Report card for Correlation Mapping on Human SEA-AD benchmark

Overview

The accuracy of cell type mapping using the correlation algorithm was evaluated against the SEA-AD human MTG benchmark.

In summary, Correlation Mapping was able to achieve strong accuracy at all resolution of the human MTG taxonomy containing donor-specific batch effects.

• Summary:
  • Inputs X are log(CPM) normalized expression values of marker genes.
  • Hierarchy was encoded by Class, Subclass, Supertype.
  • Confidence values were derived via bootstraping.
• Runtime: 1.835 Hours
• Version: X.Y.Z
• Repository: TBD
• Publication: –

Annotaion	F1-score
Class	0.999
Subclass	0.985
Supertype	0.871

Tasks

• Primary tasks:
  1. Classification of scRNA-seq samples into Supertypes.
  2. Determining generalization of Correlation Mapping classification to samples from new donors under varying degrees of Alzheimers pathology.
• Users: AIBS scientists and community mapping tool users.
• Out of scope: Classification on other modalities (e.g. SMART-seq, Patch-seq, MERFISH), or regions (e.g. V1), or species (e.g. primate)

Metrics

• Accuracy
• Precision, Recall, F1-score on validation sets.

Reference and query evaluation data

• Reference
  • Human MTG single nucleus 10xV3 dataset from aged healthy individuals.
  • Supertype and donor metadata provided for each reference sample.
• Query
  • Human MTG single nucleus 10xV3 data from donors of varying Alzheimers pathology.
    • No AD
    • Low
    • Intermediate
    • High

Quantitative analysis

Here we evaluate Correlation Mapping at predicting high quality samples for each of the query datasets from the human SEA-AD benchmark.

Class metrics:

1. Label-wise F1-score
2. Confidence values for correctly and incorrectly assigned labels
3. Label-wise recall
4. Label-wise precision
5. Confusion matrix (row-normalized)

Sublass metrics:

1. Label-wise F1-score
2. Confidence values for correctly and incorrectly assigned labels
3. Label-wise recall
4. Label-wise precision
5. Confusion matrix (row-normalized)

Supertype metrics:

1. Label-wise F1-score
2. Confidence values for correctly and incorrectly assigned labels
3. Label-wise recall
4. Label-wise precision
5. Confusion matrix (row-normalized)

Donor effect analysis

Here we evaluate Correlation Mapping at correctly predicting the Subclass label for increasingly sever Alzheimers pathology.

Annotation	Query	F1-score
Supertype	Reference_MTG_RNASeq_benchmark	0.986
Supertype	Not_AD_MTG_RNASeq_benchmark	0.988
Supertype	Low_MTG_RNASeq_benchmark	0.987
Supertype	Intermediate_MTG_RNASeq_benchmark	0.986
Supertype	High_MTG_RNASeq_benchmark	0.983

Low quality sample analysis

Here we evaluate how Correlation Mapping predicts labels for low-quality samples in the query data with predefined QC flags.

And how confident the model is when predicting labels for high and low quality data for each subclass.

Recommendations and caveats

• At the Class and Subclass level, for high quality RNA-seq data - Correlation Mapping makes few errors.
• Correlation Mapping robustly classify samples under varying conditions imparting donor and disease specific changes in gene expression.
• When Correlation Mapping makes a mistake at the Supertype level, the predicted label is typically within the same Subclass.