Experimental techniques to measure multiple modalities within the same single cell are increasingly becoming available. The demand for these measurements is driven by the promise to provide a deeper insight into the state of a cell. Yet, the modalities are also intrinsically linked. We know that DNA must be accessible (ATAC data) to produce mRNA (expression data), and mRNA in turn is used as a template to produce protein (protein abundance). These processes are regulated often by the same molecules that they produce: for example, a protein may bind DNA to prevent the production of more mRNA. Understanding these regulatory processes would be transformative for synthetic biology and drug target discovery. Any method that can predict a modality from another must have accounted for these regulatory processes, but the demand for multi-modal data shows that this is not trivial.
Figure 1: Overview of the results per method. This figures shows the mean of the scaled scores (group Overall), the mean scores per dataset (group Dataset) and the mean scores per metric (group Metric).
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Results table of the scores per method, dataset and metric (after scaling). Use the filters to make a custom subselection of methods and datasets. The “Overall mean” dataset is the mean value across all datasets.
Single-cell CITE-Seq (GEX+ADT) data collected from bone marrow mononuclear cells of 12 healthy human donors (Luecken et al. 2021).
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 Multiome (GEX+ATAC) data collected from bone marrow mononuclear cells of 12 healthy human donors (Luecken et al. 2021).
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 Multiome (GEX+ATAC) data collected from bone marrow mononuclear cells of 12 healthy human donors (Luecken et al. 2021).
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 (GEX+ADT) data collected from bone marrow mononuclear cells of 12 healthy human donors (... 2024).
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 (GEX+ADT) data collected from bone marrow mononuclear cells of 12 healthy human donors (... 2024).
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 (GEX+ADT) data collected from bone marrow mononuclear cells of 12 healthy human donors (Luecken et al. 2021).
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.
A kernel ridge regression method with RBF kernel (Lance et al. 2022)
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.
Fix, Evelyn, and J. L. Hodges. 1989. “Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties.”International Statistical Review / Revue Internationale de Statistique 57 (3): 238. https://doi.org/10.2307/1403797.
Lance, Christopher, Malte D. Luecken, Daniel B. Burkhardt, Robrecht Cannoodt, Pia Rautenstrauch, Anna Laddach, Aidyn Ubingazhibov, et al. 2022. “Multimodal Single Cell Data Integration Challenge: Results and Lessons Learned,” April. https://doi.org/10.1101/2022.04.11.487796.
Wilkinson, G. N., and C. E. Rogers. 1973. “Symbolic Description of Factorial Models for Analysis of Variance.”Applied Statistics 22 (3): 392. https://doi.org/10.2307/2346786.
