Visualizing uncertainty in biological expression data

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

Visualizing uncertainty in biological expression data. / Holzhüter, Clemens; Lex, Alexander; Schmalstieg, Dieter; Schulz, Hans-Jörg; Schumann, Heidrun; Streit, Marc.

Proceedings of the Conference on Visualization and Data Analysis VDA 2012. ed. / Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen; Robert Kosara; Mark A. Livingston; Jinah Park; Ian Roberts. Burlingame, CA, USA : SPIE, 2012.

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Holzhüter, C, Lex, A, Schmalstieg, D, Schulz, H-J, Schumann, H & Streit, M 2012, Visualizing uncertainty in biological expression data. in PC Wong, DL Kao, MC Hao, C Chen, R Kosara, MA Livingston, J Park & I Roberts (eds), Proceedings of the Conference on Visualization and Data Analysis VDA 2012. SPIE, Burlingame, CA, USA. https://doi.org/10.1117/12.908516

APA

Holzhüter, C., Lex, A., Schmalstieg, D., Schulz, H-J., Schumann, H., & Streit, M. (2012). Visualizing uncertainty in biological expression data. In P. C. Wong, D. L. Kao, M. C. Hao, C. Chen, R. Kosara, M. A. Livingston, J. Park, ... I. Roberts (Eds.), Proceedings of the Conference on Visualization and Data Analysis VDA 2012 Burlingame, CA, USA: SPIE. https://doi.org/10.1117/12.908516

CBE

Holzhüter C, Lex A, Schmalstieg D, Schulz H-J, Schumann H, Streit M. 2012. Visualizing uncertainty in biological expression data. Wong PC, Kao DL, Hao MC, Chen C, Kosara R, Livingston MA, Park J, Roberts I, editors. In Proceedings of the Conference on Visualization and Data Analysis VDA 2012. Burlingame, CA, USA: SPIE. https://doi.org/10.1117/12.908516

MLA

Holzhüter, Clemens et al. "Visualizing uncertainty in biological expression data"., Wong, Pak Chung, Kao, David L., Hao, Ming C. and Chen, Chaomei Kosara, Robert Livingston, Mark A. Park, Jinah Roberts, Ian (editors). Proceedings of the Conference on Visualization and Data Analysis VDA 2012. Chapter 82940O, Burlingame, CA, USA: SPIE. 2012. https://doi.org/10.1117/12.908516

Vancouver

Holzhüter C, Lex A, Schmalstieg D, Schulz H-J, Schumann H, Streit M. Visualizing uncertainty in biological expression data. In Wong PC, Kao DL, Hao MC, Chen C, Kosara R, Livingston MA, Park J, Roberts I, editors, Proceedings of the Conference on Visualization and Data Analysis VDA 2012. Burlingame, CA, USA: SPIE. 2012 https://doi.org/10.1117/12.908516

Author

Holzhüter, Clemens ; Lex, Alexander ; Schmalstieg, Dieter ; Schulz, Hans-Jörg ; Schumann, Heidrun ; Streit, Marc. / Visualizing uncertainty in biological expression data. Proceedings of the Conference on Visualization and Data Analysis VDA 2012. editor / Pak Chung Wong ; David L. Kao ; Ming C. Hao ; Chaomei Chen ; Robert Kosara ; Mark A. Livingston ; Jinah Park ; Ian Roberts. Burlingame, CA, USA : SPIE, 2012.

Bibtex

@inproceedings{adc409ef661046d7acdfc8307f9656b7,
title = "Visualizing uncertainty in biological expression data",
abstract = "Expression analysis of textasciitildeomics data using microarrays has become a standard procedure in the life sciences. However, microarrays are subject to technical limitations and errors, which render the data gathered likely to be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even shown when the data is visualized using for example clustered heatmaps. Yet, this is highly useful when trying not to omit data that is {"}good enough{"} for an analysis, which otherwise would be discarded as too unreliable by established conservative thresholds. Our approach addresses this shortcoming by first identifying the margin above the error threshold of uncertain, yet possibly still useful data. It then displays this uncertain data in the context of the valid data by enhancing a clustered heatmap. We employ different visual representations for the different kinds of uncertainty involved. Finally, it lets the user interactively adjust the thresholds, giving visual feedback in the heatmap representation, so that an informed choice on which thresholds to use can be made instead of applying the usual rule-of-thumb cut-offs. We exemplify the usefulness of our concept by giving details for a concrete use case from our partners at the Medical University of Graz, thereby demonstrating our implementation of the general approach.",
author = "Clemens Holzh{\"u}ter and Alexander Lex and Dieter Schmalstieg and Hans-J{\"o}rg Schulz and Heidrun Schumann and Marc Streit",
year = "2012",
doi = "10.1117/12.908516",
language = "English",
editor = "Wong, {Pak Chung} and Kao, {David L.} and Hao, {Ming C.} and Chaomei Chen and Robert Kosara and Livingston, {Mark A.} and Jinah Park and Ian Roberts",
booktitle = "Proceedings of the Conference on Visualization and Data Analysis VDA 2012",
publisher = "SPIE",

}

RIS

TY - GEN

T1 - Visualizing uncertainty in biological expression data

AU - Holzhüter, Clemens

AU - Lex, Alexander

AU - Schmalstieg, Dieter

AU - Schulz, Hans-Jörg

AU - Schumann, Heidrun

AU - Streit, Marc

PY - 2012

Y1 - 2012

N2 - Expression analysis of textasciitildeomics data using microarrays has become a standard procedure in the life sciences. However, microarrays are subject to technical limitations and errors, which render the data gathered likely to be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even shown when the data is visualized using for example clustered heatmaps. Yet, this is highly useful when trying not to omit data that is "good enough" for an analysis, which otherwise would be discarded as too unreliable by established conservative thresholds. Our approach addresses this shortcoming by first identifying the margin above the error threshold of uncertain, yet possibly still useful data. It then displays this uncertain data in the context of the valid data by enhancing a clustered heatmap. We employ different visual representations for the different kinds of uncertainty involved. Finally, it lets the user interactively adjust the thresholds, giving visual feedback in the heatmap representation, so that an informed choice on which thresholds to use can be made instead of applying the usual rule-of-thumb cut-offs. We exemplify the usefulness of our concept by giving details for a concrete use case from our partners at the Medical University of Graz, thereby demonstrating our implementation of the general approach.

AB - Expression analysis of textasciitildeomics data using microarrays has become a standard procedure in the life sciences. However, microarrays are subject to technical limitations and errors, which render the data gathered likely to be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even shown when the data is visualized using for example clustered heatmaps. Yet, this is highly useful when trying not to omit data that is "good enough" for an analysis, which otherwise would be discarded as too unreliable by established conservative thresholds. Our approach addresses this shortcoming by first identifying the margin above the error threshold of uncertain, yet possibly still useful data. It then displays this uncertain data in the context of the valid data by enhancing a clustered heatmap. We employ different visual representations for the different kinds of uncertainty involved. Finally, it lets the user interactively adjust the thresholds, giving visual feedback in the heatmap representation, so that an informed choice on which thresholds to use can be made instead of applying the usual rule-of-thumb cut-offs. We exemplify the usefulness of our concept by giving details for a concrete use case from our partners at the Medical University of Graz, thereby demonstrating our implementation of the general approach.

U2 - 10.1117/12.908516

DO - 10.1117/12.908516

M3 - Article in proceedings

BT - Proceedings of the Conference on Visualization and Data Analysis VDA 2012

A2 - Wong, Pak Chung

A2 - Kao, David L.

A2 - Hao, Ming C.

A2 - Chen, Chaomei

A2 - Kosara, Robert

A2 - Livingston, Mark A.

A2 - Park, Jinah

A2 - Roberts, Ian

PB - SPIE

CY - Burlingame, CA, USA

ER -