For the purpose of visualizing chemical structures, I will now post a few method that can achive this goal.
For the purpose of visualizing chemical structures, I will now post a few method that can achive this goal.
Continuing from the previous post on molecular formula assignment, I encountered a common issue: overfitting during TOF peak assignment. In some cases, a narrow TOF m/z window was being assigned with too many candidate formulas, resulting in high uncertainty for each individual ion. In other words, we might obtain a wrong intensity for a correct formula, which affecting the dat reliability.
To address this, I implemented a refinement step after formula assignment:
Only the local maxima within a given ppm range are retained, along with known TOF apex peaks.
In the previous post, I described the procedures for combining molecular information from both EESI-TOF and Orbitrap measurements. In our offline system, EESI-TOF can be regarded as semi-quantitative and more stable for long-term monitoring, making it ideal for tracking consistent ion trends. One key purpose of Orbitrap data is to guide the identification and fitting of TOF-detected ions, using its ultra-high resolution and accurate mass information.
However, in some cases, certain peaks are clearly detected by TOF but are either missing in Orbitrap or filtered out during the Orbitrap peak filtering and clustering process. These are often real chemical features, especially in complex mixtures, that may be weak or distorted in Orbitrap but still prominent in TOF.
To address this, I implemented a strategy to embed missing TOF peaks into the final clustered peak list, ensuring a more complete molecular representation, especially important in non-targeted or mixture-rich analyses.
After m/z calibration, the next step is to align ions detected across different retention times that likely originate from the same chemical species but appear at slightly shifted m/z values. To ensure robust clustering, it’s important to first remove rare or noisy ions that could interfere with pattern recognition.
After reading m/z data from Orbitrap raw files, it is essential to perform m/z calibration based on known reference masses of internal calibrants.
For long-term measurements, especially those spanning days or weeks, dynamic calibration is critical, since m/z drift can occur over time, and calibration parameters from Day 1 may not hold by Day 7. The function below allows automated chunk-wise recalibration across retention time.
The ultra-high resolution of Orbitrap mass spectrometry enables an unprecedented level of chemical detail. In my current research, we employ a direct-infusion Orbitrap approach (EESI inlet, Felipe et al, 2019) for non-targeted analysis. However, due to the lack of dedicated tools tailored for this application, I developed a new software package called OrbiTrack. This tool supports two core workflows:
(1) TOF peak guidance: Guiding ion fitting in EESI-TOF using high-resolution chemical information acquired at 120k resolution from Orbitrap.
(2) Directly utilization of Orbitrap MS1 data by integrating full time-series MS1 outputs, enabling online untargted molecular analysis without chromatographic sepeartion.
Linear regression is a fundamental method in data analysis to understand the relationship between two variables. Here, I summarize four reusable Python functions for performing and visualizing linear fitting:
All methods:
In this tutorial, I will demonstrate how to create a simple, interactive web app that visualizes correlations between variables. We will also walk you through the necessary environment setup and deployment steps.
In this post, I document the full procedure for turning a personal Python project into a public package, available on both GitHub and the Python Package Index (PyPI).
As a demonstration, I create a small package named PalAniSh (Palette of Animation of Shanghai), which extracts and displays color palettes from classical Chinese animations produced by the Shanghai Animation Film Studio.
Geojson provides several advantages compared to shapefiles:
lightweight, text-based, and easily readable format that can be easily shared, transmitted and used on the web.
It supports a wider range of data types compared to shapefile and can be used across multiple platforms and programming languages.
Additionally, geojson files have smaller file sizes, making them easier to store and process. These benefits make geojson a popular choice for geographic information data storage and exchange.
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