LipidTrend

LipidTrend implements a permutation-based statistical test to identify significant differences in lipidomic features between groups, incorporating Gaussian kernel smoothing to improve stability and accuracy for small sample sizes.

Key Features:

• Permutation-based statistical testing: Identifies significant differences in lipidomic features between groups using permutation methods.
• Gaussian kernel smoothing: Applies Gaussian kernel smoothing of region statistics to improve stability and accuracy, particularly for small sample sizes.
• 1D visualization: Includes a plotting function for visualizing significant tendencies in 1D feature data.
• 2D visualization: Includes a plotting function for visualizing significant tendencies in 2D feature data.

Scientific Applications:

• Lipidomics differential analysis: Detects statistically significant differences in lipidomic features between experimental groups.
• Small sample size studies: Provides improved statistical stability and accuracy for lipidomics experiments with limited sample sizes.
• Lipid feature tendency visualization: Generates plots to visualize significant patterns in one-dimensional and two-dimensional lipid feature data.

Methodology:

LipidTrend uses a permutation-based statistical test with Gaussian kernel smoothing of region statistics.