Hi there. I am analyzing fathead minnow EcoToxChip samples, and a few of my samples had failed GDC. I decided to look at the raw melt curve data and plot the florescence and temperature to look for amplified products in every sample (not just samples with flagged GDC). I found that each of my samples had ~20 wells that contained multiple products of different size amplified, so I want to remove these genes from the dataset as it could mean that gDNA was amplified. Unsurprisingly, when I delete these wells from the excel sheets of each sample, EcoToxXplorer no longer supports the data files, and I cannot use the website for the analysis.
Is there a way around this so I can still use EcoToxXplorer?
The tool expects a well-defined EcoToxChip format, removing them manually will confuse the program as you experienced. Try to achieve this using the tool functions. For instance
Use “Data Filter” option to remove those genes - you can edit their Ct values to trigger the filter. If this is easy to detect by computer, we should add a new filter to deal with this situation
Use “Data Editor” to remove them - it seems we should add a feature / gene-level edit
Thank you very much for getting back to me. I am going to try your first suggestion and edit the Ct values to be higher and trigger the data filter. From what I understand from the website and FAQ page, values higher than the cut-off are removed from the dataset, but the software then assumes that these wells had very low/no expression and thus are replaced with a value that reflects that.
In my case, wouldn’t that lead to inaccuracy as I manually changed these wells to be higher? Would I then have to uncheck the impute non-detects option and if so does that effect the quality of my differential expression analysis?
The previous answer was to illustrate the cause of the issue with an example on how to get around this. Keep in mind that you do have the full control:
If you know better values, please manually enter the values
If you don’t know better values, and don’t like the previous approach - you can also use non-detects or missing values which should be denoted with “NA”.