Hi,
I am doing differential expression analyses for the first time and using DESeq2. The tutorials are great, but I have a couple niche questions that I cannot find the precise answer to.
1) If I use a truth table for my ColumnData in DESeq2 where my rows are samples and my columns are events that occur or dont occur as follow:
Event A Event B Event C
S1 1 1 0
S2 1 0 1
S3 0 0 0
S4 1 0 1
Where 1 is the column event is occurring and 0 means the event is not occurring. Can DESeq2 recognize these as binary indicators of categorical data or will it assume it as a measure?
2) I would like to create a heatmap that increases from least difference to most difference, for the respective columns selected in ColumnData, from left to right and bottom to top. How do I do this appropriately for a count matrix?
Thank you, any help is appreciated, I just want to make sure I am proceeding correctly.
1)Thank you so much Mike! I have never used factors, I know I can make columns into arrays and use factor() on those. Though I am not sure how to apply this to a data.frame() let alone one the has columns representing both quantitative and categorical data?
2) I just want to make sure this is the ordering by p-value? Lower the p-value the greater the difference?
Thank you so much for taking the time.
If you want to use DE methods in R/Bioconductor, you should get to know factors!
These are a workhorse class for linear models and making comparisons in R. I'd suggest following some of these links:
http://genomicsclass.github.io/book/pages/resources.html
Yes you would order by p-value. Yes, lower p-values mean that the null hypothesis should be rejected, where the null hypothesis is typically "no difference". You should also probably do some catch-up on basics of inference, p-values, adjusted p-values, FDR, etc. See the Inference section here, and further down, the multiple testing section:
http://genomicsclass.github.io/book/
Thank you so much, I will definitely review. I appreciate you taking the time.