File:Statistical-and-visual-differentiation-of-subcellular-imaging-1471-2105-10-94-S1.ogv
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Statistical-and-visual-differentiation-of-subcellular-imaging-1471-2105-10-94-S1.ogv (Ogg Theora video file, length 1 min 1 s, 534 × 400 pixels, 245 kbps, file size: 1.77 MB)
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DescriptionStatistical-and-visual-differentiation-of-subcellular-imaging-1471-2105-10-94-S1.ogv |
English: An example of using iCluster. Initially, 50 mitochondria images (mitotracker) and 50 plasma membrane images (EGFR) are shown randomly placed having been loaded into iCluster and statistics calculated. 'Sammon Map Statistics' is then selected and the images move around as a spatial layout is found that reflects the distances between the statistics vectors for the images. The user then rotates the image set, and 3 outlier images are observed, selected (red tint), and then show in more detail in a 2D representation. All three appear to contain artefacts. The view then switches back to the 3D view, a new class 'outlier' is added to the class list, the selected images are reclassified to this class (green borders), and then removed from view by deselecting their class button. Representatives for each class of the remaining images are then shown side by side in a 2D view. The view then changes back to the 3D view, and 'Statistical Test' selected. The images to compare and the number of repeats to calculate a p-value for the null hypothesis (no difference) are then selected. Finally, the returned p-value of 0.000 is displayed, showing that the visual assessment of difference is confirmed statistically. |
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Source | Hamilton N, Wang J, Kerr M, Teasdale R (2009). "Statistical and visual differentiation of subcellular imaging". BMC Bioinformatics. DOI:10.1186/1471-2105-10-94. PMID 19302715. PMC: 2676259. | ||
Author | Hamilton N, Wang J, Kerr M, Teasdale R | ||
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This file is licensed under the Creative Commons Attribution 2.0 Generic license.
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 01:53, 19 November 2012 | 1 min 1 s, 534 × 400 (1.77 MB) | Open Access Media Importer Bot (talk | contribs) | Automatically uploaded media file from Open Access source. Please report problems or suggestions here. |
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Short title | Additional file 1 |
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Author | Hamilton N, Wang J, Kerr M, Teasdale R |
Usage terms | http://creativecommons.org/licenses/by/2.0/ |
Image title | An example of using iCluster. Initially, 50 mitochondria images (mitotracker) and 50 plasma membrane images (EGFR) are shown randomly placed having been loaded into iCluster and statistics calculated. 'Sammon Map Statistics' is then selected and the images move around as a spatial layout is found that reflects the distances between the statistics vectors for the images. The user then rotates the image set, and 3 outlier images are observed, selected (red tint), and then show in more detail in a 2D representation. All three appear to contain artefacts. The view then switches back to the 3D view, a new class 'outlier' is added to the class list, the selected images are reclassified to this class (green borders), and then removed from view by deselecting their class button. Representatives for each class of the remaining images are then shown side by side in a 2D view. The view then changes back to the 3D view, and 'Statistical Test' selected. The images to compare and the number of repeats to calculate a p-value for the null hypothesis (no difference) are then selected. Finally, the returned p-value of 0.000 is displayed, showing that the visual assessment of difference is confirmed statistically. |
Software used | Xiph.Org libtheora 1.1 20090822 (Thusnelda) |
Date and time of digitizing | 2009 |