Predict Modality

CellMapper is a general framework for k-NN based mapping tasks in single-cell and spatial genomics. This variant uses CellMapper to project modalities from a reference dataset (train) onto a query dataset (test) in a PCA/CCA latent space.

CellMapper is a general framework for k-NN based mapping tasks in single-cell and spatial genomics. This variant uses CellMapper to project modalities from a reference dataset (train) onto a query dataset (test) in a modality-specific latent space computed with suitable scvi-tools models. For gene expression data, we use the scVI model on raw counts (nb likelihood), for ADT data, we use the scVI models on normalized counts (gaussian likelihood), and for ATAC data, we use the PeakVI model on raw counts. The actual CellMapper pipeline is modality-agnostic.

This is a solution developed by Team Guanlab - dengkw in the Neurips 2021 competition to predict one modality from another using kernel ridge regression (KRR) with RBF kernel. Truncated SVD is applied on the combined training and test data from modality 1 followed by row-wise z-score normalization on the reduced matrix. The truncated SVD of modality 2 is predicted by training a KRR model on the normalized training matrix of modality 1. Predictions on the normalized test matrix are then re-mapped to the modality 2 feature space via the right singular vectors.

A linear model regression method.

This method trains an encoder-decoder MLP model with one output neuron per component in the target. As an input, the encoders use representations obtained from ATAC and GEX data via LSI transform and raw ADT data. The hyperparameters of the models were found via broad hyperparameter search using the Optuna framework.

This folder contains the AXX solution to the OpenProblems-NeurIPS2021 Single-Cell Multimodal Data Integration. Team took the 4th place of the modality prediction task in terms of overall ranking of 4 subtasks: namely GEX to ADT, ADT to GEX, GEX to ATAC and ATAC to GEX. Specifically, our methods ranked 3rd in GEX to ATAC and 4th in GEX to ADT. More details about the task can be found in the competition webpage.

A hierarchical neural network encoder-decoder model based on Shuji Suzuki's 1st place solution in the Open Problems Multimodal Single-Cell Integration competition. The model uses task-specific preprocessing, SVD dimensionality reduction, and hierarchical MLP blocks with residual connections for learning cross-modal mappings.

The original author's code was adapted by GitHub Copilot (using Claude Sonnet) to integrate with this repository's framework and standards.

The average difference between the expression values and the predicted expression values.

The square root of the mean of the square of all of the error.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.

Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.