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Good practices in programming and data analysis and parallel programming with SAS

Good practices in programming and data analysis and parallel programming with SAS . Aldi Kraja October 17, 2008. Assume working in a project (Imputation for example). A. where is the location of my work? mylogin@dsgcluster.dsg.wustl.edu / dsguser / mylogin Where do I go from here?

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Good practices in programming and data analysis and parallel programming with SAS

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  1. Good practices in programming and data analysis and parallel programming with SAS Aldi Kraja October 17, 2008

  2. Assume working in a project (Imputation for example) • A. where is the location of my work? • mylogin@dsgcluster.dsg.wustl.edu • /dsguser/mylogin • Where do I go from here? • For example: • /dsgmnt/dsg200/genetics/users/jim/impfhs/ • /dsgmnt/dsg200/genetics/data/jim/impfhs/ • Let’s assume that the user created a lot of programs and data (or a few of them) pgms data

  3. Where Jim’s results go? • B. After work the results will go in a central directory, maybe in the • /dsgmnt/dsg200/genetics/data/fhsanalysis/impute/ • Who is in charge of that directory? Is that directory related with my project? What are the permissions of that directory? Do I need to change permissions of the directory, or files to read only? Do I need to notify anybody? • Examples: • drwx------ 2 aldi genetics 4096 Feb 4 2008 mail • cd /dsgmnt/dsg200/genetics • [aldi@blade6-3-1 genetics]$ ls -al • total 16 • drwxrwsr-x 4 aldi genetics 160 Sep 15 11:38 . • drwxr-xr-x 4 root root 128 Oct 14 11:38 .. • drwxrwsr-x 12 aldi genetics 312 Oct 15 12:00 data • -rw-rw-r-- 1 warwick genetics 0 Aug 26 10:50 loki.head • -rw-rw-r-- 1 warwick genetics 0 Aug 26 10:50 loki.nohead • drwxrwsr-x 16 aldi genetics 1824 Oct 16 14:26 users

  4. How can I access my pgms +data? • Line command editors: emacs, xemacs, vi, less, etc • sas & • Data: sas viewer (free software), sas, jmp, through samba (windows explorer, go to network, mercury5, login and passwd, /dsg1; /dsg2; /dsg3; /dsg4; /dsg100; /dsg200; /dsgweb • Batch work through LSF: bsubsas pgm.sas • proc print data=mydata (obs=10); title “my data”; run; • proc contents data=mydata; title “contents” run;

  5. Do I need details in the output? • My SAS output will be called “results”. Do I need to add anything else in the name of the set? • Do I need to keep the output all in one directory? • Does a colleague has the right to change your data? • Do I have the right to delete your data? • Do I have the right to delete yourtmp data? • Can I open your data in a sas viewer? • Can I delete your program run?

  6. Take charge of careful check of your program results • Assume I am reading text data with sas, but they are very large • SAS viewer does not open the created sas data, because they have more than 37,000 columns. What do you do? • JMP can go beyond that limit; proc print by keeping a few selected columns, compare with the original • If my variables are genotypes of this kind • 1/3 2/2 3/3 1/4 1/1 what do I need to do to save space in our servers?

  7. Parallel programming genefreq1 marknamechrom pos M1 1 10001 M2 1 10011 M3 1 10015 M4 1 10021 M5 2 20001 markname M1 M2 M3 M4 marknamealele percent M1 1 10.0 M1 3 90.0 M2 2 25.0 M2 4 75.0 M3 2 0.10 M3 4 99.9 M4 1 0.10 M4 2 99.9 map1 locdes1 Subject M1 M2 M3 M4… Mn 1 1/1 2/4 2/2 2/2 … 1/1 2 1/3 2/2 2/4 1/1 … 1/1 3 3/3 4/4 2/4 2/2 … 1/1 4 1/3 2/4 4/4 1/1 … 1/1 … ganon1

  8. Splitting data • *--------------------------------------; • * split.sas ; • * split data based on a decided number ; • * magicNumber=, ; • * By: Aldi Kraja ; • * October 3, 2008 ; • *--------------------------------------; • options mprintmlogicsymbolgen; • %macro split(magicNumber=200, • chroms=1, • chrome=1); • %do j=&chroms %to &chrome ; • libnameind&j "/dsgmnt/dsg200/genetics/data/mydata/impute/c&j"; • libnameout&j "/dsgmnt/dsg200/genetics/data/mydata/split/c&j"; • data genefreqc&j ; • set ind&j..genefreqc&j ; run; • data mlgeno&j ; • set ind&j..mlgenoc&j ; run; • data _null_; • file "./myscript.sh"; • put " cd /dsgmnt/dsg200/genetics/data/mydata/split/c&j" "; • put " find . -name '*sas7bdat' | xargs /bin/rm -f "; • put " cd /dsgmnt/dsg200/genetics/users/mydir/split/c&&j "; • run; • %sysexecchmod +x ./myscript.sh ; • %sysexec./myscript.sh ; • data _null_; • set ind&j..mapc&j end=eod; • if eod then call symput("totm",trim(left(_n_))); • run; • %let counter=0;

  9. Splitting data (cont.) • %do m=1 %to &totm %by &magicNumber; • %let counter=%eval(&counter +1); • %let s=&m ; • %let e=%eval(&s + &magicNumber -1); • %if &m = %eval((&totm /&magicNumber )* &magicNumber +1) %then %do; • %let stringcomp&counter =%quote( &s <= _n_ <= &totm ) ; • %end; • %else %do; • %let stringcomp&counter =%quote( &s <= _n_ <= &e ); • %end; • %put &&stringcomp&counter ; • %end; • %do k=1 %to &counter; • data out&j..mapc&j.p&k ; • format markname $12.; length markname $12; • set ind&j..mapc&j ; • markname=compress(SNP); • if &&stringcomp&k then output; run; • data _null_; • set out&j..mapc&j.p&k end=eod; • call symput("m"||trim(left(_n_)),trim(left(markname))); • if eod then call symput("ptotm",trim(left(_n_))); run; marknamechrom pos M1 1 10001 M2 1 10011 M3 1 10015 M4 1 10021 M5 2 20001 mapc&j

  10. Subject M1 M2 M3 M4… Mn 1 1/1 2/4 2/2 2/2 … 1/1 2 1/3 2/2 2/4 1/1 … 1/1 3 3/3 4/4 2/4 2/2 … 1/1 4 1/3 2/4 4/4 1/1 … 1/1 … Splitting data (cont.) • data out&j..mlgenoc&j.p&k ; • set mlgenoc&j (keep=subject %do ii=1 %to &ptotm; &&m&ii %end;) ; • run; • data out&j..genefreqc&j.p&k ; • set genefreqc&j ; • %do ii=1 %to &ptotm ; • if markname ="&&m&ii" then output; • %end; • run; • %end; %* k counter loop; • %end; %* j chromosome loop; • %mend split; • %split mlgenoc&j genefreqc&j marknamealele percent M1 1 10.0 M1 3 90.0 M2 2 25.0 M2 4 75.0 M3 2 0.10 M3 4 99.9 M4 1 0.10 M4 2 99.9

  11. Run the main program • *------------------------------------------------------------; • * program: parallel mixed.interface.sas ; • *------------------------------------------------------------; • * Purpose: run mixed model ; • * ; • * By: Aldi Kraja ; • * February 17, 2008 ; • * ; • ; • *------------------------------------------------------------; • %macro parallel(v=, • dsplit=, • pheno=, • type=, • rotation=, • subject=, • pedid=, • fid=, • mid=, • sex=, • dirout=, • genlabel=, • markname=); • data _null_; • file "./rmscript.sh"; • put " cd &dirout "; • put " find . -name '*mixed*sas7bdat' | xargs /bin/rm -f "; • put " find . -name '*freq*.sas7bdat' | xargs /bin/rm -f "; • put " cd /dsgmnt/dsg200/genetics/users/mydir/split/c&v./"; • run; • %sysexecchmod +x ./rmscript.sh ; • %sysexec ./rmscript.sh ; • %do y=1 %to &dsplit ; • %sysexecrm -f ./test&y..sas ;

  12. Run the main program (cont.) • %*---------------------------------; • %* let start to program in parallel; • %*---------------------------------; • data _null_; • file "./test&y..sas" lrecl=36000; • put "options nofmterr; options nomprintnomlogicnosymbolgen; "; • put '%include "/dsgmnt/dsg200/genetics/users/mydir1/macroutil.sas"; '; • put '%include "/dsgmnt/dsg200//genetics/users/mydir2/mixed.sas"; '; • put " libname ph22 '/dsgmnt/dsg200/genetics/data/mydir/pheno'; "; • put " libname trip '/dsgmnt/dsg200/genetics/data/mydir/geno/'; "; • put " libnamesnpc&v '/dsgmnt/dsg200/genetics/data/mydir/split/c&v'; "; • put " *------------- map ------------------ ; "; • put " data cmap&v.p&y ; "; • put " format &markname $12.; length &markname $12 ; "; • put " set snpc&v..mapc&v.p&y (where=( chrom=&v )); "; • put " run; "; • put "%sortit(cmap&v.p&y,cmap&v.p&y, &markname,nodupkey) "; • put " *------------- locdes ------------------ ; "; • put "%sortit(cmap&v.p&y,clocdes&v.p&y,&markname,nodupkey) "; • put " data cmap&v.p&y ; "; • put " merge cmap&v.p&y (in=in1) clocdes&v.p&y (in=in2); "; • put " by &markname; if in1 and in2; "; • put " run; "; • put " %sortit(cmap&v.p&y ,cmap&v.p&y ,&markname) "; • put " *------------- genefreq ------------------ ; "; • put " data genefreq&v.p&y ; "; • put " format &markname $12.; length &markname $12 ; "; • put " set snpc&v..genefreqc&v.p&y ; "; • put "run; "; • put " %sortit(genefreq&v.p&y,genefreq&v.p&y,&markname) "; • put " data genefreq&v.p&y; "; • put " merge genefreq&v.p&y (in=in1) clocdes&v.p&y (in=in2 ) cmap&v.p&y ; "; • put " by &markname; if in1 and in2; run; "; • put " *------------- ganon ------------------ ; "; • put " data aganon&v.p&y ; set snpc&v..mlgenoc&v.p&y ; run; "; • put " *------------- triplet ------------------ ; "; • put " data trip; set trip.phenos (keep=&pedid &subject &fid &mid &sex ); run; "; • put " %sortit(trip,trip,&subject ,nodupkey ) ";

  13. put " *-------------phenodata------------------ ; • put " %sortit(ph22.fanomiss,phenodata (keep=&subject &pheno ),&subject) "; • put " data phenodata; merge phenodata (in=in1) trip (in=in2); by &subject; "; • put " if in1 and in2; "; • put " if &subject =. then do; "; • put " put '-------------------------------------------------------------------'; "; • put " put 'This is a note for showing that you have missing &subject in the data';"; • put " put '-------------------------------------------------------------------'; "; • put " put _all_ ; abort abend; "; • put " end; "; • put " run; "; • put " %sortit(phenodata,phenodata,&subject ) "; • put " %sortit(aganon&v.p&y,aganon&v.p&y,&subject ) "; • put "data aganon&v.p&y ; merge aganon&v.p&y (in=in1) phenodata (in=in2); by &subject ;"; • put " if in1 ; "; • put " run; "; • put " proc datasets lib=work; delete phenodata; run; "; • put " *------------- finally set up the macro ------------------ ; "; • put " %alleled( "; • put " datain=aganon&v.p&y, “ ; • put " clocdes=clocdes&v.p&y, "; • put " cmap=cmap&v.p&y, "; • put " dist=position, "; • put " cgenefreq=genefreq&v.p&y, "; • put " triplet=trip, "; • put " chrom=chrom, "; • put " marshvar=cmap, "; • put " markvar=markname, "; • put " pedid=&pedid, "; • put " subject=&subject, "; • put " fid=&fid, "; • put " mid=&mid, "; • put " sex=&sex, "; • put " qualaff=, "; • put " phenodata=aganon&v.p&y, "; • put " pheno=&pheno, "; • put " qualtrait=, "; • put " qualcovars=&sex, "; • put " quantcovars=, "; • put " correctForMean=NO, "; • put " bymark=500, "; • put " programd=MIXED, "; • put " model=a, "; • put " time=, "; • put " genlabel=&genlabel&y, "; • put " barplot=0, ";

  14. Run the main program (cont.) • put " whohasmarkers=NO) "; • run; • %end ; %* end of y loop for each split dataset ; • data _null_; • file "./mixedscript.sh "; • %do j=1 %to &dsplit; • put "bsubnohupsas -memsize 1G ./test&j..sas "; • put "sleep 5 "; • %end; • put "echo Finished the submission for chrom: &v a total of &dsplit jobs"; • run; • X 'chmod +x ./mixedscript.sh '; • %sysexec ./mixedscript.sh ; • %mend parallel; • %parallel(v=22, • dsplit=170, • pheno=Factor1mleod, • type=allf1, • rotation=varimax, • subject=subject, • pedid=pedid, • fid=dadsubj, • mid=momsubj, • sex=sex, • dirout=/dsgmnt/dsg200/genetics/data/mydir/results/w/c&v./&type./&rot./, • genlabel=f1fhs, • markname=markname)

  15. Collect results • *------------------------------------------------------------; • * summarize.sas ; • * Purpose: summarize the results of Mixed model ; • *------------------------------------------------------------; • %global nobs; • %let nobs=0; • %include "/dsgmnt/dsg200/genetics/users/mydir/macroutil.sas"; • %let study=mystudy; • libnameinncbi "/dsgmnt/dsg200/genetics/data/generaldir/annot/"; • %macro test(totloop=968 1105 872 816 841 912 717 738 611 693 651 625 521 420 362 357 293 385 186 318 170 170, • chroms=1, • chrome=22, • fact=1, • dirout=/dsgmnt/dsg200/genetics/data/mydir/results/final&study./f&fact./); • libnameout&study "&dirout"; • %let count=1; • %let tloop1=%scan(&totloop,&count); • %do %while(%scan(&totloop,&count) ^= ); • %let count=%eval(&count+1); • %let tloop&count=%scan(&totloop,&count); • %end; • %let count=%eval(&count-1); • %do k=&chroms %to &chrome; • %* delete what you will append to; • proc datasets lib=out&study ; • delete mixed&study.c&k &study.c&k ; run; • libnamefreq&k "/dsgmnt/dsg200/genetics/data/mydir/split/c&k"; • %do f=&fact %to &fact; • libnamefhs&k.f&f "/dsgmnt/dsg200/genetics/data/mydir/results/w/c&k./allf&f./var"; • %do j=1 %to &&tloop&k ; • %isempty(data=fhs&k.f&f..amixedf&f.&study&j)

  16. Collect results (cont.) • %if &nobs =0 %then %do; %*--------------work on empty set check--------------; • data a; factor=&f ; notempty=.; empty=&j ;output; run; • %if &j=1 %then %do; • data out&study..&study.c&k ; • set a; run; • proc datasets lib=work; delete a; run; • %end; %* loop when j is 1; • %else %do; • data out&study..&study.c&k ; • set out&study..&study.c&k a; run; • proc datasets lib=work; delete a; run; • %end; %* end of loop j is not 1; • %end; %*-----------------------------; • %else %do; %* the nobs is not 0 =======================; • data a; • factor=&f ; notempty=&j; empty=. ;output; run; • %*--------------------------------------------; • %* capture the frequencies present in the data; • %*--------------------------------------------; • data b (drop=markname); • format markn $12.; length markn $12; • set fhs&k.f&f..ameanfreqpermarkf&f.&study&j ; • markn=substr(markname,2); markn=compress(markn||"XXXXXXXXX"); run; • data b; set b (rename=(markn=markname)) ; run; • %sortit(b,b ,markname model) • proc transpose data=b out=c (rename=(COL1=AA COL2=AB COL3=BB)); • by markname; var freq; run; • %*-----------work on freq ********; • data p; • set freq&k..genefreqc&k.p&j ; run; • proc sort data=p; by markname percent allele; run; • proc transpose data=p out=q (rename=(COL1=a1 COL2=a2)); • by markname; var allele; run; • proc transpose data=p out=r (rename=(COL1=MAF COL2=MOA)); • by markname; var percent; run; • proc sort data=q; by markname; run; • proc sort data=r; by markname; run; • data s (drop=_NAME_); • merge q (in=in1) r (in=in2); by markname; if in1 and in2; run; • %sortit(s,s,markname) • proc datasets lib=work; delete p q r ; run; • %*--------------------------------------------; • %* work on the mixed model output ; • %*--------------------------------------------; • %if &j=1 %then %do; data out&study..&study.c&k ; set a; run; • %sortit(fhs&k.f&f..amixedf&f.&study&j,d,markname) • %sortit(fhs&k.f&f..ameanfreqpermarkf&f.&study&j,b,markname model)

  17. Collect results (cont.) • %sortit(c,c(drop=_NAME_),markname) • data d; • merge c (in=in1) d (in=in2) s; • by markname; • if in1 and in2; • factor=&f ; • run; • data out&study..mixed&study.c&k (drop=model analysis _LABEL_); set d ;run; • proc datasets lib=work; delete a c d s; run; • %end; %* loop when j is 1; • %else %do; • data out&study..&study.c&k ; • set out&study..&study.c&k a; run; • %sortit(fhs&k.f&f..amixedf&f.&study&j,d,markname) • %sortit(c,c(drop=_NAME_),markname) • data d; • merge c (in=in1) d (in=in2) s; • by markname; • if in1 or in2; run; • data out&study..mixed&study.c&k (drop=model analysis _LABEL_); set out&study..mixed&study.c&k d ; • factor=&f ; • run; • proc datasets lib=work; delete a c d s; run; • %end; %* end of loop j is not 1; • %end; %* end of condition the nobs is not 0; • %end; %* end of j loop (splits of the same chromosome) ; • %end; %* end of f loop (factors) ; • %*--------------------------------------------------------------; • %* finally annotate based on the newest version of NCBI dbSNP ; • %*--------------------------------------------------------------; • %sortit(inncbi.ncbisnpb128_c&k,annot&k,markname,nodupkey) • %sortit(out&study..mixed&study.c&k,out&study..mixed&study.c&k,markname) • data out&study..mixed&study.c&k ; • merge out&study..mixed&study.c&k (in=in1) annot&k (in=in2); • by markname ; if in1; run; • proc datasets lib=work; delete annot&k ; run; • %end; %* end of k loop (chromosome) ; • %mend test; • %test

  18. Finally when we are done: • Remove all split data • Do NOT remove the original source data • gzip unused results (you can gunzip results at the time you need them again) • Multi-threaded systems and parallel programming are common tools in modern software applications, and are used to enhance the scalability and performance of large jobs

  19. In conclusion • The statistical tasks included but were not limited to: • 􀁹 Drafting your analysis objectives • 􀁹 Creating specifications for analysis datasets • 􀁹 Creating specifications for tables, figures, and listings • 􀁹 Identifying first look analyses • 􀁹 Performing data checking • 􀁹 Parallel programming analysis datasets • 􀁹 Checking data results and parallel programming tables, figures, and listings • 􀁹 Removal of the un-necessary data

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