Hello, I have a question about model.matrix() while I'm doing some analysis using deseq2. I have counts and coldata variables. The counts column has about 15 samples, with 7 being "Ctrl" and 8 being "Treat." The rows of counts represent gene lists with expression levels. The coldata variable has a column called "disease" and another column called "new_variance" with 15 samples as rows. The "disease" column has 7 "Ctrl" and 8 "Treat" labels, and the "new_variance" column has continuous values(e.g., 1.434, 1.5, 0.989, ...). The "new_variance" data could be one of the behavioral experimental data for each sample. In conclusion, I wanted to investigate the correlation between the expression levels of RNA-seq data and "new_variance."
Here's my modeling. I'm having trouble understanding the meaning of the following three cases of
# 1. disease
mm1 = model.matrix(disease, coldata)
ddsMat <- DESeqDataSetFromMatrix(counts, coldata, design = ~ 1)
ddsMat2 = DESeq(ddsMat, full = mm1, betaPrior = FALSE)
# 2. new_variance
mm2 = model.matrix(~new_variance, coldata)
ddsMat3 <- DESeqDataSetFromMatrix(counts, coldata, design = ~ 1)
ddsMat4 = DESeq(ddsMat3, full = mm2, betaPrior = FALSE)
# 3. interaction
mm3 = model.matrix(~new_variance*disease, coldata)
ddsMat5 <- DESeqDataSetFromMatrix(counts, coldata, design = ~ 1)
ddsMat6 = DESeq(ddsMat5, full = mm3, betaPrior = FALSE)
In the above examples, I can easily understand that 1. disease is a typical case study in RNA-seq analysis, where we can predict the Fold Change as treat/control because the "disease" column in coldata clearly distinguishes "Ctrl" and "Treat" as categorical values.
However, for 2. new_variance, can we distinguish "Ctrl" and "Treat"? Furthermore, I'm not clear on how to interpret the interaction in 3. What I'm expecting is that the meaning of RNA-seq analysis might change a bit. I thought that if I use the "new_variance" column, it's not a typical case study. So, when applying a new metric to RNA-seq analysis, what does it mean, and how should I interpret the statistical analysis results?
I've looked at "Analyzing RNA-seq data with DESeq2," but I didn't quite understand it. If you could help me understand what I'm missing, I would be really grateful. In the cases of 2 and 3, what is the meaning of the Fold Change that appears in the statistical analysis results? If there's a correct answer, and if I've missed something, providing a reference link would be great. Thank you.
Fairness demands that you at least link https://support.bioconductor.org/p/9154282/ where you asked this before and got help already. Follow up on it. What is unclear after the answer from the colleague over at Bioconductor?
Thank you for the response. First, I apologize for not providing a link to the previous topic. Based on the advice from a colleague in the previous topic, I asked the question here because there was an option to do so. I was hoping for any additional insights that might come up, while also doing some reading on Google and linear regression. While I haven't found the material yet, I still don't have a clear understanding of the relationship between linear regression and RNA-seq modeling. I saw some information in this document([https://www.researchgate.net/profile/Ahmad-Al-Khraisat-2/post/I_would_like_to_know_how_to_make_a_co-expression_diagram_with_only_the_Salmon_table_of_counts_normalization_in_TPM/attachment/6032706deb0eda00013d933c/AS%3A993659591872514%401613918316996/download/Intro2RNAseq.pdf]). In section 6.1, but ultimately, it wasn't easy for me to interpret how the coefficients and variables of linear equations relate to what I'm trying to model. I believe the linear regression presented in the reference material is too generic for my specific modeling case. Therefore, I still don't fully understand what linear regression means for my modeling.