A Guided Tour Through
Melanoma Explorer
Dario Strbenac
School of Mathematics and Stastitics
PD1 ExpressionPDL1 Expression
Ligand and Receptor Expression
From Table
mRNA Expression mRNA Expression
Select
PD1PD1
From Table
mRNA Expression mRNA Expression
Select
PDL1PDL1
Lower Limit
00
Upper Limit
1515
Lower Limit
00
Upper Limit
1515
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Slope: 2.11 p-value: 0.00043
Update Plot Update Plot
The University of Sydney Page 2
Melanoma Explorer
– A web application to reanalyse existing datasets and reproduce findings from their associated journals.
– Implemented in R and Shiny.
– Can create arbitrary groups of samples and plot numeric
measurements, compare patient survival, and perform cross-
validated classification. Also, contains a data visualiser for VAN.
The University of Sydney Page 3
– Stage 3 Sydney melanoma samples analysed on gene expression microarrays.
– LYZ found to be most differentially expressed gene between good (Alive, No Relapse > 4 years) and poor survival (Died of
melanoma, < 1 year) groups.
Goal: Verify LYZ is associated with survival in the study, using all
Case 1: Survival Analysis and its
Stability
The University of Sydney Page 4
1. Measurement Viewer is the default grouping method on Dataset Definer tab of the
Datasets and Grouping section.
Go directly to the
Measurement Viewer tool.
click
2. In the variable selector
region type LYZ. There are two probes for the gene. Select the first one.
3. Click the Update Plot button.
Case 1: Survival Analysis and its
Stability
Case 1: Survival Analysis and its Stability
4. Two groups are created, Low and High, based on the lowest 40% and highest 40% of
expression values (i.e. 20% of samples are ignored). The plot is interactive.
5. Click on Survival Plots in the navigation bar and then on the Sruvival Profile tab.
click
click
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Case 1: Survival Analysis and its Stability
6. LYZ expression is clearly associated with patient survival. The events can be hovered on for more information.
Case 1: Survival Analysis and its Stability
7. Click on the p-value Contour Plot tab. Click the Generate Plot button.
click click
Conclusion: LYZ is associated with survival in the study, and the
partitioning of samples by its measurements is robust.
The University of Sydney Page 8
Case 2: Relationships Between Numeric Variables
Researchers found a relationship between tumour thickness and miR- 382 expression.
1. Click on Datasets and Grouping in the navigation bar, then scroll to the New York University miRNA Set 1 dataset and click on the journal link.
2. Observe the scatterplot in Figure 2A.
click
Case 2: Relationships Between Numeric Variables
3. Click on the Dataset Definer tab. Click on New York University Set 1 (2015) in the Clinical Dataset list and on New York University miRNA Set 1 from the
Experiment Datasets list. Disable grouping of samples by choosing None for the Group Samples Using setting.
4. Select the clinical data table and the thickness variable for the x-axis options. Also check the Transform log2 option. Select the miRNA table and type 382 for the y-axis options.
Select the only option shown.
click
The University of Sydney Page 10
Case 2: Relationships Between Numeric Variables
5. Click on the Update Plot button. Note that the regression line and location of
measurements of Melanoma Explorer’s scatterplot is identical to the researchers’ scatterplot in the journal.
JNCI Melanoma Explorer
Case 3: Explore Network Variability Results
– A tool to generate a list of dysregulated sub-networks, based on existing network databases and a numeric experimental dataset.
– Used on stage 3 Sydney melanoma dataset. Reanalysing published results requires using R or Cytoscape.
– melanomaExporer removes the need for any programming
knowledge to explore the results.
The University of Sydney Page 12
Case 3: Explore Network Variability Results
1. Click on Network Variability Analysis in the navigation bar, then on the Table Explorer tab.
click
click
2a. Network Dysregulation is the default view. Click on the
Adjusted.P.value column heading. The entries are sorted in ascending order.
click
Case 3: Explore Network Variability Results
2b. Alternatively, search for a hub gene of interest. Type BRAF into the text box under the column heading Hubs.
The network with BRAF at it’s centre does not appear to be dysregulated.
3. Click on the Pairs Concordance tab to view the correlation between every hub and its partners. Type in TGM2 click on the right-most column twice to sort it in desending order
click twice click
The University of Sydney Page 14
Case 3: Explore Network Variability Results
4. Create a graphical representation of the differences. Click on Network Plots, then on Difference
5. Type TGM2 into the hubs list and select it.
Case 4: Fast Cross-validated Classification
Performing cross-validation such as 100 resample 5-fold cross- validation requires substantial programming skills.
2. Define 2 groups of samples. Click on the Rules grouping option. Fill out the form as shown.
Note that the class sizes are 1. Click on Dataset and
Grouping in the navigation bar, then on the Dataset Definer tab.
click
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click
click 3. Click on
Classification in the navigation bar, then on the Settings tab.
Case 4: Fast Cross-validated Classification
4. Do 10 resamples, 5-fold cross-validation.
Fill out the form according to the example below.
Once the spinning circle stops, you may switch to other tabs of this section of the application to see the results.
5. Click on the Cross-validation Error tab.
Case 4: Fast Cross-validated Classification
6. Click on the Sample-wise Error tab to see the classification errors per patient.
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The cross-validation error is only about a couple of percent in all 10 iterations.
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Two men have error rates in excess of 0.4, including one in excess of 0.6.
The University of Sydney Page 18
Case 5: Quick Quality Control
– Some genes are, by definition, only expressed in men (e.g. ZFY, UTY) and others only in women (e.g. XIST). Most clinical datasets available online include basic information, such as gender.
1. Click on Measurement Viewer in the navigation bar, then on the Distribution Plot tab.
2. Choose the mRNA expression table and the ZFY probe. Click on the Update Plot button.
3. Observe that only men have expression of this gene above background level.
Case 5: Quick Quality Control
4. Choose XIST as the gene to plot. Click Update Plot again. The samples which have expression above background levels are almost exlusively women.
The clinical data regarding gender is agreeable with orthogonal mRNA expression microarray data.