pathway_pca

[raqxavier]

Good afternoon

In the function pathway_pca there are ellipses shown. What do they stand for?
Thank you,
Raquel

[cafferychen777]

Dear Raquel,

Thank you for your question regarding the ellipses in the pathway_pca function. The ellipses represent the 95% confidence intervals for the data points in the PCA plot. These confidence intervals provide a visual indication of the uncertainty associated with the estimated position of each data point in the reduced dimensional space.

In statistical terms, a 95% confidence interval means that if the same population is sampled on numerous occasions and interval estimates are made on each occasion, the resulting intervals would bracket the true population parameter in approximately 95% of the cases.

I hope this clarifies the meaning of the ellipses in the pathway_pca function. Please let me know if you have any further questions.

Best regards,
Chen YANG

[raqxavier]

Dear Chen Yang

Thank you so much for your quick reply! I thought it would be something along those lines and wanted to be sure I interpret the graph correctly. And the plots on the top and lateral sides of the PCA plot? they depict the distribution of the data in those principal components? They are nice but I am unsure about the info they add to the pca.

Many thanks!
Raquel

[cafferychen777]

Dear Raquel,
You are correct about the plots on the top and lateral sides of the PCA plot. These plots, often referred to as marginal plots or marginal histograms, depict the distribution of the data along each principal component axis.

The main purpose of these marginal plots is to provide additional insight into the distribution of the data in each principal component. They can help identify patterns, such as skewness or multimodality, that may not be apparent in the main PCA plot. Additionally, these plots can be useful for detecting outliers or unusual observations in each principal component.

While the marginal plots do not directly contribute to the interpretation of the relationships between data points in the PCA plot, they offer a complementary perspective on the data distribution. They can be particularly helpful when dealing with high-dimensional data, as they provide a way to visualize the data along each principal component separately.

However, if you find that the marginal plots do not add significant value to your analysis or if they clutter the visualization, you can consider removing them from the plot.

I hope this explanation helps clarify the role of the marginal plots in the pathway_pca function. Please feel free to ask if you have any further questions.

Best regards,
Chen Yang​​​​​​​​​​​​​​​​

[raqxavier]

Thank you so much for your responses. You were very helpful.

Best regards
Raquel