Entering edit mode
In the TOAST package, the results include a beta term, beta_var,mu and effect_size.
- Does any of these terms have a biological interpretation?
- Is there a way to get the mean of the expression for the specific cell type (after the deconvolution) ?
library(TOAST)
data("RA_100samples")
Y_raw <- RA_100samples$Y_raw
Pheno <- RA_100samples$Pheno
Blood_ref <- RA_100samples$Blood_ref
outRF1 <- csDeconv(Y_raw, K=6, TotalIter = 1, bound_negative = TRUE)
design <- Pheno[,"disease",drop=F]
design$disease <- factor(design$disease, levels=unique(design$disease))
props_vignette <- outRF1$estProp
colnames(props_vignette) <- colnames(Blood_ref)
Design_vignette <- makeDesign(design, props_vignette)
fitted_model_vignette <- fitModel(Design_vignette, as.matrix(Y_raw))
summary(res_table_vignette$Gran$effect_size)
head(res_table_vignette$Gran)
============Output===================
Min. 1st Qu. Median Mean 3rd Qu. Max.
-28835.009 -0.566 -0.015 -9.812 0.499 184.234
beta beta_var mu effect_size
cg16034991 1.1270472 0.08304893 -0.12320596 2.559623
cg13293535 0.8020964 0.04121726 -0.03557254 2.194664
cg01479768 1.3355404 0.11858170 -0.27028634 3.359986
cg15172529 0.8254852 0.05158534 -0.06037535 2.342684
cg11045746 1.1931990 0.11201747 0.01766526 1.942483
cg00414890 0.8940398 0.06705851 -0.01381030 2.063758
f_statistics p_value fdr
cg16034991 15.29502 0.0001805514 0.2024207
cg13293535 15.14201 0.0001934513 0.2024207
cg01479768 15.04168 0.0002024207 0.2024207
cg15172529 13.20968 0.0004681206 0.3510904
cg11045746 12.70984 0.0005906279 0.3519388
cg00414890 11.91955 0.0008559312 0.3519388
I have found out from CARseq article that
So I understand that the mean across all conditions per cell-type is (μ+β/2). That is analogous for example to DESeq2's basemean. But