LabStats

  1. Introduction Long‑term monitoring of adherent cell cultures (e.g., Vero, MDCK) is central to vaccine development, viral propagation, and QC workflows in CRO environments. Standard analyses typically focus on short‑term growth metrics such as doubling time, confluence progression, and endpoint viability. While informative, these metrics overlook slow drifts, cyclic environmental influences, and structural transitions that emerge over multi‑day or multi‑week monitoring. Operational routines (e.g., weekly medium changes), environmental oscillations (e.g., daily temperature cycles), and metabolic rhythms can introduce periodic components into cell‑density trajectories. These patterns are rarely analyzed explicitly, despite their potential impact on viral yield, infection kinetics, and batch‑to‑batch reproducibility. This post demonstrates how cycleTrendR can extract long‑term trends, dominant cycles, and structural transitions from a realistically simulated adherent cell‑co...

  Irregular time series are everywhere: wearable sensors, physiological recordings, ecological  monitoring, behavioral experiments, and more. Yet most analytical tools still assume regular sampling,  clean signals, and stable measurement conditions. cycleTrendR was designed to bridge this gap, not by  reinventing time‑series analysis, but by integrating robust, domain‑agnostic methods into a single  coherent pipeline. This post provides a technical overview of how cycleTrendR works under the hood: the algorithms it uses, the architectural decisions behind the package, and the principles that guided its design. 1. A unified pipeline for irregular time‑series analysis cycleTrendR is built around a simple idea: cycles, trends, and structural changes should be extracted together, not in isolation. The internal workflow follows a modular but integrated structure: Raw signal    ↓ Preprocessing (dates, gaps, normalization)    ↓ Tr...