Reputation: 1446
I have a data frame with close to a 4 million of rows in it. I need an efficient to way to subset the data based on two criteria. I can do this is a for loop but was wondering if there is a more elegant way to do this, and obviously more efficient. The data.frame looks like this:
SNP CHR BP P
rs1000000 chr1 126890980 0.000007
rs10000010 chr4 21618674 0.262098
rs10000012 chr4 1357325 0.344192
rs10000013 chr4 37225069 0.726325
rs10000017 chr4 84778125 0.204275
rs10000023 chr4 95733906 0.701778
rs10000029 chr4 138685624 0.260899
rs1000002 chr3 183635768 0.779574
rs10000030 chr4 103374154 0.964166
rs10000033 chr2 139599898 0.111846
rs10000036 chr4 139219262 0.564791
rs10000037 chr4 38924330 0.392908
rs10000038 chr4 189176035 0.971481
rs1000003 chr3 98342907 0.000004
rs10000041 chr3 165621955 0.573376
rs10000042 chr3 5237152 0.834206
rs10000056 chr4 189321617 0.268479
rs1000005 chr1 34433051 0.764046
rs10000062 chr4 5254744 0.238011
rs10000064 chr4 127809621 0.000044
rs10000068 chr2 36924287 0.000003
rs10000075 chr4 179488911 0.100225
rs10000076 chr4 183288360 0.962476
rs1000007 chr2 237752054 0.594928
rs10000081 chr1 17348363 0.517486
rs10000082 chr1 167310192 0.261577
rs10000088 chr1 182605350 0.649975
rs10000092 chr4 21895517 0.000005
rs10000100 chr4 19510493 0.296693
The first I need to do is to select those SNP
with a P
value lower than a threshold, then order this subset by CHR
and POS
. This is the easy part, using subset
and order
. However, the next step is the tricky one. Once I have this subset, I need to fetch all the SNP
that fall into a 500,000 window up and down from the significant SNP
, this step will define a region. I need to do it for all the significant SNP
and store each region into a list or something similar to carry out further analysis. For example, in the displayed data frame the most significant SNP
(i.e below a threshold of 0.001) for CHR==chr1
is rs1000000
and for CHR==chr4
is rs10000092
. Thus these two SNP
would define two regions and I need to fetch in each of these regions the SNPs that fall into a region of 500,000 up and down from the POS
of each of the most significant SNP
.
I know it a bit complicated, right now, I am doing the tricky part by hand but it takes a long time to do it. Any help would be appreciated.
Upvotes: 0
Views: 397
Reputation: 13827
Here is a partial solution ir R using data.table
, which is probably the fastest way to go in R when dealing with large datasets.
library(data.table) # v1.9.7 (devel version)
df <- fread("C:/folderpath/data.csv") # load your data
setDT(df) # convert your dataset into data.table
# Filter data under threshold 0.05 and Sort by CHR, POS
df <- df[ P < 0.05, ][order(CHR, POS)]
df[, {idx = (1:.N)[which.min(P)]
SNP[seq(max(1, idx - 5e5), min(.N, idx + 5e5))]}, by = CHR]
df[, fwrite(copy(.SD)[, SNP := SNP], paste0("output", SNP,".csv")), by = SNP]
ps. note that this answer uses fwrite
, which is still in the development version of data.table
. Go here for install instructions. You could simply use write.csv
, however you're dealing with a big dataset so speed is quite important and fwrite
is certainly one of the fastest alternatives.
Upvotes: 2