DEGs between two samples (Control and Expt)

Support Questions
1

Hi,
I am wondering if it is possible to analyze DEGs between two samples but not by cluster. Also, is it possible to generate a feature plot side by side for two samples?

Thank you
Rupendra

2

Hi Rupendra,

Yes, it is possible to analyze DEGs between two samples. You just have to select the option “Compare a selected cell set between samples/groups” and then select “All” in the “Compare cell set:” menu and the two samples you want to compare in the other two dropdown menus.

Regarding the feature plot, it is currently not possible to generate it side by side for two samples. The only workaround would be to download the two plots separately and then put them side by side outside of Cellenics. We currently have the option to generate multiple plots in the same view only for the violin plot.

I hope this helps.
Let me know if you have any questions.
Sara

3

Hi,Sara_Bioinformatics,
I have the similar questions. I have 9 samples in three groups(A,B and C). e.g, each group has 3 samples. I want to analize DEGs of the certain cellset between each groups. A toB,Ato C, B to C. There are two ways to do DEGs. The way one , for example, selecting sample 1 in A group compared to all of others. One by all of the rest them separately. In this case, DEGs of one gene represented that difference of gene between sample 1 of A group to all the other samples of A group and all samples in B group and C group. May I be right? The second way, If I want to DEGs of all genes between A,B or C in mean value per group,not one sample of each group, how should I do this? What difference meaning in biology between the way 1 and way2 ?

Thank you.

Zhen

4

Hi Zhen,

You’ve outlined two distinct approaches, and I’d be happy to provide some insights into both.

  1. In the first approach, where you’re considering comparing one sample from a group (like Sample 1 from Group A) with all other samples in the dataset, the DEGs identified will reflect the unique expression profile of that specific sample in contrast to the rest. This method can be particularly useful if your objective is to understand the unique characteristics or anomalies of a single sample. However, it’s important to note that this approach might not give you a comprehensive view of the general differences between the groups, as it focuses on individual variability.
  2. The second approach, where you compare the gene expression levels of each group, tends to provide a more representative overview of the differences between the groups. This approach is more aligned with typical research objectives where the goal is to understand the broader differences in gene expression due to different conditions or treatments across groups. To implement this approach, you should select the option “Compare a selected cell set between samples/groups” in Cellenics. Then, choose the cell set and the groups of interest from the dropdown menus.

As for the biological significance, it’s important to consider your research question. If your interest lies in pinpointing how a particular sample stands out from the others, the first method is appropriate. However, if you’re looking to understand overarching trends and differences between groups A, B, and C, the second method will likely be more informative and relevant.

I hope this helps.
Sara