Hi,

I performed polysome profiling to measure translation rate in wildtype and knockout cells. For the polysome samples I collected a monosome fraction, a low polysome fraction, and a high polysome fraction. I also collected mRNA for both wildtype and knockout. I'm interested in comparing translation changes between knockout and wildtype (and most interested in the change in high polysome relative to changes in mRNA). 

I'm using limma to find differences in the ratio of (ko high / ko mRNA) / (wt high / wt mRNA). I initially input all of my samples (mRNA, monosome, low, high) into limma and only pulled out the ratio that I was interested in. However, I noticed that the samples I put in changes the number of significant genes a lot even when I only pull out the ko vs wt high vs mRNA ratio:

I know that voom takes the variation across samples into account is that the reason that I'm losing significance the more sample types that I put in? Looking at the samples I don't think there are any outliers that are increasing overall variance.

This is the code that I'm using:

layout <- data.frame(row.names = colnames(count_mat),
                     type = colnames(count_mat),
                     condition = colnames(count_mat),
                     stringsAsFactors = F)
layout[grep('high', colnames(count_mat)), ]$type <- 'high'
layout[grep('low', colnames(count_mat)), ]$type <- 'low'
layout[grep('mono', colnames(count_mat)), ]$type <- 'mono'
layout[grep('mRNA', colnames(count_mat)), ]$type <- 'mRNA'
layout[grep('ko', colnames(count_mat)), ]$condition <- 'ko'
layout[grep('wt', colnames(count_mat)), ]$condition <- 'wt'
limmaModel <- paste(layout$condition, layout$type, sep = '.')
limmaModel <- factor(limmaModel)
print(layout)

design <- model.matrix(~0 + limmaModel)
colnames(design) <- levels(limmaModel)
rownames(design) <- rownames(layout)

# Filter low read counts
count_mat_filtered <- count_mat[rowSums(cpm(count_mat) > 5) >= 3, ]

# Normalize to log2(CPM + 0.5) with voom
d <- DGEList(count_mat_filtered, genes = rownames(count_mat_filtered))
d <- calcNormFactors(d)
v <- voom(d, design, plot = T)

# Differential expression
fit <- lmFit(v, design)
cont.matrix <- makeContrasts(
  interaction_high_mrna = (ko.high - ko.mRNA) - (wt.high - wt.mRNA),
  levels = design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)

interaction_high_mrna <- topTable(fit2, coef = 'interaction_high_mrna', number = nrow(count_mat_filtered))

sum(interaction_high_mrna$adj.P.Val < 0.05)

R version 3.4 edgeR_3.18.1 limma_3.32.9

Remember that limma is estimating a single value of the variance for each gene based on all the samples that are provided to it. This same variance value is then used for all tests involving that fitted model. Of course adding more samples also increases the precision of the estimated variance, which should improve your statistical power, all else being equal. If adding in more samples is causing you to get fewer significant genes, the most likely explanation is that the samples you added in have a higher variance than the samples that were already in the model. This should show up clearly on the voomWithQualityWeights plot. To make this more obvious, you could force voomWithQualityWeights to estimate a single weight for each group by passing your design as the var.design argument (in addition to the regular design). Keep in mind that a higher variance should result in a lower weight, so you should be looking for the mono and low groups to have low weights.