PCA-based spatial domain identification with state-of-the-art performance.

Motivation: The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data.

Results: Following Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability.

Immunomodulatory Interventions Based on a Bioinformatics Study of TLR2 in Glioblastoma Multiforme.

Objective: One of the most malignant types of tumors with a remarkable ability of recurrence rate and aggressiveness is glioblastoma multiforme(GBM). Anyway, according to the restricted remedies accessible for the treatment of this serious tumor, there is no confident and stable therapeutic strategy. Notably, bioinformatics analysis can detect many effective genes in the diagnosis and treatment of GBM.

Comparative evaluation of feature reduction methods for drug response prediction.

Personalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared to the limited number of samples presents significant challenges. Knowledge-based feature selection methods are particularly suitable for drug response prediction, as they leverage biological insights to reduce dimensionality and improve model interpretability.

The impact of the xSPECT reconstruction algorithms on the recovery coefficients value for small tumors: a phantom study with 177Lu.

Quantifying small tumors is still a challenge due to the partial volume effect (PVE). Although iterative reconstruction had promising results with a better recovery coefficient (RC), it suffers from the PVE. RC values typically depend on the reconstruction method, which may affect on Lu quantifying.