This page contains additional data supporting the paper:

"Evolution of pallium, hippocampus and cortical cell types revealed by single-cell transcriptomics in reptiles" by Maria Antonietta Tosches, Tracy M. Yamawaki, Robert K. Naumann, Ariel A. Jacobi, Georgi Tushev and Gilles Laurent (link)

Raw data have been deposited in the NCBI Sequence Read Archive, BioProject number PRJNA408230 . Analysis scripts can be found on github@molgen . Processed data can be downloaded here .

Below, one can choose a dataset among the following: lizard_all_cells, lizard_neurons, turtle_all_cells, turtle_neurons. In plot gene expression, it is possible to visualize the expression of the gene(s) of interest. With find differentially expressed genes one can find the genes that differentiate two clusters (or groups of clusters) in the dataset of choice.

Gene names come from the lizard Pogona vitticeps pvi1.1 assembly and the turtle Chrysemys picta 3.0.3 assembly. Some genes are not annotated with the standard HGCN names: for example, lizard and turtle ETV1 are annotated as Q9YHW6_CHICK and LOC101935936, respectively. Lists of lizard-human and turtle-human one-to-one orthologs can be found here.

Select dataset

Plot gene expression

tSNE plot

Violin plot

In the tSNE plot, cells (dots) are color-coded according to the normalized expression level of the selected gene(s).

The violin plots show the distribution of the expression values (normalized UMIs) of the selected gene(s) in each cluster. The plot is color-coded according to cluster identity. For the lizard_all_cells and turtle_all_cells datasets, colors correspond to the cluster colors in the tSNE plots above. For the lizard_neurons and the turtle_neurons datasets, glutamatergic clusters ("e") are colored according to the brain region of origin (see small tSNE plot above), and GABAergic clusters ("i") are color-coded according to cell class (see the paper for more details).

Plots are generated with the R package Seurat (Macosko et al., 2015).

Find differentially expressed genes

Find differentially expressed genes among clusters. Be patient, this is SLOW!


Group 1 and Group 2: the clusters or groups of clusters to compare, separated by a space.
Minimum fraction of cells: minimum fraction of cells in either of the two groups to compare. Only genes detected in a fraction of cells above this threshold will be considered for differential expression analysis. Example: a threshold of 0.2 will exclude all genes expressed in less than 20% of cells of either group.
Average difference threshold: a threshold for the differential expression testing. The tool will report only the genes which have an average differential expression (in log(e)-scale) above this threshold. For example: with a threshold of 0.4, only genes with an average differential expression between the two groups equal or greater than 0.4 (in log(e) scale, corresponding to a ~1.5 difference) will be returned by the analysis.


Genes are ranked by avg diff. avg diff is the difference (in log(e) scale) of the gene average expression value in the two groups compared.
p-value is the p-value for differential expression.
fraction in group 1 and fraction in group 2 are the fractions of cells in groups 1 and 2 where the gene is detected.

Differential expression is calculated with the R package MAST (Finak et al Genome Biol. 2015).

Differentially expressed genes