How to use R language to draw scatter diagram

R Language mapping gene expression gene “ Symmetric scatter diagram

Transcriptome analysis , After calculating the differentially expressed genes between the two groups , How do you usually mean ？ You may have thought of using volcano map for the first time . You bet , Volcano maps are the most frequently used , In the volcanic map, it is easy to distinguish between two groups according to genes Fold

Change Value and significance p value , Overview of identifying and judging differentially expressed genes . A volcano map is essentially a scatter chart , Usually the horizontal and vertical coordinates represent log2 Transformed Fold Change as well as -

log10 Transformed p Value or p Adjustment value information （ The following figure on the left ）. When it comes to scatter charts , There is another common pattern of showing differentially expressed genes ： The horizontal and vertical axes can represent the average expression values of the two groups of genes , This style makes it easier to visually compare the different states of genes in the two groups .

1 Sample file

Sample file “gene_diff.txt” It is the result of differential expression analysis of a group of genes , The processing group is recorded （treat） And the control group （control） The expression of genes that are significantly inconsistent , The identification standard is p<0.01 as well as |log2

Fold Change|≥1.

among ,gene_id For the gene name ;control and treat It represents the average expression value of genes in the two groups ;log2FoldChange namely log2 The differential multiple of gene expression after transformation ;pvalue Is the significance of different genes p value ;diff On the basis of p<0.01 as well as |log2

Fold Change|≥1 Screened differential genes , In this column “up” For upward adjustment ,“down” Is down ,“none” Non differential genes .

Next, through the example file , Display using R Language mapping differential gene expression “ Symmetric scatter diagram ” The process .

2 Data preprocessing

First, do some preprocessing for the data .

for example , The magnitude difference of gene expression value is too large , Take a logarithmic transformation ; Gene names are sorted according to whether they are different genes , Avoid being obscured by insignificant gene points in subsequent mapping , That is, the purpose of sequencing is to make the points of these significant genes all located at the top of the graph .

# Read sample data 

express <- read.delim('gene_diff.txt', sep = '\t')

# Take a gene expression value log(1+) transformation 

express$control <- log(express$control+1)

express$treat <- log(express$treat+1)

# Sort , The goal is to show the prominent genes on the front layer , Avoid being obscured by points of inconspicuous genes 

express$diff <- factor(express$diff, levels = c('up', 'down', 'none'))

express <- express[order(express$diff, decreasing = TRUE), ]

head(express)  # View the data table after reading and preprocessing 

3 Draw a scatter plot of differential genes , Color indicates the difference gene

Then you can use the preprocessed data to draw a graph .

The first type is the up regulation of genes 、 Down or non significant type shading , It is easy to identify different genes from the map . We use ggplot2 The method of mapping differential gene scatter plot .

# Draw a scatter plot , Prominently 、 Down regulated genes are distinguished by different colors 

library(ggplot2)

ggplot(express, aes(x = control, y = treat)) +

geom_point(aes(color = diff), size = 1) +  # Press up and down to specify the color of gene points 

scale_color_manual(values = c('red', 'gray', 'green4'), limit = c('up', 'none', 'down')) +  # Up and down gene color assignment 

theme_bw() +  # Background adjustment 

labs(x = 'control group', y = 'treat group', color = '') +  # Axis title settings 

geom_abline(intercept = 1, slope = 1, col = 'black', linetype = 'dashed', size = 0.5) +  # this 3 Sentences are used to add  |log2FC|>1  Threshold line of 

geom_abline(intercept = -1, slope = 1, col = 'black', linetype = 'dashed', size = 0.5) +

geom_abline(intercept = 0, slope = 1, col = 'black', linetype = 'dashed', size = 0.5)

The two axes represent the processing group （treat） And the control group （control）, The dots in the figure represent the average expression value of each gene in the two groups （ Have done log transformation ）.treat Group and control Group comparison , Up regulated genes are shown in red , Down regulated genes are shown in green . The dotted line in the figure represents |log2FC|=1 Threshold line at .

In this diagram , We can easily observe the overall distribution status and quantity comparison of different genes .

4 Draw a scatter plot of differential genes , Color means p value

There is no such thing as p Value information shows . So another idea is , Color represents p value , So you can get a gradient in the graph . Also use ggplot2 How to draw , Compared with the above process, there is only difference in color assignment .

# Press  p  Gradient dispersion plot of values 

ggplot(express, aes(x = control, y = treat)) +

geom_point(aes(color = pvalue), size = 0.8) +  # Press  p  Value size specifies the color of the gene point 

scale_color_gradient2(low = 'red', mid = 'darkgoldenrod2', high = 'royalblue2', midpoint = 0.5) +  # Gradient color assignment 

theme_bw() +  # Background adjustment 

labs(x = 'control group', y = 'treat group', color = 'p-value') +  # Axis title settings 

geom_abline(intercept = 1, slope = 1, col = 'black', linetype = 'dashed', size = 0.5) +  # this 3 Sentences are used to add  |log2FC|>1  Threshold line of 

geom_abline(intercept = -1, slope = 1, col = 'black', linetype = 'dashed', size = 0.5) +

geom_abline(intercept = 0, slope = 1, col = 'black', linetype = 'dashed', size = 0.5)

Similar to the above figure , The two axes represent the processing group （treat） And the control group （control）, The dots in the figure represent the average expression value of each gene in the two groups （ Have done log transformation ）, The dotted line in the figure represents |log2FC|=1 Threshold line at .

The difference from the above figure is , At this time, the gene is significant p Value coloring , Never significant > Prominently displayed in blue > Red gradient , So we get a kind of gradient information . So it's easy to see , The greater the difference in expression values between the two groups ,p The smaller the value. , The two trends are consistent , The emphasis is on describing the difference multiple and p Value the relationship between .