In Ni-Ar/niar: Nice Interactive Analysis with R

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Intro

ENCODE released a nice dataset of different genomics data at different stages of development in different tissues.

I took their RNA-seq data and analysed it with vast-tools to make a plot for the supplementary figure 1 of our paper.

While analysing the data I ended up making a function that queries the vast-tools output tables and a nice plotting function. Here I show how to use them.

Pax6 exon 6 example

Import pre-precessed data

These vignette showcase how to analyse one single alternative splicing (AS) event at the time. This is not a high-level data exploration. The function get_mouse_tissue_devel_tbl() in this package let's you import into R all the metadata, gene expression levels, and PSI of an AS event. All you need is a valid mouse ensembl gene id and an AS event vast-tools id, that you can find in VastDB.

For example if you want to explore Pax6 exon 6 PSI across different tissues and embryonic stages, simply run

Pax6 <- get_mouse_tissue_devel_tbl(ensembl_gene_id = "ENSMUSG00000027168", 
                                   vst_id = "MmuEX0033804")

where ENSMUSG00000027168 is the ensembl gene id of Pax6 and MmuEX0033804 is the mouse AS id.

The pre-processed data is stored on the CRG cluter at /users/mirimia/narecco/projects/07_Suz12AS/data/INCLUSION_tbl/Mouse_Development/vast_tools/vast_out. You should be able to access it easily, but if you get an error that might be related to access rights, please open a GitHub issue or write me an email. Of course this wrapper works only if you have access to the CRG cluster and works very well using the CRG RStudio Server IDE. If this was a proper R package the pre-processed data would be attached to the package, but for sack of convenience I wanted to keep the niar package lightweight.

Plot the data

To visually represent the data like in the Arecco, Mocavini et al. publication simply run:

plot_mouse_tissue_devel(data_tbl = Pax6, legend = "inside", colour_bar = 'BlueRed')

The developmental stages are on the X-axis, and the different tissues are on the Y-axis. The dots size represent the expression levels of the gene specified with ensembl_gene_id when querying the data. The dots colour represent the PSI value of the exon specified with vst_id before. If you don't like this colour palette you can use colour_bar = 'viridis' as an alternative. Also the legend position can adjusted with legend = "outside".

plot_mouse_tissue_devel(data_tbl = Pax6, legend = "outside", colour_bar = 'viridis')

There are other additional features like limiting the PSI range with PSI_limits = c(0, 100) or deciding PSI threshold to show PSI numbers with black text rather than white (PSI of 100 in black is hard to read on a dark purple background).

Lastly you can save the plot on the fly with save_plot = TRUE and specifying the location where to save with out_plot_dir = /path/to/file and the plot name with the plot_name parameter. These last parameters have good defaults options so not really required in my option. To know more run: ?get_mouse_tissue_devel_tbl().

PTBP1 & PTBP2 example

These 2 function can also be used to plot an exon PSI and the expression of another gene. To display this I like to use the Ptbp1/2 NMD-dependent cross-regulation. (See PMID: 26293963 and 17606642 for details).

• ENSMUSG00000006498 = Ptbp1; MmuEX0037628 = Ptbp1 exon 9

• ENSMUSG00000028134 = Ptbp2; MmuEX0037640 = Ptbp2 exon 11

Briefly, Ptbp1 splicing factor repress the inclusion Ptbp2 exon 11 triggering NMD and resulting in lower level of PTBP2 in non-neuronal cells.

Here I plot the expression of Ptbp1 and Ptbp2 exon 11 PSI.

get_mouse_tissue_devel_tbl(ensembl_gene_id = "ENSMUSG00000006498",
                           vst_id = "MmuEX0037640") |>
  plot_mouse_tissue_devel(legend = "inside", colour_bar = 'BlueRed', 
                          title = "Ptbp1 expression vs Ptbp2 exon 11 inclusion", 
                          blck_wht_PSI_col_thshld = 75)

As one can see the tissues and developmental stages where the exon is more lowly included are those where Ptbp1 are more included.

Here instead I show how the gene expression (expressed in log2 TPMs) of Ptbp2 is higher in the tissues where the exon 11 inclusion levels are higher.

get_mouse_tissue_devel_tbl(ensembl_gene_id = "ENSMUSG00000028134",
                           vst_id = "MmuEX0037640") |>
  plot_mouse_tissue_devel(legend = "inside", colour_bar = 'BlueRed',
                          title = "Ptbp2 expression vs Ptbp2 exon 11 inclusion", 
                          blck_wht_PSI_col_thshld = 75)

Note how the data retrieving function output is piped on the fly to the plotting function input with |>. If you don't have R 4.1+ you can use magrittr %>% operator.

Ni-Ar/niar documentation built on Feb. 3, 2025, 9:25 a.m.