PALMO

PALMO analyzes longitudinal bulk and single-cell multi-omics data to decompose variation, track features across time and participants, identify up- or down-regulated markers, and detect outlier events for studying dynamic biological processes.

Key Features:

• Five analytical modules: Five distinct analytical modules enable complementary examination of longitudinal multi-omics data.
• Decomposition of variations: Dissects intrinsic types of variation present within longitudinal multi-omics datasets.
• Feature collection across timepoints and participants: Identifies and collects stable or variable features across different time points and participants.
• Marker regulation analysis: Identifies up- or down-regulated markers across individual participant timepoints.
• Outlier investigation: Investigates potential outlier events within samples from the same participants.

Scientific Applications:

• Immunology: Enables analysis of temporal molecular changes relevant to immunological studies.
• Oncology: Enables study of temporal molecular changes relevant to oncology research.
• Personalized medicine: Supports tracking individual-level molecular trajectories for personalized monitoring and treatment evaluation.
• Disease mechanisms and treatment effects: Supports research into disease mechanisms and treatment effects over time.
• Biomarker identification: Facilitates identification of biomarkers for early diagnosis or prognosis from longitudinal multi-omics data.

Methodology:

Validated by testing on a complex longitudinal multi-omics dataset comprising five data modalities from the same samples and on six external datasets with diverse backgrounds.