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https://github.com/loukesio/ggvolc
𝐠𝐠𝐯𝐨𝐥𝐜 effortlessly translates differential expression datasets and RNAseq data into informative volcano plots. Highlight genes of interest with unprecedented ease. With just a single line of code, visualize complex datasets, gaining deeper insights and simplifying data representation
https://github.com/loukesio/ggvolc
bioinformatics data-science data-visualization gro-seq rna-seq
Last synced: 4 days ago
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𝐠𝐠𝐯𝐨𝐥𝐜 effortlessly translates differential expression datasets and RNAseq data into informative volcano plots. Highlight genes of interest with unprecedented ease. With just a single line of code, visualize complex datasets, gaining deeper insights and simplifying data representation
- Host: GitHub
- URL: https://github.com/loukesio/ggvolc
- Owner: loukesio
- License: other
- Created: 2023-08-11T10:34:11.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-10-14T09:38:55.000Z (about 1 year ago)
- Last Synced: 2023-10-16T13:03:38.388Z (about 1 year ago)
- Topics: bioinformatics, data-science, data-visualization, gro-seq, rna-seq
- Language: R
- Homepage:
- Size: 5.53 MB
- Stars: 51
- Watchers: 3
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://www.tidyverse.org/lifecycle/#experimental)
[](https://cran.r-project.org/package=icons)
[](https://opensource.org/licenses/MIT)
## Install the `ggvolc` package
Install the package using the following commands 
```r
# for now, you can install the developmental version of ltc
# first you need to install the devtools package
# in case you have not already installed
install.packages("devtools")
# and load it
library(devtools)
# then you can install the dev version of the ltc
devtools::install_github("loukesio/ggvolc")
# and load it
library(ggvolc)
```
### How do I start?
Load the library and explore the example datasets!
```console
library(ggvolc)
#> Welcome to ggvolc version 0.1.0 !
#>
#> 888
#> 888
#> 888
#> .d88b. .d88b. 888 888 .d88b. 888 .d8888b
#> d88P"88b d88P"88b 888 888 d88""88b 888 d88P"
#> 888 888 888 888 Y88 88P 888 888 888 888
#> Y88b 888 Y88b 888 Y8bd8P Y88..88P 888 Y88b.
#> "Y88888 "Y88888 Y88P "Y88P" 888 "Y8888P
#> 888 888
#> Y8b d88P Y8b d88P
#> "Y88P" "Y88P"
#>
```
```
data(all_genes) # data.frame that contains the output of differentially expressed genes
head(all_genes,5) # have a look at the first 5 rows
#> genes baseMean log2FoldChange lfcSE stat pvalue
#> 1 GCR1 7201.5782 2.244064 0.2004959 11.192564 4.434241e-29
#> 2 OPI10 1009.4171 -2.257454 0.2096469 -10.767889 4.880607e-27
#> 3 AGA2 249.1173 3.829474 0.3623263 10.569132 4.143136e-26
#> 4 FIM1_1376 5237.5035 2.550409 0.2560379 9.961059 2.256459e-23
#> 5 HMG1 10838.1037 2.214300 0.2229065 9.933763 2.968371e-23
#> padj
#> 1 2.153711e-25
#> 2 1.185255e-23
#> 3 6.707736e-23
#> 4 2.739905e-20
#> 5 2.883475e-20
data(attention_genes) # here is a data.frame with genes that I want to mention to the volcano plot
head(attention_genes,5) # have a look at the first five rows
#> genes baseMean log2FoldChange lfcSE stat pvalue
#> 1 THI13 480.5194 1.585811 0.5219706 3.038122 2.380572e-03
#> 2 FBP1 22710.8428 -2.366733 0.3533032 -6.698871 2.100354e-11
#> 3 TRA1 4491.1343 -1.410696 0.4384316 -3.217595 1.292700e-03
#> 4 YDR222W 591.2289 -4.045918 0.9133881 -4.429572 9.442026e-06
#> 5 BRL1 4434.7712 2.375919 0.5037264 4.716686 2.397176e-06
#> padj
#> 1 1.371582e-02
#> 2 3.290780e-09
#> 3 8.565681e-03
#> 4 1.819838e-04
#> 5 5.850796e-05
```
Created on 2023-08-11 with [reprex v2.0.2](https://reprex.tidyverse.org)
### 1. Plot a simple volcano plot!
Install the package using the following commands
```
ggvolc(all_genes)
```
### 2. Add the genes of attention.
```
ggvolc(all_genes, attention_genes)
```
### 3. Add segments to indicate areas of significance.
```
ggvolc(all_genes, attention_genes, add_seg = TRUE) +
labs(title="Add segments of significance")
```
### 4. Indicate the size of point based on the log2FoldChange column.
```
ggvolc(all_genes, attention_genes, size_var = "log2FoldChange", add_seg = TRUE)
```
### 5. Indicate the size of the point based on the pvalue.
```
ggvolc(all_genes, attention_genes, size_var = "pvalue", add_seg = TRUE)
```
### 6. Add a table with the genes of interest.
```
plot <- ggvolc(all_genes, attention_genes, add_seg = TRUE) +
labs(title="Add a table with the genes of interest")
plot %>%
genes_table(attention_genes)
```
