Devon Kohler

Devon Kohler

PhD student at Northeastern University. Studying causal and statistical inference applications to mass spectrometry-based proteomics.

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

publications

Recent Developments in MSstats Ecosystem: A collection of statistical methods for general scalable quantitative analysis of proteomic experiments.

Published in HUPO, 2022

The MSstats ecosystem is a family of open-source R/Bioconductor packages implementing statistical methods for quantitative mass spectrometry-based proteomic experiments. Here we review its recent developments, as well as advances in previously available methods and implementations.

Recommended citation: Kohler, D., Stankiak, M., & Vitek, O. (2022). Recent Developments in MSstats Ecosystem: A collection of statistical methods for general scalable quantitative analysis of proteomic experiments. HUPO News. https://hupo.org/News/12904194

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications

Published in Nature Protocols, 2022

We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins.

Recommended citation: Malinovska, L., Cappelletti, V., Kohler, D. et al. Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications. Nat Protoc 18, 659–682 (2023). https://www.nature.com/articles/s41596-022-00771-x

MSstatsPTM: Statistical Relative Quantification of Post-translational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Published in Molecular & Cellular Proteomics, 2023

This manuscript proposes a versatile statistical analysis framework that accurately detects relative changes in PTMs.

Recommended citation: Kohler D, et al. MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics. Mol Cell Proteomics. 2023 Jan;22(1):100477. https://www.mcponline.org/article/S1535-9476(22)00285-7/fulltext#secsectitle0020

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

Published in Journal of Proteome Research, 2023

To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM.

Recommended citation: Kohler D, et al. MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments. J Proteome Res. 2023 Feb 3;22(2):551-556. https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00603

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

Published in Journal of Proteome Research, 2023

Here, we introduce MSstats version 4.0 (v4.0), a statistical methodology and core package in the family of R/Bioconductor packages designed for statistical analysis of experiments with chromatography-based quantification.

Recommended citation: Kohler D, et al. MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale. J Proteome Res. 2023 May 5;22(5):1466-1482. https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00834

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Published in Nature Protocols, 2024

This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows.

Recommended citation: Kohler, D., Staniak, M., Yu, F. et al. An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing. Nat Protoc (2024). https://www.nature.com/articles/s41596-024-01000-3

talks

MSstatsPTM: an R/Bioconductor software for detecting quantitative changes in post-translational modifications

Published:

The scientific community widely utilizes mass spectrometry (MS)-based proteomics to quantify the abundance of proteins and their post-translational modifications (PTMs). Experiments targeting PTMs face several specific challenges. These include the low abundance, few representative peptides, and convolution with abundance changes in the overall protein expression. Due to these challenges, a robust approach to estimate relative changes in PTMs should combine PTM sites over several peptides, replicates in multiple conditions, and consider sources of confounding present in the experiment. We propose a general statistical model and workflow that is both reproducible and comprehensive. The method measures PTM and protein abundance by summarizing intensities through Tukey’s median polish method. Then a model based on the family of linear mixed-effects models is fit. Finally, the PTM abundances are adjusted to remove variance from changes in the overall protein. The package can handle a diverse range of acquisition types, including label-free, DDA, DIA, and TMT. We implement this model in the free and open-source R package MSstatsPTM and available at the links below.

Explorations of causal probabilistic programming approaches for rule-based models of biological signaling pathways

Published:

Rule-based models handle the complexity of biological signaling pathways. Biological signalling pathways are complex systems that underlie many cellular process and whose dysregulation is the source of many morbidities. To address the combinatorial complexity of interactions, patterns of transitions between model states can be compactly represented as probabilistic events using rule-based models.

MSstatsShiny: A Multipurpose UI for Reproducible Analysis of Quantitative Proteomic Experiments

Published:

Quantitative mass spectrometry proteomic experiments require robust analytic and modeling techniques to ensure the results are correctly interpreted. There are a variety of different tools that can assist researchers in analysis, however they are generally only suited for one type of experimental design and are usually implemented in coding packages. To address these challenges, we have created the UI MSstats-Shiny, an RShiny based UI integrated with the packages Msstats, MSstatsTMT, and MSstatsPTM providing all users an end-to-end pipeline that can analyze a variety of experimental designs.

Single-cell mass spectrometry-based proteomics enables causal inference in observational studies

Published:

Single cell proteomics greatly increases the number of replicates and cellular resolution in MS-based proteomic experiments. However, these experiments are expensive (especially when targeting perturbations). Causal inference methods, which are typically challenging to apply to traditional bulk-MS due to the lack of replicates, allow us to estimate the impact of perturbations from purely observational data. We propose a method and workflow for predicting the effect of interventions in observational single cell MS experiments and apply the workflow to a recent observational single cell experiment.

Analysis of Quantitative MS-based Proteomics Experiments using MSstats and MSstatsTMT

Published:

Recent advances in the techniques and technologies used in Mass Spectrometry (MS)-based proteomics have greatly increased the variety and complexity of experimental designs in the field. Experiments can differ in the labeling method (label-free vs tandem mass tag), acquisition type (DDA/DIA/SRM/PRM), biological question of interest, and differing numbers of conditions and replicates. Statistical methods used to analyze these experiments need to be flexible enough to fit these design complexities, while being robust enough to not overfit to one specific design. The MSstats family of R packages is widely used to analyze the results of MS-based proteomics experiments and has been shown to outperform other methods on a variety of experimental designs. In this webinar we will review the core workflow and methods behind MSstats and its extensions, highlighting critical choices that need to be made in each step of the analysis. We will review how different analysis choices affect the final protein-level conclusions made on the experiment and discuss how to avoid potential analysis pitfalls. After reviewing the methods in MSstats, we will present a hands-on session using MSstatsShiny, where participants are provided with two datasets (label-free DIA and TMT-DDA) and are invited to follow along.

Data Independent Acquisition: After the Acquisition

Published:

This panel discussed topics related to mass spectrometry(MS)-based proteomics experiments using data independent acquisition (DIA). Specifically, it focused on what to do with the data resulting from these experiments. This included indentification and quantificaiton of mass spectra, as well as post id and quantificaiton processing of the data.

Bayesian statistical modeling reveals missing value mechanisms in label-free Mass Spectrometry-based proteomics experiments

Published:

This talk reviewed bayesian statistical modeling strategies for upstream data processing of MS-based proteomics experiments. This included missing value imputation and summarization of peptide ions to the protein-level. These processing steps were performed with a bayesian model which allowed error propogation at each step. The final summarized values were used for differential analysis in a error-in-variance model.

Single cell mass spectrometry (MS)-based proteomics coupled with suitable experimental design enables the application of causal inference methods to observational experiments.

Published:

This talk explored the integration of new single cell MS-based proteomics methods and causal inference. We argued that single cell MS-based proteomics enables estimating the effect of perturbations from observational studies. These methods were shown on a simulation and real world single cell experiment.

teaching

Algorithms

Teachers Assistant, Northeastern University, 2019

Teacher’s Assistant for master’s level algorithms class.