Merging data frames by selecting for correct value - r

Question

I have a data frame called "ref" that contains information that allows mapping of gene entrez ID to the gene's start and end positions. I have another data frame "ori_data" where each row contains unique mutations from samples, which gives a genomic position. I am trying to assign each position given in "ori_data" to map to information on "ref" in order to assign entrez ID to each mutation. I have tried a for loop to match for the same chromosome, and then select for positions in "ori_data" that fall between the coordinates in "ref" though I have not been successful. The "ori_data" dataset is over 1 million rows, so I'm not sure a for loop is an efficient solution. Note that many positions will be mapped to the same entrez ID in my real dataset. "Final" is what I want to happen- which would just add a column for entrezID according to chromosome/position. TYIA!
ref = data.frame("EntrezID" = c(1, 10, 100, 1000), "Chromosome" = c("19", "8", "20", "18"), "txStarts" = c("58345182", "18391281", "44619518", "27950965"), "txEnds" = c("58353492", "18401215", "44651758", "28177130"))
ori_data = data.frame("Chromosome" = c("19", "8", "20", "18"), "Pos" = c("58345186", "18401213", "44619519", "27950966"),
"Sample" = c("HCC1", "HCC2", "HCC1", "HCC3"))
final = data.frame("Chromosome" = c("19", "8", "20", "18"), "Pos" = c("58345186", "18401213", "44619519", "27950966"),
"Sample" = c("HCC1", "HCC2", "HCC1", "HCC3"), "EntrezID" = c(1,10,100,1000))
I have tried this line of code and I'm unsure as to why it does not work.
for (i in 1:dim(ori_data)[1])
{
for (j in 1:dim(ref)[1])
{
ID = which(ori_data[i, "Chromosome"] == ref[j,
"Chromosome"])
if (length(ID) > 0)
{
Pos = ori_data[ID, "POS"]
IDj = which(Pos >= ref[j, "txStarts"] & Pos <=
ref[j, "txEnds"])
print(IDj)
if (length(IDj) > 0)
{
ori_data = cbind("Entrez" = ref[IDj,
"EntrezID"], ori_data)
}
}
}
}

Answer 1

In base apply could be used to find matches per row for Chromosome and test if Pos is in the range of txStarts txEnds.
ori_data$EntrezID <- apply(ori_data[c("Chromosome", "Pos")], 1, \(x)
ref$EntrezID[ref$Chromosome == x["Chromosome"] &
x["Pos"] >= ref$txStarts & x["Pos"] <= ref$txEnds][1])
ori_data
# Chromosome Pos Sample EntrezID
#1 19 58345186 HCC1 1
#2 8 18401213 HCC2 10
#3 20 44619519 HCC1 100
#4 18 27950966 HCC3 1000
A version which could be faster:
lup <- list2env(split(ref[c("EntrezID", "txStarts", "txEnds")], ref$Chromosome))
ori_data$EntrezID <- Map(\(x, y) {
. <- get(x, envir=lup)
.$EntrezID[y >= .$txStarts & y <= .$txEnds][1]
}, ori_data$Chromosome, ori_data$Pos)
Or another way but not keeping the original order. (If original order is important, have a look at unsplit.)
#Assuming you have many rows with same Chromosome
x <- split(ori_data, ori_data$Chromosome)
#Assuming you have also here many rows with same Chromosome
lup <- split(ref[c("EntrezID", "txStarts", "txEnds")], ref$Chromosome)
#Now I am soting this by the names of x - try which Method ist faster
#Method 1:
lup <- lup[names(x)]
#Method 2:
lup <- mget(names(x), list2env(lup))
res <- do.call(rbind, Map(\(a, b) {
cbind(a, b[1][a$Pos >= b[[2]] & a$Pos <= b[[3]]][1])
}, x, lup))

Answer 2

One option would be to use sqldf, which should also be efficient for a large dataframe.
library(tibble)
library(sqldf)
as_tibble(sqldf("select dna.*, ref.EntrezID from dna
join ref on dna.Pos > ref.'txStarts' and
dna.Pos < ref.'txEnds'"))
Another option using fuzzy_join:
library(dplyr)
library(fuzzyjoin)
dna %>%
fuzzy_join(ref %>% select(-Chromosome), by = c("Pos" = "txStarts", "Pos" = "txEnds"),
match_fun = list(`>`, `<`)) %>%
select(names(dna), EntrezID)
Output
Chromosome Pos Sample EntrezID
1 19 58345186 HCC1 1
2 8 18401213 HCC2 10
3 20 44619519 HCC1 100
4 18 27950966 HCC3 1000

Answer 3

If the 'Pos', 'txStarts', 'txEnds' are numeric, then we can use non-equi join
library(data.table)
setDT(dna)[ref, EntrezID := i.EntrezID,
on = .(Chromosome, Pos > txStarts, Pos <txEnds)]
-output
> dna
Chromosome Pos Sample EntrezID
<char> <num> <char> <num>
1: 19 58345186 HCC1 1
2: 8 18401213 HCC2 10
3: 20 44619519 HCC1 100
4: 18 27950966 HCC3 1000
data
dna <- type.convert(dna, as.is = TRUE)
ref <- type.convert(ref, as.is = TRUE)