Webinar: Gene Expression Analysis with KNIME Analytics Platform - July 9

You were able to join Jeany and Temesgen on July 9 and find out how genes express themselves :slight_smile:

Gene expression analysis is widely used in bioinformatics because it enables researchers to find gene products with increased or decreased synthesis in individuals with particular diseases, for example. In this webinar, we find and annotate differentially expressed genes from tumors as well as matched normal tissue from patients with oral squamous cell carcinomas. For this, we make use of KNIME’s openness for other tools which enables us to use our favourite R library, extract data from Google’s BigQuery and use shared components to customize our analysis.

Jeany is Head of the Life Science team at KNIME and Temesgen is a data scientist whose focus is on Life Science.

Jeany and Temesgen have put together the questions & answers that were discussed at the webinar. Some of these questions are listed here. Click the arrow next to the question to see the answer :smiley:

How can I find the workflow that was demonstrated in the talk?

You can download it from the KNIME Hub here.

Can the workflow generate Python or R code?

You can integrate your favorite R or Python code into KNIME, but KNIME cannot create R or Python code.

Do you have any work related example involving covid-19 genes?

You might check out this workflow. Note that this is simply a visualization limited to COVID outbreaks.

Is it is possible to work with multiple samples?

Yes, in the example that was demo’ed we have 3 samples. See a recording of the webinar here for reference.

Can KNIME be used for microarray data analysis?

The answer depends what you want to do. KNIME can always be used as a platform but the solution/algorithms comes from the expert. For example if you have an R package that is doing a good job in analyzing mircroarray data you can use it from KNIME.

Quick question about the gene annotations. How were they generated?

They were gathered using the REST API of Open Targets Platform.

When I change my R workflow, would I need to copy and paste the code again or could a node be pointed to the R-file and automatically use the updated code (the same code that would run fine in R directly)?

To load your R source code directly from a file, you can use the source() function of R.

Similar to interactive volcano plots; can you also do interactive 3D PCA plots of gene expression data?

To interactively display 3D data one use the 3D Scatter Plot (plotly).

Further resources