Data Visualization Gallery

In this webpage we list examples of really nice visualizations that our students have created working on assignments and projects during our courses.

For more information about our courses, you can visit our education website: courses.scinet.utoronto.ca

For questions or comments, contact us at courses _at_ scinet.utoronto.ca

Enjoy…

Students’ Contributions

The images below belong to assignments from students in our courses, in some cases they even represent actual research results and full recognition and credit is duly noted to each contributing student.

Computational Biostatistics

Quantitative Applications for Data Analysis

Examples from class

Computational Biostatistics

"Mouse retinas were treated with a neuroprotective drug candidate or vehicle, before a metabolic injury. Retinas were then collected and stained for a specific cellular marker of retinal ganglion cell neurons (RGCs), and the
number of cells per field were quantified. A dose-dependent increase in protection was
observed in drug treated eyes compared to vehicle, with the highest concentration
significantly improving RGC survival. Bar graph represents the mean number of RGCs per
treatment group with standard error bars."
<br>
-- <i>Alessandra Tuccitto</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — "Mouse retinas were treated with a neuroprotective drug candidate or vehicle, before a metabolic injury. Retinas were then collected and stained for a specific cellular marker of retinal ganglion cell neurons (RGCs), and the number of cells per field were quantified. A dose-dependent increase in protection was observed in drug treated eyes compared to vehicle, with the highest concentration significantly improving RGC survival. Bar graph represents the mean number of RGCs per treatment group with standard error bars."
-- *Alessandra Tuccitto*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
"Mouse retinas were treated with a neuroprotective drug candidate or vehicle, before a metabolic injury. Retinas were then collected and stained for a specific cellular marker of retinal ganglion cell neurons (RGCs), and the number of cells per field were quantified. A dose-dependent increase in protection was observed in drug treated eyes compared to vehicle, with the highest concentration significantly improving RGC survival. Bar graph represents the mean number of RGCs per treatment group with standard error bars."
-- *Alessandra Tuccitto*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Deep brain stimulation (DBS) is a neurosurgical procedure used for the treatment of movement disorders, and has recently been used to treat major depression. Depression severity is clinically measured using the Hamilton Depression Rating Scale (HAMD), whereby high values represent increased depression severity. The graphs represent patients’ HAMD scores before and after surgical intervention with DBS, and shows that DBS leads to a reduction of depressive symptoms (decrease in HAMD score).
<br>
-- <i>Irene Harmsen</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Fall 2017 — Deep brain stimulation (DBS) is a neurosurgical procedure used for the treatment of movement disorders, and has recently been used to treat major depression. Depression severity is clinically measured using the Hamilton Depression Rating Scale (HAMD), whereby high values represent increased depression severity. The graphs represent patients’ HAMD scores before and after surgical intervention with DBS, and shows that DBS leads to a reduction of depressive symptoms (decrease in HAMD score).
-- *Irene Harmsen*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
Deep brain stimulation (DBS) is a neurosurgical procedure used for the treatment of movement disorders, and has recently been used to treat major depression. Depression severity is clinically measured using the Hamilton Depression Rating Scale (HAMD), whereby high values represent increased depression severity. The graphs represent patients’ HAMD scores before and after surgical intervention with DBS, and shows that DBS leads to a reduction of depressive symptoms (decrease in HAMD score).
-- *Irene Harmsen*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

-- *Maria Vladoiu*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
Figure generated by
-- *Maria Vladoiu*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

Relative abundance plot of bacterial phyla in healthy and psoriatic skin. A common way to present microbiome data, such plots provide a visual representation of differences in taxa abundance between samples/groups that may have a biological relevance to the study.
<br>
-- <i>Meital Yerushalmi</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Fall 2017 — Relative abundance plot of bacterial phyla in healthy and psoriatic skin. A common way to present microbiome data, such plots provide a visual representation of differences in taxa abundance between samples/groups that may have a biological relevance to the study.
-- *Meital Yerushalmi*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
Relative abundance plot of bacterial phyla in healthy and psoriatic skin. A common way to present microbiome data, such plots provide a visual representation of differences in taxa abundance between samples/groups that may have a biological relevance to the study.
-- *Meital Yerushalmi*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

Electrode Matrix description: Interaction matrix between EEG electrodes upon magnetic-stimulation induced activation in the motor cortex (C3 electrode).
<br>
-- <i>Jeanette Hui</i>, MSc (year 2)
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Electrode Matrix description: Interaction matrix between EEG electrodes upon magnetic-stimulation induced activation in the motor cortex (C3 electrode).
-- *Jeanette Hui*, MSc (year 2)

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Electrode Matrix description: Interaction matrix between EEG electrodes upon magnetic-stimulation induced activation in the motor cortex (C3 electrode).
-- *Jeanette Hui*, MSc (year 2)

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

In the absence of corneal sensory innervation patients develop neurotrophic keratopathy, a disease characterized by corneal ulcer formation that can ultimately lead to vision loss. Corneal neurotization is a novel surgical procedure that restores innervation and sensation in patients with neurotrophic keratopathy through the use of nerve grafts. At the Borschel Laboratory, we have developed an animal model of neurotrophic keratopathy and of corneal neurotization. After performing the surgeries in our models, we stitch the eyelids together (tarsorrhaphy), to protect the cornea from developing epithelial injuries. The included figure represents the area of the ulcer (injury) formed in the neurotized (blue) and diseased (orange) corneas after tarsorrhaphy removal.
<br>
-- <i>Kira Antonyshyn</i>, MSc Candidate (’19)
<br>
Borschel Laboratory, Hospital for Sick Children:
<code>http://lab.research.sickkids.ca/borschel/</code>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Fall 2017 — In the absence of corneal sensory innervation patients develop neurotrophic keratopathy, a disease characterized by corneal ulcer formation that can ultimately lead to vision loss. Corneal neurotization is a novel surgical procedure that restores innervation and sensation in patients with neurotrophic keratopathy through the use of nerve grafts. At the Borschel Laboratory, we have developed an animal model of neurotrophic keratopathy and of corneal neurotization. After performing the surgeries in our models, we stitch the eyelids together (tarsorrhaphy), to protect the cornea from developing epithelial injuries. The included figure represents the area of the ulcer (injury) formed in the neurotized (blue) and diseased (orange) corneas after tarsorrhaphy removal.
-- *Kira Antonyshyn*, MSc Candidate (’19)
Borschel Laboratory, Hospital for Sick Children: `http://lab.research.sickkids.ca/borschel/`

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
In the absence of corneal sensory innervation patients develop neurotrophic keratopathy, a disease characterized by corneal ulcer formation that can ultimately lead to vision loss. Corneal neurotization is a novel surgical procedure that restores innervation and sensation in patients with neurotrophic keratopathy through the use of nerve grafts. At the Borschel Laboratory, we have developed an animal model of neurotrophic keratopathy and of corneal neurotization. After performing the surgeries in our models, we stitch the eyelids together (tarsorrhaphy), to protect the cornea from developing epithelial injuries. The included figure represents the area of the ulcer (injury) formed in the neurotized (blue) and diseased (orange) corneas after tarsorrhaphy removal.
-- *Kira Antonyshyn*, MSc Candidate (’19)
Borschel Laboratory, Hospital for Sick Children: `http://lab.research.sickkids.ca/borschel/`

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

MACIS, Thyroid differentiation score, BRS score, and Mutational Density of TCGA Papillary Thyroid Carcinomas by sex.
<br>
Data taken from https://www-sciencedirect-com.myaccess.library.utoronto.ca/science/article/pii/S0092867414012380
<br>
-- <i>Ana Stosic</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — MACIS, Thyroid differentiation score, BRS score, and Mutational Density of TCGA Papillary Thyroid Carcinomas by sex.
Data taken from https://www-sciencedirect-com.myaccess.library.utoronto.ca/science/article/pii/S0092867414012380
-- *Ana Stosic*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
MACIS, Thyroid differentiation score, BRS score, and Mutational Density of TCGA Papillary Thyroid Carcinomas by sex.
Data taken from https://www-sciencedirect-com.myaccess.library.utoronto.ca/science/article/pii/S0092867414012380
-- *Ana Stosic*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Heatmap representation of the "Orange" data included within the R datasets.
<br>
-- <i>Michael Tang</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Heatmap representation of the "Orange" data included within the R datasets.
-- *Michael Tang*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Heatmap representation of the "Orange" data included within the R datasets.
-- *Michael Tang*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Example of Hheatmap plot using R's "swiss" dataset.
<br>
-- <i>Alessandra Tuccitto</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Example of Hheatmap plot using R's "swiss" dataset.
-- *Alessandra Tuccitto*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Example of Hheatmap plot using R's "swiss" dataset.
-- *Alessandra Tuccitto*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Height vs weight data and fit.
<br>
-- <i>Kevin Wang</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Height vs weight data and fit.
-- *Kevin Wang*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Height vs weight data and fit.
-- *Kevin Wang*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Produced by a student in the course "Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Produced by a student in the course "Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Student survey heatmap.
<br>
-- <i>Kevin Wang</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Student survey heatmap.
-- *Kevin Wang*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Student survey heatmap.
-- *Kevin Wang*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

3D scatter plot of player score vs opponent score throughout 50 rounds of Modified Taylor Aggression Task. Part of the AHIMSA-1 study
investigating the effect of Aprepitant on aggression.
<br>
-- <i>Jack Sheen</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — 3D scatter plot of player score vs opponent score throughout 50 rounds of Modified Taylor Aggression Task. Part of the AHIMSA-1 study investigating the effect of Aprepitant on aggression.
-- *Jack Sheen*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
3D scatter plot of player score vs opponent score throughout 50 rounds of Modified Taylor Aggression Task. Part of the AHIMSA-1 study investigating the effect of Aprepitant on aggression.
-- *Jack Sheen*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Three-dimensional representation of the length of arm and age in the plane of the linear model.
This visualization is needed to verify that this data is applicable to all patients and that the data is not effected by patients with dwarfism or other malformation.
<br>
-- <I>William Chu Kwan</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Three-dimensional representation of the length of arm and age in the plane of the linear model. This visualization is needed to verify that this data is applicable to all patients and that the data is not effected by patients with dwarfism or other malformation.
-- *William Chu Kwan*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Three-dimensional representation of the length of arm and age in the plane of the linear model. This visualization is needed to verify that this data is applicable to all patients and that the data is not effected by patients with dwarfism or other malformation.
-- *William Chu Kwan*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Baseline assessment scores of depressive symptom severity (MADRS) and anhedonia (SHAPS) subgrouped by levels of an inflammatory biomarker (CRP).
<br>
-- <i>Yena Lee</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Baseline assessment scores of depressive symptom severity (MADRS) and anhedonia (SHAPS) subgrouped by levels of an inflammatory biomarker (CRP).
-- *Yena Lee*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Baseline assessment scores of depressive symptom severity (MADRS) and anhedonia (SHAPS) subgrouped by levels of an inflammatory biomarker (CRP).
-- *Yena Lee*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Box plot with whiskers representing the relationship between the points deducted by the player when faced with deductions from another
player during the Modified Taylor Aggression Task. Aggressive patients deduct more points as a "punishment" when they experience high level deductions from the opponent.
<br>
-- <i>Jack Sheen</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Box plot with whiskers representing the relationship between the points deducted by the player when faced with deductions from another player during the Modified Taylor Aggression Task. Aggressive patients deduct more points as a "punishment" when they experience high level deductions from the opponent.
-- *Jack Sheen*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Box plot with whiskers representing the relationship between the points deducted by the player when faced with deductions from another player during the Modified Taylor Aggression Task. Aggressive patients deduct more points as a "punishment" when they experience high level deductions from the opponent.
-- *Jack Sheen*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

<br>
-- <i>Nathan Soucier</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — -- *Nathan Soucier*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

-- *Nathan Soucier*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

95% CI and Linear Regression for neurovascular structure obtained from MRI's data.
<br>
-- <I>William Chu Kwan</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — 95% CI and Linear Regression for neurovascular structure obtained from MRI's data.
-- *William Chu Kwan*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
95% CI and Linear Regression for neurovascular structure obtained from MRI's data.
-- *William Chu Kwan*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Box plots illustrating the projected levels of cortical activation in the left motor cortex (red) vs. the right motor cortex (blue) across three drug conditions for twelve subjects.
<br>
-- <i>Jeanette Hui</i>, MSc (year 2)
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Box plots illustrating the projected levels of cortical activation in the left motor cortex (red) vs. the right motor cortex (blue) across three drug conditions for twelve subjects.
-- *Jeanette Hui*, MSc (year 2)

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Box plots illustrating the projected levels of cortical activation in the left motor cortex (red) vs. the right motor cortex (blue) across three drug conditions for twelve subjects.
-- *Jeanette Hui*, MSc (year 2)

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Heat map and dendrogram of hierarchical clustering analysis using Ward method. Individual subjects are divided in 3 clusters based on the
similar values for each test variable (coagulation tests). Same methods were applied to coagulation test variables, showing coagulation tests
grouped in 2 clusters based on individual subjects results (study ID).
<br>
-- <i>Bruna Camilotti</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Fall 2017 — Heat map and dendrogram of hierarchical clustering analysis using Ward method. Individual subjects are divided in 3 clusters based on the similar values for each test variable (coagulation tests). Same methods were applied to coagulation test variables, showing coagulation tests grouped in 2 clusters based on individual subjects results (study ID).
-- *Bruna Camilotti*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
Heat map and dendrogram of hierarchical clustering analysis using Ward method. Individual subjects are divided in 3 clusters based on the similar values for each test variable (coagulation tests). Same methods were applied to coagulation test variables, showing coagulation tests grouped in 2 clusters based on individual subjects results (study ID).
-- *Bruna Camilotti*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

Heatmap made using <code>ggplot2</code>, with tips from https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/
<br>
-- <i>Joan Miguel Romero</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Fall 2017 — Heatmap made using `ggplot2`, with tips from https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/
-- *Joan Miguel Romero*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
Heatmap made using `ggplot2`, with tips from https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/
-- *Joan Miguel Romero*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

Heatmap of healthy and psoriatic skin microbiome. Each column corresponds to a specific sample, and each row to the bacterial phyla identified in the sequencing data. The relative phyla abundance is indicated by the colour scale (red: low; white: high). Heatmaps are used to visualize trends in high-throughput data, such as gene expression or microbiome, where clusters of high-abundance species can be easily detected in specific sample group (e.g. disease vs. healthy).
<br>
-- <i>Meital Yerushalmi</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Fall 2017 — Heatmap of healthy and psoriatic skin microbiome. Each column corresponds to a specific sample, and each row to the bacterial phyla identified in the sequencing data. The relative phyla abundance is indicated by the colour scale (red: low; white: high). Heatmaps are used to visualize trends in high-throughput data, such as gene expression or microbiome, where clusters of high-abundance species can be easily detected in specific sample group (e.g. disease vs. healthy).
-- *Meital Yerushalmi*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
Heatmap of healthy and psoriatic skin microbiome. Each column corresponds to a specific sample, and each row to the bacterial phyla identified in the sequencing data. The relative phyla abundance is indicated by the colour scale (red: low; white: high). Heatmaps are used to visualize trends in high-throughput data, such as gene expression or microbiome, where clusters of high-abundance species can be easily detected in specific sample group (e.g. disease vs. healthy).
-- *Meital Yerushalmi*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

“Effect of Maternal Smoking on Infant Birth Weight”, using data from the “Child Health and Development Studies” from http://vincentarelbundock.github.io/Rdatasets/datasets.html
<br>
-- <i>Julia Tomasi</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Fall 2017 — “Effect of Maternal Smoking on Infant Birth Weight”, using data from the “Child Health and Development Studies” from http://vincentarelbundock.github.io/Rdatasets/datasets.html
-- *Julia Tomasi*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017
“Effect of Maternal Smoking on Infant Birth Weight”, using data from the “Child Health and Development Studies” from http://vincentarelbundock.github.io/Rdatasets/datasets.html
-- *Julia Tomasi*

"Intro to Computational BioStats with R" (MSC1090), Fall 2017

Graphical representation of decaying exponential model and data.
<br>
-- <i>Nathan Soucier</i>
<br><br>
"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18 — Graphical representation of decaying exponential model and data.
-- *Nathan Soucier*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18
Graphical representation of decaying exponential model and data.
-- *Nathan Soucier*

"Intro to Computational BioStats with R" (MSC1090), Winter 2017/18

Quantitative Applications for Data Analysis

From the “Quantitative Applications for Data Analysis” (EES1137) course
Visualizations of the merger of a Binary Neutron Star (BNS) system

Animation produced by Syed Ghazali Ahmad (winter 2018).	Animation produced by another student in the course (winter 2018).

Movies generated by our students, taking as input simulated data in netCDF format of a BNS merger generating a three panel visualization, including a 3D representation of the trajectory followed by the stars displayed in the upper panel.
The movies were produced using python scripts and the techniques presented in class.

Examples from class

Examples from class…

From the 2018 “Introduction to Computational Biostatiscs” (MSC1090) course
Slicing of MRI data…	Random dots…

Visualization of successive slices of MRI data in the three (x,y,z) coordinates.	As humble as this movie might look like, it contains the basic principles of movie generation. It also represents the dynamics of a random diffusion process, such as colloidal particles in fluids, or a droplet of milk in a mug of coffee, or even some drunk friends leaving the SGU pub at St. George campus…

From the 2017/2018 “Quantitative Applications for Data Analysis” (EES1137) course
Representation of the trajectories for a binary neutron star merger, where the vertical axis displays the time to merger…

Zombie’s apocalypses survival curves…

SciNet | Advanced Research Computing at the University of Toronto