phenoptrExtra

The goal of phenoptrExtra is to extend the phenoptr package with additional functionality for working with immunofluorescence data tables exported from the PerkinElmer inForm software.

Installation

You can install the unreleased development version of phenoptrExtra from GitHub with:

devtools::install_github("gerkelab/phenoptrExtra")

This Package is in the Development Stage

library(phenoptrExtra)

What works now

Importing

import_all_csd()

Import all cell segmentation files inside a folder or folders. Uses phenoptr::list_cell_seg_files and phenoptr::read_cell_seg_data to import the data sets, with additional but minor processing. All imported cell segmentation files are row-bound upon import.

ex> import_all_csd("base_csd_dir")

Tidying and Preparing

merge_sample_info()

Merge information about each sample into the CSD file. The sample info should be a data frame containing at least the column ‘Sample’. Columns that overlap with column names in the cell segmentation data are renamed with an appended ‘.info’.

ex> merge_sample_info(csd, sample_info)

reorder_phenotype()

Re-order the factor levels of the ‘.var’ column (default is ‘Phenotype’) by “alphabetical”, “frequency”, or “weighted”. The “Other” and “Undefined” levels are also automatically moved to the end of the ordering if present.

ex> reorder_phenotype(csd, sort = "frequency")

Counting Cell Types

count_observations()
count_cells()

Counts observations by ‘count_by’ column, respecting grouping variables. When the input data frame is cell segmentation data and each row is an individual cell, then the output is cell counts.

ex> csd %>% group_by(Sample) %>% count_observations(Phenotype)
ex> csd %>% group_by(Sample, `Tissue Category`) %>% count_cells(Phenotype)

Working with IF Intensity

gather_cell_intensity()

Select IF intensity measures to associate with a phneotype.

pick_measure()

Helper function for selecting region, frequency, and measure for cell intensity readings according to inForm column name output.

csd %>% 
  group_by(Sample) %>% 
  gather_cell_intensity(
    `CD3`   = pick_measure("Membrane",    520, "Mean"), 
    `CD8`   = pick_measure("Membrane",    540, "Mean"),
    `FOXP3` = pick_measure("Entire Cell", 570, "Mean"),
    `PD1`   = pick_measure("Membrane",    620, "Mean"),
    `PD-L1` = pick_measure("Entire Cell", 650, "Mean"),
    `PCK`   = pick_measure("Entire Cell", 690, "Mean")
  )