A simple and small example of t-SNE dimensionality reduction in R language

Some people have left a message on the public account before asking how to use R language to achieve t-SNE dimensionality reduction. Today's tweet introduces the code implemented in R language. The main content refers to the link https://datavizpyr.com/how-to-make- tsne-plot-in-r/

The full name of t-SNE is t-Distributed Stochastic Neighbor Embedding. I don’t know the specific calculation principle. Anyway, it is similar to PCA to convert high-dimensional data into low-dimensional data.

The example data set uses the penguin data set, the name is penguins, this data set comes from the R package palmerpenguins, if you want to use this data, you need to install the R package, and you need to use the R package to implement t-SNE Rtsne. The R package tidyverseis used for data sorting, so load these three R packages first. If it is the first time to use it, you need to install it first. The installation command is

install.packages("tidyverse")
install.packages("palmerpenguins")
install.packages("Rtsne")

Load the required R packages

library(tidyverse)
library(palmerpenguins)
library(Rtsne)

Select numeric variables in the dataset for subsequent analysis

penguins %>% 
  select(where(is.numeric),-year,species) %>%
  mutate(ID=row_number()) %>% 
  column_to_rownames("ID") %>% 
  na.omit()-> df

Newly learned function here

• Select numeric variables in a data frame select(where(is.numeric))
• Add 1 to the dataset: the number of how many rows mutate(ID=row_number())
• Specify the column in the data set as the row name (provided that there are no duplicates) column_to_rownames("ID")

t-SNE dimensionality reduction

tSNE_fit<-df %>% 
  select(-species) %>% 
  scale() %>% 
  Rtsne()

Extract dimensionality reduction results

tSNE_fit$Y %>% 
  as.data.frame() %>% 
  rename(tSNE1="V1",
         tSNE2="V2") %>% 
  mutate(Species=df$species) -> tSNE.plot

Scatter plot showing results

library(ggplot2)
ggplot()+
  geom_point(data=tSNE.plot,
             aes(x=tSNE1,y=tSNE2,color=Species))+
  stat_ellipse(data=tSNE.plot,
               geom="polygon",
               aes(x=tSNE1,y=tSNE2,
                   group=Species,
                   fill=Species),
               alpha=0.5,
               lty="dashed",
               color="black",
               key_glyph="blank")+
  theme_bw()

Elegantly modify the legend

https://mp.weixin.qq.com/s/I3YnxqulQRu-9i-gZIh7fA

image.png

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The sample code of today's tweet can be 20210827obtained by leaving a message in the background of the official account

Xiaoming’s data analysis notebook public account mainly shares: 1. Simple examples of R language and python for data analysis and data visualization; 2. Reading notes on horticultural plants related transcriptomics, genomics, and population genetics literature; 3. Bioinformatics Learn introductory study materials and your own study notes!

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