Interesting. You may get a bit more speed if
you replace this
egen rank_1 = rank(expression), by(ssrownum)
egen rank_2 = rank(iso_VSV), by(ssrownum)
egen corr = corr(rank_1 rank_2), by(ssrownum)
by this:
sort ssrownum
by ssrowsum : egen rank_1 = rank(expression)
by ssrowsum : egen rank_2 = rank(iso_VSV)
by ssrowsum : egen corr = corr(rank_1 rank_2)
The two code segments are equivalent in what
you end with, but not in when they -sort-.
SImilarly
keep if _n >= `start' & _n <= `stop'
should be faster as
keep in `start'/`stop'
and I would always use the built-in -sqrt()-
when it applies, rather than powering to 0.5.
Nick
[email protected]
David Airey
> Nick Cox asked how egen rank and egen corr compares to using
> spearman
> in the context of statsby. It turns out to be at least 10 times
> faster. Thanks Nick.
>
>
> // Break file into subfiles of 500 "by groups" (genes),
> // because Stata "statsby" command is so slow across 22K by groups,
> // then calculate and save out spearman correlation stats
> // for iso_VSV and gene expression and append into a file 22K
> rows long
> // and apply FDR
>
> // This version comments out statsby and uses egen rank and egen corr
> // and consequently is 10 times fast, doing 22K spearman correlations
> // in about 50 seconds (along with file creation, etc.).
>
> set more off
> set rmsg on
> clear
> set memory 200M
>
> // sabatini_merged is a long format file
> // merging expression data and editing data
> use "/Users/dairey/Desktop/primate_editing/sabatini_merged.dta"
>
> preserve
> local start = 1
> forvalues stop = 6000(6000)267396 {
> keep if _n >= `start' & _n <= `stop'
> save sabatini_`stop'.dta, replace
> egen rank_1 = rank(expression), by(ssrownum)
> egen rank_2 = rank(iso_VSV), by(ssrownum)
> egen corr = corr(rank_1 rank_2), by(ssrownum)
> collapse (mean) spearman=corr (count) count=corr, by(ssrownum)
> gen t = spearman*((count-2)/(1-spearman^2))^0.5
> gen p = 2*ttail(count-2,abs(t))
> save spearman_`stop'.dta, replace
> local start = `stop'+1
> restore, preserve
> }
>
> clear
> use "/Users/dairey/Desktop/primate_editing/sabatini_merged.dta"
> keep if _n > 264000 & _n <= 267396
> save sabatini_267396.dta, replace
> keep ssrow expression iso_VSV
> egen rank_1 = rank(expression), by(ssrownum)
> egen rank_2 = rank(iso_VSV), by(ssrownum)
> egen corr = corr(rank_1 rank_2), by(ssrownum)
> collapse (mean) spearman=corr (count) count=corr, by(ssrownum)
> gen t = spearman*((count-2)/(1-spearman^2))^0.5
> gen p = 2*ttail(count-2,abs(t))
> save spearman_267396.dta, replace
>
>
> // append the statsby results into one file
> use spearman_6000.dta
> forvalues stop = 12000(6000)267396 {
> append using spearman_`stop'.dta
> erase spearman_`stop'.dta
> }
> append using spearman_267396.dta
> erase spearman_267396.dta
> save iso_VSV.dta, replace
> erase spearman_6000.dta
>
> // see if there is significance after FDR
> multproc, pvalue(p) method(simes) puncor(0.05) rank(rank)
>
> // keep top 100 and bring in gene information against ssrownum key id
> sort rank
> keep if rank < 101
> sort ssrownum
> merge ssrownum using "/Users/dairey/Desktop/primate_editing/
> sabatini_genes.dta"
> keep if _merge == 3
>
> // list gene information
> sort rank
> list gene_title
> set rmsg off
> set more on
>
> /*
>
> // use a forvalues loop to make smaller files and do statsby
> preserve
> local start = 1
> forvalues stop = 6000(6000)267396 {
> keep if _n >= `start' & _n <= `stop'
> save sabatini_`stop'.dta, replace
> statsby n=r(N) spearman=r(rho) p=r(p), by(ssrownum): ///
> spearman iso_VSV expression
> save spearman_`stop'.dta, replace
> local start = `stop'+1
> restore, preserve
> }
>
> // above, the forvalues misses data after row 264000 so
> // those are picked up here
> clear
> use "/Users/dairey/Desktop/primate_editing/sabatini_merged.dta"
> keep if _n > 264000 & _n <= 267396
> save sabatini_267396.dta, replace
> statsby n=r(N) spearman=r(rho) p=r(p), by(ssrownum): ///
> spearman iso_VSV expression
> save spearman_267396.dta, replace
>
> */
>
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