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# 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
- 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.
A tibble of this kind
| dimension1 | dimension2 | annotations |
|---|---|---|
chr or fctr |
numeric |
… |
tidygate_gate <-
tidygate_data %>%
mutate( gate = gate_chr( Dim1, Dim2 ) )
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
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
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
))
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