FindThresholdsAuto: Automated GO Term-based Thresholding without using a Shiny...

In jn-goe/gruffi: Gruffi identifies and removes stressed cells from brain organoid single-cell datasets.

Automated GO Term-based Thresholding without using a Shiny app.

Description

Automatically applies granular GO term-based thresholding to identify stressed cells in a Seurat object without launching a Shiny app. This function calculates the "granule average GO-scores" for individual GO terms and updates the object with a combined stress identification based on these thresholds.

Usage

FindThresholdsAuto(
  obj = combined.obj,
  proposed.method = c("fitted", "empirical")[1],
  quantile = c(0.99, 0.9)[1],
  stress.ident1,
  stress.ident2,
  notstress.ident3,
  notstress.ident4 = NULL,
  step.size = 0.001,
  plot.results = TRUE,
  ...
)

Arguments

obj	Seurat single cell object, with Granule Averages GO-Scores in metadata (ident1-4).
proposed.method	The method for estimating thresholds, default: "fitted". Options are "fitted" or "empirical".
quantile	The quantile cutoff for threshold determination, default: 0.99. Options are 0.99 or 0.9.
stress.ident1	Identifier for the first stress-related GO term.
stress.ident2	Identifier for the second stress-related GO term.
notstress.ident3	Identifier for the first non-stress-related GO term. Idents 3 and 4 act as a negative filter for stress identification.
notstress.ident4	Identifier for the second non-stress-related GO term, optional.
step.size	Digit precision, equivalent to FindThresholdsShinys step size. Default: 0.001.
plot.results	Boolean flag to control the plotting of results on UMAPs and histograms, default: TRUE.

jn-goe/gruffi documentation built on Nov. 7, 2024, 10:38 p.m.