SC-JNMF: single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization.

Single-cell RNA-sequencing is a rapidly evolving technology that enables us to understand biological processes at unprecedented resolution. Single-cell expression analysis requires a complex data processing pipeline, and the pipeline is divided into two main parts: The quantification part, which converts the sequence information into gene-cell matrix data; the analysis part, which analyzes the matrix data using statistics and/or machine learning techniques. In the analysis part, unsupervised cell clustering plays an important role in identifying cell types and discovering cell diversity and subpopulations. Identified cell clusters are also used for subsequent analysis, such as finding differentially expressed genes and inferring cell trajectories. However, single-cell clustering using gene expression profiles shows different results depending on the quantification methods. Clustering results are greatly affected by the quantification method used in the upstream process. In other words, even if the original RNA-sequence data is the same, gene expression profiles processed by different quantification methods will produce different clusters. In this article, we propose a robust and highly accurate clustering method based on joint non-negative matrix factorization (joint-NMF) by utilizing the information from multiple gene expression profiles quantified using different methods from the same RNA-sequence data. Our joint-NMF can extract common factors among multiple gene expression profiles by applying each NMF under the constraint that one of the factorized matrices is shared among multiple NMFs. The joint-NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to conventional clustering methods, which use only a single gene expression profile. Additionally, we showed the usefulness of discovering marker genes with the extracted features using our method.

Following Embryonic Stem Cells, Their Differentiated Progeny, and Cell-State Changes During iPS Reprogramming by Raman Spectroscopy.

Monitoring cell-state transition in pluripotent cells is invaluable for application and basic research. In this study, we demonstrate the pertinence of noninvasive, label-free Raman spectroscopy to monitor and characterize the cell-state transition of mouse stem cells undergoing reprogramming. Using an isogenic cell line of mouse stem cells, reprogramming from neuronal cells was performed, and we showcase a comparative analysis of living single-cell spectral data of the original stem cells, their neuronal progenitors, and reprogrammed cells. Neural network, regression models, and ratiometric analyses were used to discriminate the cell states and extract several important biomarkers specific to differentiation or reprogramming. Our results indicated that the Raman spectrum allowed us to build a low-dimensional space allowing us to monitor and characterize the dynamics of cell-state transition at a single-cell level, scattered in heterogeneous populations. The ability of monitoring pluripotency by Raman spectroscopy and distinguishing differences between ES and reprogrammed cells is also discussed.

Performance assessment of displacement-field estimation of the human left atrium from 4D-CT images using the coherent point drift algorithm.

BACKGROUND: Cardiac four-dimensional computed tomography (4D-CT) imaging is a standard approach used to visualize left atrium (LA) deformation for clinical diagnosis. However, the quantitative evaluation of LA deformation from 4D-CT images is still a challenging task. We assess the performance of LA displacement-field estimation from 4D-CT images using the coherent point drift (CPD) algorithm, which is a robust point set alignment method based on the expectation-maximization (EM) algorithm. METHOD: Subject-specific LA surfaces at 20 phases/cardiac cycles were reconstructed from 4D-CT images and expressed as sets of triangular elements. The LA surface at the phase that maximized the LA surface area was assigned as the control LA surface and those at the other 19 phases were assigned as observed LA surfaces. The LA displacement-field was estimated by solving the alignment between the control and observation LA surfaces using CPD. RESULTS: Global correspondences between the estimated and observed LA surfaces were successfully confirmed by quantitative evaluations using the Dice similarity coefficient and differences of surface area for all phases. The surface distances between the estimated and observed LA surfaces ranged within 2 mm, except at the left atrial appendage and boundaries, where incomplete data, such as missing or false detections, were included on the observed LA surface. We confirmed that the estimated LA surface displacement and its spatial distribution were anisotropic, which is consistent with existing clinical observations. CONCLUSION: These results highlight that the LA displacement field estimated by CPD robustly tracks global LA surface deformation observed in 4D-CT images.