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PNAAssay Methods

PNAAssay-methods.Rd

Methods for PNAAssay objects for generics defined in other packages

Usage

# S3 method for class 'PNAAssay'
RenameCells(object, new.names = NULL, ...)

# S4 method for class 'PNAAssay'
show(object)

# S3 method for class 'PNAAssay'
subset(x, features = NULL, cells = NULL, ...)

# S3 method for class 'PNAAssay'
merge(
  x = NULL,
  y = NULL,
  merge.data = TRUE,
  add.cell.ids = NULL,
  collapse = TRUE,
  ...
)

Arguments

object

A PNAAssay or a PNAAssay5 object

new.names

A character vector with new cell IDs. The length of the vector must be equal to the number of cells in the object and the names must be unique.

...

Arguments passed to other methods

x

A PNAAssay object

features

Feature names

cells

Cell names

y

A PNAAssay object or a list of PNAAssay objects

merge.data

Merge the data slots instead of just merging the counts (which requires renormalization); this is recommended if the same normalization approach was applied to all objects

add.cell.ids

A character vector with sample names

collapse

If TRUE, merge layers of the same name together

Value

A PNAAssay object

Functions

  • RenameCells(PNAAssay): Rename cell IDs of a PNAAssay or PNAAssay5 object

  • show(PNAAssay): Show method for PNAAssay objects

  • subset(PNAAssay): Subset a PNAAssay or a PNAAssay5 object

  • merge(PNAAssay): Merge two or more PNAAssay or PNAAssay5 objects together

Examples

library(pixelatorR)
library(SeuratObject)

pxl_file <- minimal_pna_pxl_file()
seur_obj <- ReadPNA_Seurat(pxl_file)
#> ✔ Created a <Seurat> object with 5 cells and 158 targeted surface proteins
pna_assay <- seur_obj[["PNA"]]
pna_assay <- RenameCells(pna_assay, new.names = paste0(colnames(pna_assay), "-new"))
colnames(pna_assay)
#> [1] "0a45497c6bfbfb22-new" "2708240b908e2eba-new" "c3c393e9a17c1981-new"
#> [4] "d4074c845bb62800-new" "efe0ed189cb499fc-new"

library(pixelatorR)

pxl_file <- minimal_pna_pxl_file()
seur_obj <- ReadPNA_Seurat(pxl_file)
#> ✔ Created a <Seurat> object with 5 cells and 158 targeted surface proteins
seur_obj[["PNA"]]
#> PNAAssay data with 158 features for 5 cells
#> Top 10 variable features:
#>  HLA-ABC, B2M, CD11b, CD11c, CD18, CD82, CD8, TCRab, HLA-DR, CD45 
#> Loaded CellGraph objects:
#>  0

library(pixelatorR)

pxl_file <- minimal_pna_pxl_file()
seur_obj <- ReadPNA_Seurat(pxl_file)
#> ✔ Created a <Seurat> object with 5 cells and 158 targeted surface proteins
pna_assay <- seur_obj[["PNA"]]

pna_assay <- subset(pna_assay, cells = colnames(pna_assay)[1:2])
pna_assay
#> PNAAssay data with 158 features for 2 cells
#> Top 10 variable features:
#>  HLA-ABC, B2M, CD11b, CD11c, CD18, CD82, CD8, TCRab, HLA-DR, CD45 
#> Loaded CellGraph objects:
#>  0

library(pixelatorR)
library(dplyr)

pxl_file <- minimal_pna_pxl_file()
seur_obj <- ReadPNA_Seurat(pxl_file)
#> ✔ Created a <Seurat> object with 5 cells and 158 targeted surface proteins
pna_assay <- seur_obj[["PNA"]]

# Merge two data sets
pna_assay_merged <-
  merge(pna_assay, pna_assay, add.cell.ids = c("A", "B"))
pna_assay_merged
#> PNAAssay data with 158 features for 10 cells
#> First 10 features:
#>  HLA-ABC, B2M, CD11b, CD11c, CD18, CD82, CD8, TCRab, HLA-DR, CD45 
#> Loaded CellGraph objects:
#>  0

# Merge multiple data sets
pna_assay_list <- list(pna_assay, pna_assay, pna_assay)
pna_assay_merged <-
  merge(pna_assay_list[[1]], pna_assay_list[-1], add.cell.ids = c("A", "B", "C"))
pna_assay_merged
#> PNAAssay data with 158 features for 15 cells
#> First 10 features:
#>  HLA-ABC, B2M, CD11b, CD11c, CD18, CD82, CD8, TCRab, HLA-DR, CD45 
#> Loaded CellGraph objects:
#>  0