Paper ID: 2410.09964 • Published Oct 13, 2024
Lower-dimensional projections of cellular expression improves cell type classification from single-cell RNA sequencing
Muhammad Umar, Muhammad Asif, Arif Mahmood
TL;DR
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Single-cell RNA sequencing (scRNA-seq) enables the study of cellular
diversity at single cell level. It provides a global view of cell-type
specification during the onset of biological mechanisms such as developmental
processes and human organogenesis. Various statistical, machine and deep
learning-based methods have been proposed for cell-type classification. Most of
the methods utilizes unsupervised lower dimensional projections obtained from
for a large reference data. In this work, we proposed a reference-based method
for cell type classification, called EnProCell. The EnProCell, first, computes
lower dimensional projections that capture both the high variance and class
separability through an ensemble of principle component analysis and multiple
discriminant analysis. In the second phase, EnProCell trains a deep neural
network on the lower dimensional representation of data to classify cell types.
The proposed method outperformed the existing state-of-the-art methods when
tested on four different data sets produced from different single-cell
sequencing technologies. The EnProCell showed higher accuracy (98.91) and F1
score (98.64) than other methods for predicting reference from reference
datasets. Similarly, EnProCell also showed better performance than existing
methods in predicting cell types for data with unknown cell types (query) from
reference datasets (accuracy:99.52; F1 score: 99.07). In addition to improved
performance, the proposed methodology is simple and does not require more
computational resources and time. the EnProCell is available at
this https URL