# Quickly aggregate your data in R with data.table

2015-01-28

In genomics data, we often have multiple measurements for each gene. Sometimes we want to aggregate those measurements with the mean, median, or sum. The data.table R package can do this quickly with large datasets.

In this note, we compute the average of multiple measurements for each gene in a gene expression matrix.

To see what else you can do with data.table, check out these fantastic cheat sheets:

# Summary#

1. Make random data
2. Aggregate quickly with data.table
3. Aggregate slowly with stats::aggregate()

# Step 1. Make random data#

random_string <- function(n, chars) {
replicate(n = n, expr = {
paste(sample(LETTERS, chars, replace = TRUE), collapse = "")
})
}

set.seed(42)

# Each gene is represented by 1 or more probes.
n_probes <- 1e5
gene_names <- random_string(n_probes, 3)
sort(table(gene_names), decreasing = TRUE)[1:10]
#> gene_names
#> BWU DIH QLP WAX XVA ZPT FBE HMO LZS NTD
#>  17  16  16  16  16  16  15  15  15  15

library(data.table)
d <- data.table(
Gene = gene_names,
Probe = seq_along(gene_names)
)

# We measured genes in a number of samples.
n_samples <- 100
for (i in seq(n_samples)) {
d[[sprintf("S%s", i)]] <- rnorm(nrow(d))
}

# Now we have a gene expression matrix.
# Notice that gene "AAB" is represented by multiple probes.
d <- d[order(d$Gene)] d[1:5,1:5] #> Gene Probe S1 S2 S3 #> 1: AAA 17339 0.6886677 -0.59704276 0.73534953 #> 2: AAA 19529 0.2430747 0.48148142 -0.07411017 #> 3: AAA 19915 0.4096048 -0.02989425 -0.91970815 #> 4: AAA 34545 -0.4604622 -2.02437078 -0.09687003 #> 5: AAA 81092 0.9990284 -1.22508517 1.32621912 # Step 2. Aggregate quickly with data.table# Now we can easily average the probes for each gene. system.time({ d_mean <- d[, lapply(.SD, mean), by = Gene, .SDcols = sprintf("S%s", 1:100)] }) #> user system elapsed #> 0.179 0.015 0.072 d_mean[1:5,1:5] #> Gene S1 S2 S3 S4 #> 1: AAA 0.33549805 -0.9506345 0.04691119 -0.08173554 #> 2: AAC -0.46102761 -0.2623302 -0.03353125 0.20238075 #> 3: AAD 0.02861623 0.6479871 0.42838896 -0.28628726 #> 4: AAE 0.04544300 -0.3214495 0.33347142 0.11855635 #> 5: AAF -0.25445109 -0.5118745 -0.24236236 0.03974918 # Step 3. Aggregate slowly with stats::aggregate()# The base R function stats::aggregate() can do the same thing, but it is much slower. dat <- data.frame(d) system.time({ d_mean2 <- aggregate(dat[, 3:102], by = list(dat$Gene), mean)
})
#>    user  system elapsed
#>  10.617   0.181  10.862

The results are identical:

colnames(d_mean2)[1] <- "Gene"
all.equal(d_mean, data.table(d_mean2))
#> [1] TRUE

Feel free to edit the source code for this post.