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Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Sat, 23 Nov 2024 15:25:42 +0000 Subject: [PATCH 2/4] Creating 10.21105.joss.07264.crossref.xml --- joss.07264/10.21105.joss.07264.crossref.xml | 386 ++++++++++++++++++++ 1 file changed, 386 insertions(+) create mode 100644 joss.07264/10.21105.joss.07264.crossref.xml diff --git a/joss.07264/10.21105.joss.07264.crossref.xml b/joss.07264/10.21105.joss.07264.crossref.xml new file mode 100644 index 0000000000..e8a164fd96 --- /dev/null +++ b/joss.07264/10.21105.joss.07264.crossref.xml @@ -0,0 +1,386 @@ + + + + 20241123152458-1c762b3b58da3b2d842c8e223f57ae6efe1dba8a + 20241123152458 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 11 + 2024 + + + 9 + + 103 + + + + PhenoFeatureFinder: a python package for linking +developmental phenotypes to omics features + + + + Lissy-Anne M. + Denkers + + University of Amsterdam, Department of Plant Physiology, Green Life Science Research Theme, Swammerdam Institute for Life Sciences, Amsterdam, The Netherlands + + https://orcid.org/0009-0004-8222-8689 + + + Marc D. + Galland + + INRAE, Institute of Genetics, Environment and Plant Protection (IGEPP—Joint Research Unit 1349), Le Rheu, France + + https://orcid.org/0000-0003-2161-8689 + + + Annabel + Dekker + + Enza Zaden R&D B.V., BTR-BM Bioinformatics, Enkhuizen, The Netherlands + + + + Valerio + Bianchi + + Enza Zaden R&D B.V., BTR-BM Bioinformatics, Enkhuizen, The Netherlands + Wageningen Bioveterinary Research, Wageningen University & Research, Lelystad, Netherlands + + https://orcid.org/0000-0001-9025-7923 + + + Petra M. + Bleeker + + University of Amsterdam, Department of Plant Physiology, Green Life Science Research Theme, Swammerdam Institute for Life Sciences, Amsterdam, The Netherlands + + https://orcid.org/0000-0003-2222-9263 + + + + 11 + 23 + 2024 + + + 7264 + + + 10.21105/joss.07264 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.14179966 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/7264 + + + + 10.21105/joss.07264 + https://joss.theoj.org/papers/10.21105/joss.07264 + + + https://joss.theoj.org/papers/10.21105/joss.07264.pdf + + + + + + Contribution of omics and systems biology to +plant biotechnology + Dalio + 10.1007/978-3-030-80352-0_10 + 2021 + Dalio, R. 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Statistics for analyzing ecotoxicity test data. In J.-F. Férard +& C. Blaise (Eds.), Encyclopedia of Aquatic Ecotoxicology (pp. +1081–1095). Springer Dordrecht. +https://doi.org/10.1007/978-94-007-5704-2 + + + Effect of host plants nutrients, +antioxidants, and phytochemicals on growth, development, and fecundity +of spodoptera litura (fabricius) (lepidoptera: +noctuidae) + Vengateswari + International Journal of Tropical Insect +Science + 42 + 10.1007/s42690-022-00868-6 + 1742-7592 + 0123456789 + 2022 + Vengateswari, G., Arunthirumeni, M., +Shivaswamy, M. S., & Shivakumar, M. S. (2022). Effect of host plants +nutrients, antioxidants, and phytochemicals on growth, development, and +fecundity of spodoptera litura (fabricius) (lepidoptera: noctuidae). +International Journal of Tropical Insect Science, 42, 3161–3173. +https://doi.org/10.1007/s42690-022-00868-6 + + + + + + From 9d34e6fbb4fd7d8a6e918b95f1deb742dc4a5456 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Sat, 23 Nov 2024 15:25:43 +0000 Subject: [PATCH 3/4] Adding JATS media file: package_figure.png --- joss.07264/paper.jats/package_figure.png | Bin 0 -> 725049 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 joss.07264/paper.jats/package_figure.png diff --git a/joss.07264/paper.jats/package_figure.png b/joss.07264/paper.jats/package_figure.png new file mode 100644 index 0000000000000000000000000000000000000000..67a0169f07c499d69b2a8236f8e46ffcbd2413bf GIT binary patch literal 725049 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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +7264 +10.21105/joss.07264 + +PhenoFeatureFinder: a python package for linking +developmental phenotypes to omics features + + + +https://orcid.org/0009-0004-8222-8689 + +Denkers +Lissy-Anne M. + + + + +https://orcid.org/0000-0003-2161-8689 + +Galland +Marc D. + + + + + +Dekker +Annabel + + + + +https://orcid.org/0000-0001-9025-7923 + +Bianchi +Valerio + + + + + +https://orcid.org/0000-0003-2222-9263 + +Bleeker +Petra M. + + + + + +University of Amsterdam, Department of Plant Physiology, +Green Life Science Research Theme, Swammerdam Institute for Life +Sciences, Amsterdam, The Netherlands + + + + +INRAE, Institute of Genetics, Environment and Plant +Protection (IGEPP—Joint Research Unit 1349), Le Rheu, +France + + + + +Enza Zaden R&D B.V., BTR-BM Bioinformatics, Enkhuizen, +The Netherlands + + + + +Wageningen Bioveterinary Research, Wageningen University +& Research, Lelystad, Netherlands + + + + +5 +6 +2024 + +9 +103 +7264 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2024 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +insect development +phenotyping +metabolomics +omics +feature selection +preprocessing + + + + + + Summary +

PhenoFeatureFinder is designed to facilitate + the analyses required to analyse quantitative and/or progressive + phenotypic- and omics data, and link those using Machine Learning with + the aim to identify causal features, in one package. It can be used + for 1) evaluation and visualisation of phenotype progression over + multiple stages and between groups (e.g. treatments, genotypes), 2) + pre-processing of omics data, and 3) prediction of features that + explain the phenotypic classification. To facilitate usability, each + step in the pipeline can also be performed independently, hence has + been assigned a class in the package + ([fig1]). We provide an example + of implementation below that focuses on insect development through + time and the selection of metabolic features causal to the observed + phenotype, but different input data could be used, provided it has a + similar structure. This could be any phenotype that is scored in + progressive stages over time. Also, + PhenoFeatureFinder was developed initially with + metabolomics data, but users can evaluate its fit applying other types + of omics data.

+ +

Overview of the package, consisting of three classes + that can be used separately or as a workflow. Class 1: analysing and + visualising the phenotype, Class 2: preprocessing and visualising + omics datasets, and Class 3: feature selection through a Machine + Learning approach. +

+ +
+
+ + Statement of need +

The analysis of developmental phenotypes can be challenging, due to + the many variables involved (e.g. time, developmental stages, + replicates, treatments), especially for researchers whose strength or + interest does not lie in data analysis. The same goes for the + pre-processing of omics data and linking the omics data and + developmental phenotypes. With + PhenoFeatureFinder, we aim to support such + research by combining the necessary functionalities in one package + with easy to follow manuals and examples.

+

In R, the package drc is available for + fitting dose-response curves + (Ritz + et al., 2015), offering an extensive and versatile set of + functionalities. However, for the purposes described here + drc poses some limitations, such as the options + for custom pre-processing and analyses of multiple experimental groups + simultaneously. Here we implemented pre-processing steps and aimed to + decrease the amount of coding needed to obtain a fitted development + curve.

+
+ + Use case example +

Plants interact with their (a)biotic environment through a range of + specialised metabolites and deal with pathogens and pest attack + through constitutive or inducible production of those defence + molecules + (Erb + & Kliebenstein, 2020; + García-Olmedo + et al., 1998). High-throughput “-omics” tools including + (untargeted) metabolomics have been successfully implemented in plant + biology + (Dalio + et al., 2021), but the accompanying resistance phenotyping + often lacks in robustness + (Song + et al., 2021).

+

Proliferation of an insect population is affected by various + factors, including the chemical composition of the host, and/or the + environment + (Ma et + al., 2022). In particular, host resistance via hampered larval + development is noteworthy, because reducing the speed at which larvae + reach the adult stage and produce offspring negatively affects + pest-population development + (Maharijaya + et al., 2019; + Muema + et al., 2016; + Vengateswari + et al., 2022). However, evaluating larval development results + in a complex dataset that is challenging to process. Developmental + success is based on the number of larvae throughout various larval + stages, as well as on the speed of development.

+

To identify underlying mechanisms of resistance, the chemical or + molecular composition of a plant can be investigated. Proteins and + metabolites are commonly analysed through untargeted + Mass-Spectrometry, yielding exhaustive profiles generally consisting + of many thousands of unannotated features. Often such data displays + sparsity, i.e. missing values between datasets, and a low + sample-to-feature ratio, adding to the complexity of the analysis + (Kortbeek + et al., 2021; + Liebal + et al., 2020). Tree-based Machine-Learning algorithms (e.g., + random forest) are suitable for the analysis of, and feature selection + from, untargeted data + (Liebal + et al., 2020) computing the contribution of each feature in the + phenotypic classification.

+ + Class I: <monospace>PhenotypeAnalysis</monospace> +

A binary classification of plants into “resistant” or + “susceptible” helps to extract relevant features especially when + threshold effects or sparsity (presence/absence) effects are at + play. Here we firstly assess performance over different + developmental stages of larvae on different host plants. The number + of individuals in each stage at a given time is recorded. When + plotted, the cumulative data of these bioassays resemble a growth- + or dose-response curve that can be used to manually assign a binary + phenotype (e.g., resistant/non-resistant), a resistance + classification used as input for + FeatureSelection (Class 3).

+

To account for missing data when individuals that reached the + final developmental stage are removed from the experiment, we + implemented an automated correction step. The count data can be + transformed to cumulative data to analyse the maximum of individuals + that reach each of the developmental stages. Next, the time to reach + a specific stage can be compared between treatments by fitting a + 3-parameter log-logistic curve + (Muse + et al., 2021; + Seefeldt + et al., 1995; + Vliet + & Ritz, 2013) to the cumulative data for each treatment, + with the function:

+

+ + f(x)=m1+exp(s(log(x)log(e50)))

+

where + + x + is time, + + m + is the upper limit (or maximum of individuals that developed to the + stage of interest), + + s + is the slope of the linear part of the curve and + + + e50 + is the EmT50 (the time point at which 50% of the individuals have + developed to the stage of interest). We added the possibility to + compare performance between treatments by fitting a curve with the + function:

+

+ + f(x)=asm(xm)s11+(xm)s

+

Here, + + x + is time, + + a + the area under the curve, + + s + is the shape of the curve and + + m + the median time point. Both functions output a table with the model + parameters, confidence intervals and the model fit, together with a + plot displaying the observed data and the fitted model. For both + functions it is possible to predict the potential maximum beyond the + final experimental measurements.

+
+ + Class II: <monospace>OmicsAnalysis</monospace> +

Untargeted omics results in large datasets that tend to contain + background noise and unreliable features. To clean the data, + multiple filtering methods are implemented in the + OmicsAnalysis class, including the removal of + contaminants present in blank samples, filtering to decrease + sparsity and other quality control steps. The structure of the data + can subsequently be visualised with a PCA and an UpSet plot.

+
+ + Class III: <monospace>FeatureSelection</monospace> +

Combining the output of Classes 1 and 2, i.e. the binary + phenotype classification and the tidied untargeted metabolomics, + FeatureSelection is set up to predict + features that can explain the phenotypic observation under study. + This part of the pipeline was built as a wrapper around the Python + libraries scikit-learn and + TPOT + (Olson + et al., 2016; + Pedregosa + et al., 2011). The FeatureSelection + wrapper is designed to select optimal pipelines for data + preprocessing and identification of the most suitable Machine + Learning model. One characteristic of metabolomics data is strongly + correlated features (linear dependencies between variables) that + make it difficult to extract individual feature importance. + Therefore, this method implements a PCA as dimensionality reduction + method before searching for the best fitting pipeline. Finally, the + importance of the Principal Components and their most related + features (high loadings) can be retrieved to select features with + predicted importance to the phenotypic classification.

+
+
+ + Acknowledgements +

The Amsterdam Data Science Centre is acknowledged for their input + and Frans van der Kloet for his statistical support. Gea van der Lee + and Piet Verdonschot are kindly thanked for providing the data from + (Lee et + al., 2020) used in the example in GitHub.

+
+ + Author contributions +

The software was written by Lissy-Anne Denkers (LD) and Marc + Galland (MG), with input from Petra Bleeker (PB) and tested by Annabel + Dekker (AD) and Valerio Bianchi (VB). The manuals and examples were + written by LD with major input from AD and VB. The manuscript was + designed, written and revised by LD, MG, AD, VB and PB.

+
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