https://github.com/stemangiola/tidygate

Label elements within user drawn gates
https://github.com/stemangiola/tidygate

clustering datavis dataviz dplyr drawing facs gate ggplot2 interactive pipe programmatic r seurat single-cell single-cell-rna-seq tibble tidy-data tidyverse

Last synced: 3 months ago
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Label elements within user drawn gates

• Host: GitHub
• URL: https://github.com/stemangiola/tidygate
• Owner: stemangiola
• Created: 2020-06-26T07:36:13.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-07-11T00:15:53.000Z (4 months ago)
• Last Synced: 2024-07-22T00:15:34.077Z (4 months ago)
• Topics: clustering, datavis, dataviz, dplyr, drawing, facs, gate, ggplot2, interactive, pipe, programmatic, r, seurat, single-cell, single-cell-rna-seq, tibble, tidy-data, tidyverse
• Language: R
• Homepage:
• Size: 6.41 MB
• Stars: 20
• Watchers: 5
• Forks: 3
• Open Issues: 5
• Metadata Files:
  • Readme: README.Rmd

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• jimsghstars - stemangiola/tidygate - Label elements within user drawn gates (R)

README

        
---

title: "tidygate: add gate information to your tibble"

output: github_document

---

  [![Lifecycle:maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)

```{r setup, echo=FALSE, include=FALSE}

library(knitr)

library(tidygate)

library(dplyr)

library(ggplot2)

library(stringr)

library(readr)

knitr::opts_chunk$set(cache = TRUE, warning = FALSE, message = FALSE, cache.lazy = FALSE)

```

## Introduction 

tidygate allows you to interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package [gatepoints](https://github.com/wjawaid/gatepoints) by Wajid Jawaid. 

For more tidy data analysis:

- [tidyomics](https://github.com/tidyomics) - A software ecosystem for tidy analysis of omic data.

- [tidyHeatmap](https://github.com/stemangiola/tidyHeatmap) - Produce heatmaps with tidy principles.

## Installation

```{r, eval=FALSE}

# From Github

devtools::install_github("stemangiola/tidygate")

# From CRAN

install.package("tidygate")

```

## Example usage 

tidygate provides a single user-facing function: `gate`. The following examples make use of this function, four packages from the tidyverse and the inbuilt `mtcars` dataset. 

```{r}

library(dplyr)

library(ggplot2)

library(stringr)

library(readr)

library(tidygate)

mtcars |>

  head()

```

By default, `gate` creates an interactive scatter plot based on user-defined X and Y coordinates. Colour, shape, size and alpha can be defined as constant values, or can be controlled by values in a specified column. 

Once the plot has been created, multiple gates can be drawn with the mouse. When you have finished, click continue. `gate` will then return a vector of strings, recording the gates each X and Y coordinate pair is within.

```{r eval=FALSE}

mtcars_gated <- 

  mtcars |>

  mutate(gated = gate(x = mpg, y = wt, colour = disp))

```

![](man/figures/demo_gate.gif)

```{r, echo=FALSE}

load("data/demo_gate_data.rda")

# Load pre-recorded brush path from data for example

tidygate_env <<- rlang::env()

tidygate_env$gates <- demo_gate_data

mtcars_gated <- 

  mtcars |>

  mutate(gated = gate(x = mpg, y = wt, programmatic_gates = tidygate_env$gates))

```

To select points which appear within any gates, filter for non-NA values. To select points which appear within a specific gate, string pattern matching can be used.

```{r}

# Select points within any gate

mtcars_gated |> 

  filter(!is.na(gated))

# Select points within gate 2

mtcars_gated |>

  filter(str_detect(gated, "2"))

```

Details of the interactively drawn gates are saved to `tidygate_env$gates`. This variable is overwritten each time interactive gates are drawn, so save it right away if you would like to access it later.  

```{r}

# Inspect previously drawn gates

tidygate_env$gates |>

  head()

```

```{r, eval=FALSE}

# Save if needed

tidygate_env$gates |>

  write_rds("important_gates.rds")

```

If previously drawn gates are supplied to the `programmatic_gates` argument, points will be gated programmatically. This feature allows the reproduction of previously drawn interactive gates.

```{r, eval=FALSE}

important_gates <-

  read_rds("important_gates.rds")

mtcars |>

  mutate(gated = gate(x = mpg, y = wt, programmatic_gates = important_gates)) |>

  filter(!is.na(gated))

```

```{r, echo=FALSE}

mtcars |>

  mutate(gated = gate(x = mpg, y = wt, programmatic_gates = tidygate_env$gates)) |>

  filter(!is.na(gated))

```