Welcome

This document contains some simple examples for how to use the advanced filtering options in https://plae.nei.nih.gov and a brief introduction to using the Seurat file to do custom tests.

Gene Focused

By “gene focused” we mean that:

  1. You have a gene you are interested in
  2. You want to learn more about where / when / what it is expressed in

In silico In Situ

For those who are most comfortable with stained in situ slices of the retina this visualization may be useful. The major cell types of the retina are laid on in rough anatomical positioning. The cell types are colored by intensity, with the brighter colors meaning that the gene is more highly expressed in that cell type. As an example we show expression of RHO (rhodopsin, a rod marker) and RPE65 (RPE marker).

In silico in situ screenshot with Rho gene

In silico in situ screenshot with RPE65 gene

UMAP - Table

If you are curious about a gene, then there are several ways you can learn about its retinal cell type expression patterning. We will use ATOH7, a transcription factor that regulates retinal ganglion development as our example gene.

The UMAP view is a two dimensional representation of the individual cells in the scEiaD. Cells that are closer together have more related gene expression profiles (and thus are likely to be similar cell types).

Let’s go the UMAP - Table viewer in plae:

Viz -> UMAP - Tables

umap visualization with atoh7 gene

Dark gray are cells which have no detectable ATOH7.

Yellow is the highest expression and dark purple is the lowest expression.

umap visualization with atoh7 with relaxed expression filtering

Show highest expressing cells

What if we want to see which cells have the highest expression? We can use the “Filter Gene Expression” slider to only show cells with expression above a log2(expression) value.

We see that the highest expressing cells are in the “center” before the branching happens.

umap visualization with atoh7 with tightened expression filtering

Species Filtering

By default plae shows data for all organisms in the database (human, mouse, macaque).

If we only want to see ATOH7 expression in human data, then that is very easy with the powerful “Scatter Filter Category” and “Gene Filter On” sections.

umap visualization with atoh7 on only human data

Table Information

While the UMAP view is cool looking, it can’t show you everything….what if we want to know what kind of cells are expressing ATOH7?

We can have quantified information on where ATOH7 is expressed by Cell Type (predicted) (this is our machine learned cell type labels) and organism.

screenshot of atoh7 gene info table

We see that about 50% of the mouse and human neurogenic cell type express ATOH7. In raw counts that is 16,449 of 28,811 total mouse neurogenic cells. They have an average expression of 1.86. You can sort or filter the table based on queries. If you wanted to see ATOH7 expression in the RGCs this is trivial to do by typing in the box below.

screenshot of atoh7 gene info table with filtering shown for cell type

This shows us that ATOH7 expression seems to be dropping in the maturing/mature RGCs (and is much lower in the macaque) relative to the neurogenic population.

Study filtering

As scEiaD is constructed from publicly available datasets, you can also filter the data to only show results from a specific paper. This may be useful if you using the results from that paper and want to check or confirm a finding.

You can see information about the papers / studies in scEiaD by using the adjacent “Make Meta Table” section as follows:

meta table study screenshot

We see that the Clark et al. 2019 study did Smart-seq2 and 10X across many developmental time points in mouse. They study_accession ID is SRP158081. We can use this ID to look at ATOH7 expression only in this study in both the UMAP view and the table view