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
# From Github
devtools::install_github("stemangiola/tidygate")
# From CRAN
install.package("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
A tibble of this kind
| dimension1 | dimension2 | annotations | 
|---|---|---|
chr or fctr | 
numeric | 
… | 
tidygate_gate <-
  tidygate_data %>%
  mutate( gate = gate_chr( Dim1, Dim2 ) )
escape on your keyboardtidygate_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
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