Great Apes Macaque

Report card for Correlation, Tree, and Seurat Mapping on Macaque neocortex (Jorstad et al. 2023)

Overview

A taxonomy was initially built using the macaque neocortex single nucleus dataset. In building the taxonomy, 1000 binary marker genes were selected based on their gene expression from the single-cell transcriptome. Subsequently, the dataset was mapped to itself, termed self-projection, for evaluating the ideal performances of correlation, tree, and seurat mapping algorithms.

Quantitative analysis

The analysis evaluates the predictions of correlation, tree, and Seurat mappings in determining cluster labels in a self-projection evaluation.

Annotaion	F1-score
Cluster Correlation Mapping	0.869
Cluster Tree Mapping	0.820
Cluster Seurat Mapping	0.996

Correlation Mapping

1. Label-wise F1-score
   

2. Confidence values for correctly and incorrectly assigned labels
   

3. Confusion matrix (row-normalized)
   

Tree Mapping

1. Label-wise F1-score
   

2. Confidence values for correctly and incorrectly assigned labels
   

3. Confusion matrix (row-normalized)
   

Seurat mapping

1. Label-wise F1-score
   

2. Confidence values for correctly and incorrectly assigned labels
   

3. Confusion matrix (row-normalized)