScores).

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.

  1. If we want to compare the "stimulated1" group to the "control" group:

    
dca_markers <- RunDCA(object = seurat_object,
                      contrast_column = "sampleID",
                      reference = "control",
                      targets = "stimulated1")

  2. If we want to compare the "stimulated1" and "stimulated2" groups to the "control" group:

    
dca_markers <- RunDCA(object = seurat_object,
                      contrast_column = "sampleID",
                      reference = "control",
                      targets = c("stimulated1", "stimulated2"))

  3. If we want to compare the "stimulated1" and "stimulated2" groups to the "control" group, and split the tests by cell type:

    
dca_markers <- RunDCA(object = seurat_object,
                     contrast_column = "sampleID",
                     reference = "control",
                     targets = c("stimulated1", "stimulated2"),
                     group_vars = "cell_type")

Error handling

If the test fails for a certain comparison, a warning is raised and no results will be returned for that comparison. This can happen if one of the two groups being compared has too few observations. Note that when using parallel processing, these warnings are muted to reduce the overhead of communication between processes.

See also

Other DA-methods: RunDAA(), RunDPA()

Examples

library(pixelatorR)
library(dplyr)

pxl_file <- system.file("extdata/five_cells",
  "five_cells.pxl",
  package = "pixelatorR"
)
# Seurat objects
seur1 <- seur2 <- ReadMPX_Seurat(pxl_file)
#> ✔ Created a 'Seurat' object with 5 cells and 80 targeted surface proteins
seur1$sample <- "Sample1"
seur2$sample <- "Sample2"
seur_merged <- merge(seur1, seur2, add.cell.ids = c("A", "B"))

# Subset data to run test on a few markers
seur_merged <- subset(seur_merged,
  features = c(
    "CD3", "CD4", "CD8", "CD19",
    "CD20", "CD45RA", "CD45RO"
  )
)

# Run DCA
dca_markers <- RunDCA(seur_merged,
  contrast_column = "sample",
  target = "Sample1", reference = "Sample2"
)
#> ℹ Splitting data by: marker_1, marker_2
#> ℹ Running 10 tests for the following comparison:
#>   - Sample1 vs Sample2
#> Warning: Got the following message when running wilcox.test test for marker 'CD19/CD20': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD19/CD4': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD19/CD45RA': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD19/CD8': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD20/CD4': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD20/CD45RA': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD20/CD8': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD4/CD45RA': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD4/CD8': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
#> Warning: Got the following message when running wilcox.test test for marker 'CD45RA/CD8': Sample1 vs Sample2
#>   cannot compute exact confidence intervals with ties
dca_markers
#> # A tibble: 10 × 15
#>      estimate data_type target  reference    n1    n2 statistic     p p_adj
#>         <dbl> <chr>     <chr>   <chr>     <int> <int>     <dbl> <dbl> <dbl>
#>  1  0         pearson_z Sample1 Sample2       4     4       8       1     1
#>  2  0         pearson_z Sample1 Sample2       5     5      12.5     1     1
#>  3 -0.0000165 pearson_z Sample1 Sample2       5     5      12.5     1     1
#>  4  0         pearson_z Sample1 Sample2       5     5      12.5     1     1
#>  5  0         pearson_z Sample1 Sample2       4     4       8       1     1
#>  6  0         pearson_z Sample1 Sample2       4     4       8       1     1
#>  7  0         pearson_z Sample1 Sample2       4     4       8       1     1
#>  8  0         pearson_z Sample1 Sample2       5     5      12.5     1     1
#>  9  0         pearson_z Sample1 Sample2       5     5      12.5     1     1
#> 10 -0.0000464 pearson_z Sample1 Sample2       5     5      12.5     1     1
#> # ℹ 6 more variables: conf.low <dbl>, conf.high <dbl>, method <chr>,
#> #   alternative <chr>, marker_1 <chr>, marker_2 <chr>