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tidygate: add gate information to your tibble

Lifecycle:maturing

Please have a look also to

  • nanny for tidy high-level data analysis and manipulation

  • tidyHeatmap for producing heatmaps following tidy principles

  • tidybulk for tidy and modular transcriptomics analyses

Installation

# From Github
devtools::install_github("stemangiola/tidygate")

# From CRAN
install.package("tidygate")

What is tidygate

It interactively or programmately labels points within custom gates on two dimensions, according to tidyverse principles. The information is added to your tibble. It is based on the package gatepoints from Wajid Jawaid.

The main benefits are

  • in interactive mode you can draw your gates on extensive ggplot-like scatter plots
  • you can draw multiple gates
  • you can save your gates and apply the programmatically.

Input

A tibble of this kind

dimension1 dimension2 annotations
chr or fctr numeric …

Step-by-step instructons for Rstudio

1) Execute the following code in the console panel
tidygate_gate <-
  tidygate_data %>%
  mutate( gate = gate_chr( Dim1, Dim2 ) )
2) look at the Viewer and draw a gate clicking at least three times on the plot

3) Click the finish button on the top-right corner, or press escape on your keyboard

The output tibble

tidygate_gate
## # A tibble: 2,240 x 9
##    group   hierarchy `ct 1`    `ct 2`    relation cancer_ID   Dim1    Dim2 gate 
##    <chr>       <dbl> <chr>     <chr>        <dbl> <chr>      <dbl>   <dbl> <chr>
##  1 adrenal         1 endothel… epitheli…    -1    ACC       -0.874 -0.239  0    
##  2 adrenal         1 endothel… fibrobla…    -1    ACC       -0.740  0.114  1    
##  3 adrenal         1 endothel… immune_c…    -1    ACC       -0.988  0.118  0    
##  4 adrenal         1 epitheli… endothel…     1    ACC        0.851  0.261  0    
##  5 adrenal         1 epitheli… fibrobla…     1    ACC        0.839  0.320  0    
##  6 adrenal         1 epitheli… immune_c…     1    ACC        0.746  0.337  0    
##  7 adrenal         1 fibrobla… endothel…     1    ACC        0.722 -0.0696 0    
##  8 adrenal         1 fibrobla… epitheli…    -1    ACC       -0.849 -0.317  0    
##  9 adrenal         1 fibrobla… immune_c…     0.52 ACC       -0.776 -0.383  0    
## 10 adrenal         1 immune_c… endothel…     1    ACC        0.980 -0.116  0    
## # … with 2,230 more rows

Gates are saved in a temporary file for later use

## [[1]]
##            x          y
## 1 -0.9380459  0.2784375
## 2 -0.9555544 -0.1695209
## 3 -0.3310857  0.2116150
## 
## [[2]]
##             x          y
## 1  0.01324749  0.2165648
## 2 -0.31065917 -0.1026984
## 3 -0.11514794 -0.2982161
## 4  0.48013998  0.1225183

Programmatic gating

We can use previously drawn gates to programmately add the gate column

tidygate_data %>%
  mutate( gate = gate_chr(
    Dim1, Dim2,
     # Pre-defined gates
    gate_list = my_gates
  ))
## # A tibble: 2,240 x 9
##    group   hierarchy `ct 1`    `ct 2`    relation cancer_ID   Dim1    Dim2 gate 
##    <chr>       <dbl> <chr>     <chr>        <dbl> <chr>      <dbl>   <dbl> <chr>
##  1 adrenal         1 endothel… epitheli…    -1    ACC       -0.874 -0.239  0    
##  2 adrenal         1 endothel… fibrobla…    -1    ACC       -0.740  0.114  1    
##  3 adrenal         1 endothel… immune_c…    -1    ACC       -0.988  0.118  0    
##  4 adrenal         1 epitheli… endothel…     1    ACC        0.851  0.261  0    
##  5 adrenal         1 epitheli… fibrobla…     1    ACC        0.839  0.320  0    
##  6 adrenal         1 epitheli… immune_c…     1    ACC        0.746  0.337  0    
##  7 adrenal         1 fibrobla… endothel…     1    ACC        0.722 -0.0696 0    
##  8 adrenal         1 fibrobla… epitheli…    -1    ACC       -0.849 -0.317  0    
##  9 adrenal         1 fibrobla… immune_c…     0.52 ACC       -0.776 -0.383  0    
## 10 adrenal         1 immune_c… endothel…     1    ACC        0.980 -0.116  0    
## # … with 2,230 more rows

Subsampling

The subsample parameter allows you to sample a subset of your data for analysis with a fixed seed. This can be particularly useful for large datasets or for preliminary exploratory analysis. Here's how you can use it:

# For sampling 50% of your data
tidygate_data %>%
  mutate( gate = gate_chr(
    Dim1, Dim2, subsample = 0.5
  ))


# For sampling 100 specific observations
tidygate_data %>%
  mutate( gate = gate_chr(
    Dim1, Dim2, subsample = 100
  ))