gate_chr-methods: Label points within a scatter plot drawing a gate

In tidygate: Interactively Gate Points

Label points within a scatter plot drawing a gate

Description

gate() takes as input a 'tbl' formatted as | <DIMENSION 1> | <DIMENSION 2> | <...> | and calculates the rotated dimensional space of the feature value.

Usage

gate_chr(
  .dim1,
  .dim2,
  .color = NULL,
  .shape = NULL,
  .size = NULL,
  opacity = 1,
  how_many_gates = 1,
  .group_by = NULL,
  gate_list = NULL,
  ...
)

gate_int(
  .dim1,
  .dim2,
  .color = NULL,
  .shape = NULL,
  .size = NULL,
  opacity = 1,
  how_many_gates = 1,
  .group_by = NULL,
  gate_list = NULL,
  ...
)

Arguments

.dim1	A column symbol. The x dimension
.dim2	A column symbol. The y dimension
.color	A column symbol. Colour of points
.shape	A column symbol. Shape of points
.size	A column symbol. Size of points
opacity	A number between 0 and 1. The opacity level of the data points
how_many_gates	An integer. The number of gates to label
.group_by	A column symbol. The column that is used to calculate distance (i.e., normally genes)
gate_list	A list of gates. It is returned by gate function as attribute \"gate\". If you want to create this list yourself, each element of the list is a data frame with x and y columns. Each row is a coordinate. The order matter.
...	Further parameters passed to the function gatepoints::fhs

Details

\lifecycle

maturing

This function allow the user to label data points in inside one or more 2D gates. This package is based on on the package gatepoints.

Value

An character vector, with "0" for elements outside gates and "1..N" for the elements inside the N gates.

An integer vector, with 0 for elements outside gates and 1..N for the elements inside the N gates.

Examples

# Standard use - interactive

if(interactive()){

  tidygate::tidygate_data  %>%
    distinct(`ct 1` , `ct 2`, Dim1, Dim2) %>%
    mutate(gate = gate_chr( Dim1, Dim2)) 

}

library(magrittr)
library(dplyr)

# Standard use - programmatic
res_distinct =
 tidygate::tidygate_data  %>%
 distinct(`ct 1` , `ct 2`, Dim1, Dim2) %>%
 mutate(gate = gate_chr( Dim1, Dim2,gate_list = tidygate::gate_list)) 

# Grouping - programmatic
res =
 tidygate::tidygate_data  %>%
   mutate(gate = gate_chr( 
     Dim1, Dim2,
     .group_by = c(`ct 1` , `ct 2`), 
     gate_list = tidygate::gate_list
   ))

tidygate documentation built on Sept. 30, 2024, 9:35 a.m.