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ProximityScores

ProximityScores.Rd

Get and set proximity scores for a PNAAssay, PNAAssay5 or a Seurat object

Usage

ProximityScores(object, ...)

ProximityScores(object, ...) <- value

# S3 method for class 'PNAAssay'
ProximityScores(
  object,
  add_marker_counts = FALSE,
  add_marker_proportions = FALSE,
  lazy = FALSE,
  calc_log2ratio = TRUE,
  ...
)

# S3 method for class 'PNAAssay5'
ProximityScores(
  object,
  add_marker_counts = FALSE,
  add_marker_proportions = FALSE,
  lazy = FALSE,
  calc_log2ratio = TRUE,
  ...
)

# S3 method for class 'PNAAssay'
ProximityScores(object, ...) <- value

# S3 method for class 'PNAAssay5'
ProximityScores(object, ...) <- value

# S3 method for class 'Seurat'
ProximityScores(
  object,
  assay = NULL,
  meta_data_columns = NULL,
  add_marker_counts = FALSE,
  add_marker_proportions = FALSE,
  lazy = FALSE,
  calc_log2ratio = TRUE,
  ...
)

# S3 method for class 'Seurat'
ProximityScores(object, assay = NULL, ...) <- value

Arguments

object

An object with polarization scores

...

Not implemented

value

A tbl_df or tbl_lazy with proximity scores

add_marker_counts

A logical indicating whether to add marker counts to the output ("count_1" and "count_2")

add_marker_proportions

A logical indicating whether to add marker count proportions to the output ("p1" and "p2")

lazy

A logical indicating whether to lazy load the proximity scores from the PXL files

calc_log2ratio

A logical indicating whether to calculate the log2 ratio proximity score

assay

Name of a PNAAssay

meta_data_columns

A character vector with meta.data column names. This option can be useful to join meta.data columns with the proximity score table.

Value

ProximityScores: Proximity scores

ProximityScores<-: An object with updated proximity scores

See also

Other spatial metrics: ColocalizationScores(), Edgelists(), PolarizationScores()

Examples

library(pixelatorR)
library(dplyr)

pxl_file <- minimal_pna_pxl_file()
seur_obj <- ReadPNA_Seurat(pxl_file)
ProximityScores(seur_obj[["PNA"]])
#> # A tibble: 58,696 × 9
#>    marker_1 marker_2 join_count join_count_expected_mean join_count_expected_sd
#>    <chr>    <chr>         <dbl>                    <dbl>                  <dbl>
#>  1 CD56     CD56              0                     0                     0    
#>  2 CD56     mIgG2b            0                     0.03                  0.171
#>  3 CD56     CD71              0                     0.01                  0.1  
#>  4 CD56     CD6               0                     2.07                  1.58 
#>  5 CD56     Siglec-9          0                     0.03                  0.171
#>  6 CD56     CD79a             0                     0                     0    
#>  7 CD56     NKp80             0                     0                     0    
#>  8 CD56     CD85j             0                     0.17                  0.378
#>  9 CD56     IgM               0                     0.08                  0.307
#> 10 CD56     TCRva7.2          0                     0.01                  0.1  
#> # ℹ 58,686 more rows
#> # ℹ 4 more variables: join_count_z <dbl>, join_count_p <dbl>, component <chr>,
#> #   log2_ratio <dbl>

# Set proximity scores
ProximityScores(seur_obj[["PNA"]]) <-
  ProximityScores(seur_obj[["PNA"]]) %>%
  mutate(ratio = join_count / join_count_expected_mean)

library(pixelatorR)

# Create example Seurat object
pxl_file <- minimal_pna_pxl_file()
seur_obj <- ReadPNA_Seurat(pxl_file)

# Get proximity scores
proximity <- ProximityScores(seur_obj)
proximity
#> # A tibble: 58,696 × 9
#>    marker_1 marker_2 join_count join_count_expected_mean join_count_expected_sd
#>    <chr>    <chr>         <dbl>                    <dbl>                  <dbl>
#>  1 CD56     CD56              0                     0                     0    
#>  2 CD56     mIgG2b            0                     0.03                  0.171
#>  3 CD56     CD71              0                     0.01                  0.1  
#>  4 CD56     CD6               0                     2.07                  1.58 
#>  5 CD56     Siglec-9          0                     0.03                  0.171
#>  6 CD56     CD79a             0                     0                     0    
#>  7 CD56     NKp80             0                     0                     0    
#>  8 CD56     CD85j             0                     0.17                  0.378
#>  9 CD56     IgM               0                     0.08                  0.307
#> 10 CD56     TCRva7.2          0                     0.01                  0.1  
#> # ℹ 58,686 more rows
#> # ℹ 4 more variables: join_count_z <dbl>, join_count_p <dbl>, component <chr>,
#> #   log2_ratio <dbl>

# Get proximity scores with additional meta data
proximity <-
  ProximityScores(seur_obj, meta_data_columns = c("n_umi", "n_edges"))
proximity
#> # A tibble: 58,696 × 11
#>    marker_1 marker_2 join_count join_count_expected_mean join_count_expected_sd
#>    <chr>    <chr>         <dbl>                    <dbl>                  <dbl>
#>  1 CD56     CD56              0                     0                     0    
#>  2 CD56     mIgG2b            0                     0.03                  0.171
#>  3 CD56     CD71              0                     0.01                  0.1  
#>  4 CD56     CD6               0                     2.07                  1.58 
#>  5 CD56     Siglec-9          0                     0.03                  0.171
#>  6 CD56     CD79a             0                     0                     0    
#>  7 CD56     NKp80             0                     0                     0    
#>  8 CD56     CD85j             0                     0.17                  0.378
#>  9 CD56     IgM               0                     0.08                  0.307
#> 10 CD56     TCRva7.2          0                     0.01                  0.1  
#> # ℹ 58,686 more rows
#> # ℹ 6 more variables: join_count_z <dbl>, join_count_p <dbl>, component <chr>,
#> #   log2_ratio <dbl>, n_umi <int>, n_edges <int>

# Get proximity scores with marker_1 and marker_2 counts
proximity <-
  ProximityScores(seur_obj, add_marker_counts = TRUE)
proximity
#> # A tibble: 58,696 × 11
#>    marker_1 marker_2 join_count join_count_expected_mean join_count_expected_sd
#>    <chr>    <chr>         <dbl>                    <dbl>                  <dbl>
#>  1 CD56     CD56              0                     0                     0    
#>  2 CD56     mIgG2b            0                     0.03                  0.171
#>  3 CD56     CD71              0                     0.01                  0.1  
#>  4 CD56     CD6               0                     2.07                  1.58 
#>  5 CD56     Siglec-9          0                     0.03                  0.171
#>  6 CD56     CD79a             0                     0                     0    
#>  7 CD56     NKp80             0                     0                     0    
#>  8 CD56     CD85j             0                     0.17                  0.378
#>  9 CD56     IgM               0                     0.08                  0.307
#> 10 CD56     TCRva7.2          0                     0.01                  0.1  
#> # ℹ 58,686 more rows
#> # ℹ 6 more variables: join_count_z <dbl>, join_count_p <dbl>, component <chr>,
#> #   log2_ratio <dbl>, count_1 <int>, count_2 <int>

# Update proximity scores in Seurat object
ProximityScores(seur_obj) <- ProximityScores(seur_obj) %>%
  mutate(ratio = join_count / join_count_expected_mean)