04_Data_presentation.pot

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#: ../../04_Data_presentation/Introduction.md:1
#: ../../04_Data_presentation/Statistics.md:3
#: ../../04_Data_presentation/_notinyet_Presentation_graphs.md:3
msgid "Introduction"
msgstr ""

#: ../../04_Data_presentation/Introduction.md:3
msgid "When presenting microscopy image data in biology and biomedicine, it's important to consider the quality of the images, the labeling and annotation of important features, and the overall visual appeal. This also applies to diagrams and plots which convey numerical data derived from microscopy images. Chart types must in addition be suitable for the specific data that is being conveyed to not mislead audiences. This applies to image data in scientific posters, talk-slides, or publications."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:1
msgid "Presentation of microscopy images"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:3
#: ../../04_Data_presentation/Statistics.md:9
#: ../../04_Data_presentation/Statistics.md:34
#: ../../04_Data_presentation/Statistics.md:57
msgid "What is it?"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:5
msgid "Microscopy images are often shown in scientific papers to illustrate a particular conclusion. While qualitative conclusions are not a substitute for quantitative comparisons (see next section), images can certainly guide our reasoning and our conclusions. Following a few consistent best practices ensures that these conclusions are correct and robust."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:14
msgid "10 tips for image presentation"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:14
msgid "**A brief visual summary of image presentation tips.** Figure by Helena Jambor. [Source](https://doi.org/10.5281/zenodo.7750259)"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
msgid "Adjust the image crop, orientation, and size."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:18
msgid "For any adjustments, work with an image copy and do not alter the original file. Note, do not use adjusted images for quantitative image data analyses  Adjustments to effectively communicate the image content may include removing uninformative image regions (crop), changing the image orientation, and adjusting the size. Note that rotation and re-sizing may change the image data when pixel information is redistributed."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:17
msgid "rotation"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
msgid "🤔 How do I do it?"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:25
#: ../../04_Data_presentation/Presentation_images.md:43
#: ../../04_Data_presentation/Presentation_images.md:62
#: ../../04_Data_presentation/Presentation_images.md:82
#: ../../04_Data_presentation/Presentation_images.md:98
msgid "See the [cheat-sheet below](image-cheat-sheet) for more information."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
#: ../../04_Data_presentation/Statistics.md:0
msgid "<span style=\"color: red\">⚠️</span> Where can things go wrong?"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:28
msgid "Any adjustments that alter the conclusions are not permitted."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
#: ../../04_Data_presentation/Statistics.md:0
msgid "📚🤷‍♀️ Where can I learn more?"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:31
msgid "📄 [Reproducible image handling and analysis](https://doi.org/10.15252/embj.2020105889) {cite}`Miura2021-mb`"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:32
#: ../../04_Data_presentation/Presentation_images.md:49
msgid "📄 [Avoiding Twisted Pixels: Ethical Guidelines for the Appropriate Use and Manipulation of Scientific Digital Images](https://doi.org/10.1007%2Fs11948-010-9201-y) {cite}`Cromey2010-jr`"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
msgid "Enhance visibility of image content"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:37
msgid "Images often do not have regular spaced intensity values. To still display the data visible on a screen/in a figure, adjustments of brightness and contrast are usually necessary."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:36
msgid "image adjustment"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:46
msgid "Any adjustments that result in the disappearance of image details are considered misleading {cite}`Cromey2010-jr`. Note that many nonlinear transformations of brightness and contrast are available in image processing software, before using these users should ensure that they faithfully represent the data to avoid accidentally misleading audiences and disclose them as annotations."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
msgid "Use accessible colors"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:54
msgid "Fluorescent microscope images are often composed of data from multiple wavelengths/color channels. To best visualize molecular structures, individual channels can be shown in separate grayscale images. When colors are chosen to represent the illumination wavelength (blue, green, red, far-red), for example Green-Fluorescent Protein is shown in green color, be reminded that intensity values on a black background reduces the level of detail."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:56
msgid "When channels are over-laid in ‘composite’ images, authors should ensure that structures are visible, i.e., that the overlay does not obstruct features and that the colors used are clearly distinguishable."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:53
msgid "multicolor image composition"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:65
msgid "For composite images consider if color combinations are accessible to color-blind audiences (e.g. not combine red with green, but rather magenta and green, see reference below for examples) and possibly additionally show individual channels in grayscale for maximizing accessibility and detail.  Tools for color blindness simulation of the images exist in image processing software (ImageJ/Fiji) and visibility of colors in final image figures can be tested with applications such as ColorOracle."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:69
msgid "📄 [Creating clear and informative image-based figures for scientific publications](https://doi.org/10.1371/journal.pbio.3001161) {cite}`Jambor2021-qe`"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
msgid "Annotate key image features"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:74
msgid "Each image needs a reference to its physical dimensions. This is typically achieved by including a scale bar with dimensions annotated in the image or the figure legend."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:76
msgid "In addition, authors should remember to annotate the colors used, any symbols and arrows used to guide readers, and, if used, the origin of any zoom/inset. If specialized images are shown (time-lapse, volumes, reconstructions) authors are encouraged to consider annotating important information in the figures."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:73
msgid "speech bubbles"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:85
msgid "Lack of details and missing of key explanations will make it impossible for audiences to interpret image data in figures.  To unambiguously reference probes consider using terms from the ISAC Probe Tag Dictionary, a standardized nomenclature for probers used in cytometry and microscopy."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:89
msgid "📄 [ISAC Probe Tag Dictionary: Standardized Nomenclature for Detection and Visualization Labels Used in Cytometry and Microscopy Imaging ](https://doi.org/10.1002/cyto.a.24224) {cite}`Blenman2021-ki`"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:0
msgid "Explain the image"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:95
msgid "To rapidly orient audiences, a minimal explanatory text should be presented along with images. This includes the figure legend and the methods section in scientific papers or the title of figures in posters and slides. Consider using a controlled vocabulary to reduce ambiguity and increase machine-readability of the descriptions of specimens, tissues, cell lines, and proteins etc. A useful tool is the [RRID (Research Resource Identifying Data)index](https://scicrunch.org/resources) , which provides indices for commonly used biological reagents and resources, e.g., plasmids, cell lines and antibodies ."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:101
msgid "Missing explanations of image details/methods may result in non-reproducible data and limits the insights from the data."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:104
msgid "📄 [Replication Study: Biomechanical remodeling of the microenvironment by stromal caveolin-1 favors tumor invasion and metastasis](https://doi.org/10.7554/eLife.45120) {cite}`Sheen2019-bg`"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:105
msgid "📄 [Imaging methods are vastly underreported in biomedical research](https://doi.org/10.7554/eLife.55133) {cite}`Marques2020-nx`"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:106
msgid "📄 [Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension.](https://doi.org/10.1186/1747-5333-4-1) {cite}`Yu2009-ip`"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:110
msgid "Where can I learn more?"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:112
msgid "Check out _Creating clear and informative image-based figures for scientific publications_{cite}`Jambor2021-qe` and _Community-developed checklists for publishing images and image analysis_{cite}`Schmied2024-et` for more tips and best practices for making image figures."
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:114
msgid "A cheat sheet on how to do basic image preparation with open source software:"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:124
msgid "Instructions for common image processing operations in Fiji"
msgstr ""

#: ../../04_Data_presentation/Presentation_images.md:124
msgid "**How to correctly perform various image manipulations in Fiji.** Figure by Christopher Schmied and Helena Jambor. [Source](https://doi.org/10.12688/f1000research.27140.2)"
msgstr ""

#: ../../04_Data_presentation/Resources.md:1
msgid "Resources for learning more"
msgstr ""

#: ../../04_Data_presentation/Resources.md:8
msgid "**Resource Name**"
msgstr ""

#: ../../04_Data_presentation/Resources.md:9
msgid "**Link**"
msgstr ""

#: ../../04_Data_presentation/Resources.md:10
msgid "**Brief description**"
msgstr ""

#: ../../04_Data_presentation/Resources.md:11
msgid "📄 Creating clear and informative image-based figures for scientific publications {cite}`Jambor2021-qe`"
msgstr ""

#: ../../04_Data_presentation/Resources.md:12
msgid "[link](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001161 )"
msgstr ""

#: ../../04_Data_presentation/Resources.md:13
msgid "Review article on how to create accessible, fair scientific figures, including guidelines for microscopy images"
msgstr ""

#: ../../04_Data_presentation/Resources.md:14
msgid "📄 Community-developed checklists for publishing images and image analysis {cite}`Schmied2024-et`"
msgstr ""

#: ../../04_Data_presentation/Resources.md:15
msgid "[link](https://doi.org/10.1038/s41592-023-01987-9)"
msgstr ""

#: ../../04_Data_presentation/Resources.md:16
msgid "A paper recommending checklists and best practices for publishing image data. [It also has a JupyterBook](https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html)"
msgstr ""

#: ../../04_Data_presentation/Resources.md:17
msgid "📖 Modern Statistics for Modern Biology {cite}`Holmes2019-no`"
msgstr ""

#: ../../04_Data_presentation/Resources.md:18
msgid "[link](https://www.huber.embl.de/msmb/)"
msgstr ""

#: ../../04_Data_presentation/Resources.md:19
msgid "Online statistics for biologists textbook with code examples (in R)"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:1
msgid "Statistics"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:5
msgid "Quantitative data is often summarized and analyzed with statistical methods and visualized with plots/graphs/diagrams. Statistical methods reveal quantitative trends, patterns, and outliers in data, while plots and graphs help to convey them to audiences. Carrying out a suitable statistical analysis and choosing a suitable chart type for your data, identifying their potential pitfalls, and faithfully realizing the analysis or generating the chart with suitable software are essential to back up experimental conclusions with data and reach communication goals."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:7
msgid "Dimensionality reduction"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:10
msgid "Dimensionality reduction (also called dimension reduction) aims at mapping high-dimensional data onto a lower-dimensional space in order to better reveal trends and patterns. Algorithms performing this task attempt to retain as much information as possible when reducing the dimensionality of the data: this is achieved by assigning importance scores to individual features, removing redundancies, and identifying uninformative (for instance constant) features. Dimensionality reduction is an important step in quantitative analysis as it makes data more manageable and easier to visualize. It is also an important preprocessing step in many downstream analysis algorithms, such as machine learning classifiers."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:0
msgid "📏 How do I do it?"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:13
msgid "The most traditional dimensionality reduction technique is principal component analysis (PCA){cite}`Lever2017-pca`. In a nutshell, PCA recovers a linear transformation of the input data into a new coordinate system (the principal components) that concentrates variation into its first axes. This is achieved by relying on classical linear algebra, by computing an eigendecomposition of the covariance matrix of the data. As a result, the first 2 or 3 principal components provide a low-dimensional version of the data distribution that is faithful to the variance that was originally present. More advanced dimensionality reduction methods that are popular in biology include t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). In contrast to PCA, these methods are non-linear and can therefore exploit more complex relationships between features when building the lower-dimensional representation. This however comes at a cost: both t-SNE and UMAP are stochastic, meaning that the results they produce are highly dependent on the choice of hyperparameters and can differ across different runs."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:18
msgid "Although reducing dimensionality can be very useful for data exploration and analysis, it may also wipe information or structure that is relevant to the problem being studied. This is famously well illustrated by the [Datasaurus dataset](https://cran.r-project.org/web/packages/datasauRus/vignettes/Datasaurus.html), which demonstrates how very differently-looking sets of measurements can become indistinguishable when described by a small set of summary statistics. The best way to minimize this risk is to start by visually exploring the data whenever possible, and carefully checking any underlying assumptions of the dimensionality reduction method being used to ensure that they hold for the considered data. Dimensionality reduction may also enhance and reveal patterns that are not biologically relevant, due to noise or systematic artifacts in the original data (see Batch effect correction section below). In addition to applying normalization and batch correction to the data prior to reducing dimensionality, some dimensionality reduction methods also offer so-called regularization strategies to mitigate this. In the end, any pattern identified in dimension-reduced data should be considered while keeping in mind the biological context of the data in order to interpret the results appropriately."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:26
msgid "📖 [Dimension Reduction: A Guided Tour](https://www.researchgate.net/publication/220416606_Dimension_Reduction_A_Guided_Tour) {cite}`Burges2010-fi`"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:27
msgid "💻 [UMAP introduction and Python implementation](https://umap-learn.readthedocs.io/en/latest/index.html)"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:28
msgid "💻 [t-SNE Python implementation](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:32
msgid "Batch correction"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:35
msgid "Batch effects are systematic variations across samples correlated with experimental conditions (such as different times of the day, different days of the week, or different experimental tools) that are not related to the biological process of interest. Batch effects must be mitigated prior to making comparisons across several datasets as they impact the reproducibility and reliability of computational analysis and can dramatically bias conclusions. Algorithms for batch effect correction address this by identifying and quantifying sources of technical variation, and adjusting the data so that these are minimized while the biological signal is preserved. Most batch effect correction methods were originally developed for microarray data and sequencing data, but can be adapted to feature vectors extracted from images."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:38
msgid "Two of the most used methods for batch effect correction are ComBat and Surrogate Variable Analysis (SVA), depending on whether the sources of batch effects are known a priori or not. In a nutshell, ComBat involves three steps: 1) dividing the data into known batches, 2) estimating batch effect by fitting a linear model that includes the batch as a covariate and 3) adjusting the data by removing the estimated effect of the batch from each data point. In contrast, SVA aims at identifying \"surrogate variables\" that capture unknown sources of variability in the data. The surrogate variables can be estimated relying on linear algebra methods (such as singular value decomposition) or through a Bayesian factor analysis model. SVA has been demonstrated to reduce unobserved sources of variability and is therefore of particular help when identifying possible causes of batch effects is challenging, but comes at a higher computational cost than ComBat."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:41
msgid "As important as it is for analysis, batch effect correction can go wrong when too much or too little of it is done. Both over- and under-correction can happen when methods are not used properly or when their underlying assumptions are not met. As a result, either biological signals can be removed (in the case of over-correction) or irrelevant sources of variation can remain (in the case of under-correction) - both potentially leading to inaccurate conclusions. Batch effect correction can be particularly tricky when the biological variation of interest is suspected to confound with the batch. In this case in particular (although always a good approach), the first lines of fight against batch effects should be thought-through experimental design and careful quality control, as well as visual exploration of the data{cite}`Lord2020-sp`. Plotting data batch-by-batch before applying any correction can help confirm (or infirm) that the observed trends are similar across batches."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:49
msgid "📄 [Why Batch Effects Matter in Omics Data, and How to Avoid Them](https://doi.org/10.1016/j.tibtech.2017.02.012) {cite}`Goh2017-kd`"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:50
msgid "💻 [pyComBat (ComBat Python implementation)](https://epigenelabs.github.io/pyComBat/)"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:51
msgid "📄 [The sva package for removing batch effects and other unwanted variation in high-throughput experiments](https://doi.org/10.1093/bioinformatics/bts034) {cite}`Leek2012-rv`"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:55
msgid "Normality testing"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:58
msgid "Normality testing is about assessing whether data follow a Gaussian (or normal) distribution. Because the Gaussian distribution is frequently found in nature and has important mathematical properties, normality is a core assumption in many widely-used statistical tests. When this assumption is violated, their conclusions may not hold or be flawed. Normality testing is therefore an important step of the data analysis pipeline prior to any sort of statistical testing."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:61
msgid "Normality of a data distribution can be qualitatively assessed through plotting, for instance relying on a histogram. For a more quantitative readout, statistical methods such as the Kolmogorov-Smirnov (KS) and Shapiro-Wilk tests (among many others) report how much the observed data distribution deviates from a Gaussian. These tests usually return and a p-value linked to the hypothesis that the data are sampled from a Gaussian distribution. A high p-value indicates that the data are not inconsistent with a normal distribution, but is not sufficient to prove that they indeed follow a Gaussian. A p-values smaller than a pre-defined significance threshold (usually 0.05) indicates that the data are not sampled from a normal distribution."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:65
msgid "Although lots of the “standard” statistical methods have been designed with a normality assumption, alternative approaches exist for non-normally-distributed data. Many biological processes result in multimodal “states” (for instance differentiation) that are inherently not Gaussian. Normality testing should therefore not be mistaken for a quality assessment of the data: it merely informs on the types of tools that are appropriate to use when analyzing them."
msgstr ""

#: ../../04_Data_presentation/Statistics.md:68
msgid "📖 [Modern statistics for modern biology](https://www.huber.embl.de/msmb/) {cite}`Holmes2019-no`"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:69
msgid "💻 [To get started with statistical analysis: R](https://www.r-project.org/)"
msgstr ""

#: ../../04_Data_presentation/Statistics.md:70
msgid "💻 [To do statistics in Python: scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)"
msgstr ""

#: ../../04_Data_presentation/_notinyet_Presentation_graphs.md:1
msgid "Presentation of graphs"
msgstr ""