IceR

IceR improves proteome coverage and data completeness in label-free data-dependent acquisition (DDA) proteomics by propagating peptide identifications and reducing missing values to enable consistent protein quantification across global and single-cell studies.

Key Features:

• Hybrid Peptide Identification Propagation (PIP): IceR employs a hybrid PIP that leverages ion current information from DDA data to propagate peptide identifications across runs and reduce missing values.
• Combination of DDA and DIA advantages: The approach integrates the high identification rates of DDA with the low missing-value characteristics of data-independent acquisition (DIA) to improve quantification precision, accuracy, and reliability.
• Enhanced Quantification Sensitivity: IceR increases the number of reliably quantified proteins in published plasma and single-cell proteomics datasets, improving discriminability between single-cell populations.
• Developmental Trajectory Reconstruction: More complete quantitative datasets produced by IceR enable reconstruction of developmental trajectories with greater accuracy.

Scientific Applications:

• Global Proteomics: Improves data completeness and protein quantification for large-scale label-free DDA proteomic studies.
• Plasma Proteomics: Increases the number of reliably quantified proteins in plasma datasets, enhancing quantitative sensitivity.
• Single-Cell Proteomics: Enhances sensitivity and reliability for low-input single-cell proteomics and improves discrimination between cell populations.
• Developmental Trajectory Analysis: Supports reconstruction of cellular developmental and differentiation trajectories from proteomic data.

Methodology:

Hybrid peptide identification propagation (PIP) that leverages ion current information from DDA data and combines DDA and DIA characteristics to reduce missing values and improve quantification precision and accuracy.