Compute log fractions and precision weights from matrix of c ounts, where columns are variables and rows are samples
Value
An EList object with the following components:
- E:
numeric matrix of log transformed counts
- weights:
numeric matrix of observation-level inverse-variance weights
Details
For real data, the asymptotic variance formula can give weights that vary substantially across samples and give very high weights for a subset of samples. In order to address this, we regularize the weights to reduce the variation in the weights to have a maximum ratio of max.ratio between the maximum and quant quantile value.
Examples
# set probability of each category
prob <- c(0.1, 0.2, 0.3, 0.5)
# number of total counts
countsTotal <- 300
# number of samples
n_samples <- 100
# simulate info for each sample
info <- data.frame(Age = rgamma(n_samples, 50, 1))
rownames(info) <- paste0("sample_", 1:n_samples)
# simulate counts from multinomial
counts <- t(rmultinom(n_samples, size = countsTotal, prob = prob))
colnames(counts) <- paste0("cat_", 1:length(prob))
rownames(counts) <- paste0("sample_", 1:n_samples)
# run logFrac on counts
cobj <- logFrac(counts)
# run standard variancePartition analysis on crumblr results
library(variancePartition)
fit <- dream(cobj, ~ Age, info)
fit <- eBayes(fit)
topTable(fit, coef = "Age", sort.by = "none")
#> logFC AveExpr t P.Value adj.P.Val B
#> cat_1 0.0004874472 -2.4063982 0.1499668 0.8810907 0.9119836 -8.856395
#> cat_2 -0.0013536071 -1.6940978 -0.7022378 0.4841504 0.9119836 -8.562192
#> cat_3 0.0008978300 -1.3177560 0.5717690 0.5687521 0.9119836 -8.642022
#> cat_4 -0.0001136616 -0.7907723 -0.1108149 0.9119836 0.9119836 -8.791745