Dear all,
thank you for the great package, it´s a staple in my workflows.
Recently, I had the task to analyze continuous covariates in my RNA-seq data. I know continuous variables can be included in the model, but I would like to estimate the p-values for the logfoldchanges, and the DESeqStats class takes only a contrast, e.g. from a categorical factor.
Estimating continuous variables would be amazing.
(I did check and test if it´s possible, but could not find anything. Please correct me if I am wrong.)
A minor feature request: Add the option to create the design from a mode formula (patsy-style) in DESeqDataSet. If I can set the reference level of several categorical factors in my data and then create the design matrix from a formula (in comparison to setting the ref_level of only one design factor), I could fit a model once and then extract DESeqStats for several variables.
Thank you for any help and best regards,
Max
Dear all,
thank you for the great package, it´s a staple in my workflows.
Recently, I had the task to analyze continuous covariates in my RNA-seq data. I know continuous variables can be included in the model, but I would like to estimate the p-values for the logfoldchanges, and the
DESeqStatsclass takes only a contrast, e.g. from a categorical factor.Estimating continuous variables would be amazing.
(I did check and test if it´s possible, but could not find anything. Please correct me if I am wrong.)
A minor feature request: Add the option to create the design from a mode formula (
patsy-style) inDESeqDataSet. If I can set the reference level of several categorical factors in my data and then create the design matrix from a formula (in comparison to setting theref_levelof only one design factor), I could fit a model once and then extractDESeqStatsfor several variables.Thank you for any help and best regards,
Max