# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 2 Materials and Methods
### Dataset Description  
The **Peripheral Blood Mononuclear Cells (PBMC3k)**, PBMC3k dataset from 10x Genomics consists of single-cell RNA sequencing data from 3000 Peripheral Blood Mononuclear Cells (PBMCs) collected from a healthy donor with cells containing small amount of RNA per cell [56]. The dataset includes approximately 3000 cells that were sequenced on an Illumina NextSeq 500, with 69,000 reads per cell. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Key Hyper-Parameters** & **Training Configuration** \\ \hline \multirow{3}{*}{Decision Tree} & Criterion: gini, & 80\% train data, pruning via \\  & Max Depth: None, & max depth \\  & Min Samples Split: 2, & \\ \hline \multirow{3}{*}{Random Forest} & Estimators: 100, & \\  & Max Features: sort, & \\  & Max Depth: Tuned, & \\  & Bootstrap: True & \\ \hline \multirow{3}{*}{Gradient Boosting (XGBoost)} & Estimators: 300, & \\  & Learning Rate: 0.1, & \\  & Max Depth: 3, & \\  & Early Stopping & \\ \hline \multirow{3}{*}{Support Vector Machine (SVM)} & Kernel: RBF, & \\  & Regularization (C): k, & \\  & Tolerance: \(1\times 10^{-3}\) & \\ \hline \multirow{3}{*}{Logistic Regression} & Regularization: L2, & \\  & Max Iterations: 500, & \\  & Multi-Class: Multinomial & \\ \hline \multirow{3}{*}{K-Nearest Neighbours (KNN)} & Neighbours (k): 5, & \\  & Distance Metric: Euclidean, & \\  & Weighting: distance-based & \\ \hline \multirow{3}{*}{Naive Bayes} & Distribution: Gaussian, & \\  & Variance Smoothing: \(1\times 10^{-9}\) & Log-transformed input \\ \hline \multirow{3}{*}{Transformer} & Embedding Dimension: 512, & \\  & Encoder Layers: 6, & \\ \cline{1-1}  & Attention Heads: 8, & \\ \cline{1-1}  & Dropout: 0.1, Optimizer: Adam, & \\ \cline{1-1}  & Learning Rate: 0.0001 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyper-parameter description for ML techniques. The **Multiple sclerosis (MS)** dataset has been accessed ([[https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-35/results/tsne](https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-35/results/tsne)]([https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-35/results/tsne](https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-35/results/tsne))) on 15 October 2024 from the European Molecular Biology Laboratory--European Bioinformatics Institute (EMBL-EBI), and it consists of nine healthy control samples and twelve MS samples [57]. We split the control samples into the reference set for model fine-tuning and held out the MS samples as the query set for evaluation. This setting serves as an example of out-of-distribution data. We excluded three cell types: B cells, T cells, and oligodendrocyte B cells, which only existed in the query dataset. The final cell counts were 7844 in the training reference set and 13,468 in the query set. The provided cell type labels from the original publication were used as ground truth labels for evaluation. The data-processing protocol involved selecting HVGs to retain 3000 genes. The utilized **Zhengh68K**[58] dataset involves single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) and bone marrow mononuclear cells (BMMCs) collected from transplant patients and healthy subjects, including the transcriptome of 68,000 PBMCs and 250,000 cells across 29 different samples. The dataset contains 68,000 cells captured from fresh peripheral blood mononuclear cells (PBMCs) from a healthy donor and 250,000 cells from a variety of sources, including cell lines and patient-derived samples, such as bone marrow cells from transplant patients. These cells were processed in a droplet-based system, with up to 8 samples being processed in parallel per run. In summary, the dataset used in this study provided a comprehensive look at the transcriptomic landscape of PBMCs and BMMCs, aiding in the identification of distinct subpopulations and cell types across a range of biological conditions. It provides comprehensive data for studying the cell annotation process.