\section{Machine learning for taming cell heterogeneity in AML}

A common task for unsupervised methods is to use the intrinsic structure of the data to find clusters of cells. The advantage of these approaches is that cells can be grouped in an automatic and unbiased manner and thus, have the potential to discover unknown cell populations.

The popular single-cell processing packages Seurat \cite{Butler_2018} and Scanpy \cite{Wolf_2018} use a graph-based clustering approach combined with modularity optimization to group transcriptionally-similar cells together. Markers differentially expressed in each cluster can be found using different methods, including logistic regression. The cell identity of each cluster is assigned manually according to previous knowledge of cell-type specific markers. The main disadvantage of this approach is that the number of clusters depends on a resolution parameter assigned by the user (higher values will lead to a greater number of clusters) and thus, they may not faithfully reflect cell types.

The recently developed Single-Cell Clustering Assessment Framework (SCCAF) \cite{Miao_2020} generates an optimal number of clusters automatically. After the data has been clustered, SCCAF builds an ML classifier (logistic regression) using part of the data (training). By applying this model to the rest of the dataset (test), it iteratively merges clusters that appear indistinguishable to the ML classifier to produce the final optimum clustering. The output of the model is a weighted list of feature genes characteristic of every cluster that often include known markers for a given cell type and could potentially be used to detect common biomarkers of leukemic cell subsets from AML patients.

Another unsupervised method, single-cell consensus clustering (SC3) uses the first 4--7\% * N (number of cells) eigenvectors to build multiple k-means clustering solutions \cite{Kiselev_2017}. After hierarchical grouping, the final clustering is driven by the combination of multiple clustering solutions. The output is a list of marker genes that define each consensus cluster. While SC3 may not be the most sensitive method to find rare populations (such as LSCs), SC3 was successful in identifying clusters of prevalent genetic subclones with different mutations in myeloproliferative neoplasms \cite{Kiselev_2017}. A disadvantage of this method is that it does not scale well for datasets with more than 5,000 cells \cite{Andrews_2021}.

A recent unsupervised method, weighted-nearest neighbour (WNN), was used to cluster cells using multiple data modalities (e.g. surface proteins and transcriptomes) measured in the same cell \cite{Hao_2020}. This method uses k-nearest neighbours (kNN) to learn cell-specific modality ``weights''. When applied to a multiomics dataset generated from human bone marrow samples \cite{Stuart_2019}, it showed that the combination of surface proteins and gene expression was superior for identifying cell populations than using one data modality alone. Multiomic single-cell technologies quantifying both surface proteins and transcriptomes of individual cells (e.g. CITE-seq), could be ideally applied to the identification of surface targets for the design of cell based immunotherapies \cite{Stoeckius_2017}.