Dear All,

Hope you are well. I used tradeSeq for my monocle3 object to find DEG of various lineage along trajectory. Output provides gene names, waldStat, pvalue and meanLogFC. This is my first time doing tradeSeq. Therefore, I am wondering whether p-value here referred adjust-pvalue? How to know which genes are up- and down-regulated. Less than 1 is down or greater than 1 is up? because I did not see any negative meanLogFC in my tradeSeq output at all. Hope you could help review and give me some answers. Here are my tradeSeq R script! Could you help review whether my script is right. Many thanks beforehand !

BiocManager::install(c("tradeSeq", "SingleCellExperiment", "slingshot"))
library(tradeSeq)
library(SingleCellExperiment)
library(slingshot)


#pseudotime obj
# Extract pseudotime and assume uniform cell weights
pseudotime <- as.numeric(pseudotime(cds_second))
cellWeights <- matrix(1, ncol = 1, nrow = length(pseudotime))

# Create SingleCellExperiment object
sce <- SingleCellExperiment(assays = list(counts = counts(cds_second)))
colData(sce)$pseudotime <- pseudotime
colData(sce)$cellWeights <- cellWeights

# Fit the GAM
sce <- fitGAM(counts = counts(sce), pseudotime = pseudotime, cellWeights = cellWeights)

# Perform differential expression testing as a function of pseudotime! 
diffExpr <- associationTest(sce)
diffExpr_omitna<- na.omit(diffExpr)
significant_genes <- rownames(diffExpr)[which(diffExpr$pvalue < 0.05)]

#filter significant genes 
second_deg_pseudo<- diffExpr_omitna %>% filter(pvalue < 0.05)
write.csv(second_deg_pseudo, file = "second_deg_pseudo.csv")

# View significant genes
print(significant_genes)

Kind Regards,
Synat