Welcome

This document shows an example of making custom plots from plae data for use in your own presentations / publications. If you need assistance with making a custom figure / table and believe you are missing some data required, please contact me.

Step 1

Make plot in plae.nei.nih.gov and download data

Step 2

Look at source plotting code and copy out the relevant bits. The names of the functions (I think) are self-explanatory for which kind of plot they make. This example is remaking the “Expression Plot” so we will open up the make_exp_plot.R file. For all of these functions you can just go straight to the bottom and find the plotting part near there.

Copied code bit

  ggplot(aes(x=!!as.symbol(input$exp_plot_facet), 
             y = !!as.symbol(input$exp_plot_ylab), 
             color = !!as.symbol(grouping_features))) +
      geom_boxplot(color = 'black', outlier.shape = NA) +
      ggbeeswarm::geom_quasirandom(aes(size = `Total Cells`), groupOnX = TRUE) +
      cowplot::theme_cowplot() +
      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
      scale_radius(range=c(2, 6)) +
      scale_colour_manual(values = rep(c(pals::alphabet() %>% unname()), 20)) +
      theme(legend.position="bottom") +
      facet_wrap(ncol = as.numeric(input$exp_plot_col_num), scales = 'free_y', ~Gene)

Step 3

Load in the downloaded data into R

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.0.2     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggbeeswarm)
library(cowplot)
library(pals)
plae_exp_data <- read_csv("~/Downloads/plae_exp_2023-02-16.csv")

## New names:
## * `` -> ...1

## Rows: 1732 Columns: 8

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): CellType_predict, Gene, study_accession
## dbl (5): ...1, Cell # Detected, Total Cells, % of Cells Detected, Mean log2(...

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

plae_exp_data

## # A tibble: 1,732 × 8
##     ...1 CellType_predict  Gene   study_accession `Cell # Detecte… `Total Cells`
##    <dbl> <chr>             <chr>  <chr>                      <dbl>         <dbl>
##  1     1 Retinal Ganglion… PAX6 … SRP186396                    555           557
##  2     2 Retinal Ganglion… PAX6 … SRP238409                    111           112
##  3     3 Amacrine Cell     PAX6 … SRP257758                   8936          9477
##  4     4 Retinal Ganglion… PAX6 … SRP200599                   1039          1081
##  5     5 Retinal Ganglion… PAX6 … SRP255195                   6459          7013
##  6     6 AC/HC Precursor   PAX6 … SRP200599                    149           152
##  7     7 Horizontal Cell   PAX6 … SRP200599                     92            97
##  8     8 Amacrine Cell     PAX6 … SRP200599                    153           164
##  9     9 Amacrine Cell     PAX6 … E-MTAB-7316                  195           222
## 10    10 Corneal Epitheli… PAX6 … SRP251245                   7787          8619
## # … with 1,722 more rows, and 2 more variables: % of Cells Detected <dbl>,
## #   Mean log2(Counts + 1) <dbl>

Step 4

Adjust the code chunk above and make a plot