LUMINATE

LUMINATE infers longitudinal microbial community dynamics by estimating relative abundances and distinguishing biological from technical zeros in noisy read count data to enable analysis of temporal microbiome changes.

Key Features:

• Efficient Inference: Provides computational efficiency that surpasses existing methods by orders of magnitude for large-scale longitudinal microbiome datasets.
• Accurate Trajectory Estimation: Infers relative abundances from noisy read count data and distinguishes biological zeros (taxon absence) from technical zeros (below detection).
• Noise Reduction: Reduces technical noise, high dimensionality, and data sparsity by smoothing temporal trajectories observed in longitudinal datasets.
• Versatile Methodology: Includes four distinct programs tailored to different aspects of inference in longitudinal microbiome datasets.
• Experimental Parameter Estimation: Estimates community-level variance (cLV) parameters experimentally and supports training model parameters using pseudo-counts via the --use-pseudo-counts flag.

Scientific Applications:

• Disease Progression Studies: Characterizes temporal shifts in microbiome composition to identify microbial markers and associations with disease progression.
• Microbiome Intervention Trials: Quantifies microbial population trajectories to evaluate the impact of interventions on community dynamics.
• Ecological and Evolutionary Studies: Analyzes how microbial communities evolve or respond to environmental or host-related changes over time.

Methodology:

Applies advanced statistical techniques to infer relative abundances from noisy read count data, distinguish biological versus technical zeros, smooth temporal trajectories, and estimate community-level variance (cLV) parameters, with an option to train parameters using pseudo-counts (--use-pseudo-counts).