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Aldi Kraja October 17, 2008.  A. where is the location of my work?   /dsguser/mylogin.

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Presentation on theme: "Aldi Kraja October 17, 2008.  A. where is the location of my work?   /dsguser/mylogin."— Presentation transcript:

1 Aldi Kraja October 17, 2008

2  A. where is the location of my work?  mylogin@dsgcluster.dsg.wustl.edu 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  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  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: bsub sas pgm.sas  proc print data=mydata (obs=10); title “my data”; run;  proc contents data=mydata; title “contents” run;

5  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 your tmp data?  Can I open your data in a sas viewer?  Can I delete your program run?

6  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 markname chrom pos M1 1 10001 M2 1 10011 M3 1 10015 M4 1 10021 M5 220001 markname chrom pos M1 1 10001 M2 1 10011 M3 1 10015 M4 1 10021 M5 220001 markname M1 M2 M3 M4 markname M1 M2 M3 M4 markname alele percent M1 110.0 M1 390.0 M2225.0 M2475.0 M320.10 M3499.9 M410.10 M4299.9 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 … 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 … map1 locdes1 genefreq1 ganon1

8  *--------------------------------------;  * split.sas ;  * split data based on a decided number ;  * magicNumber=, ;  * By: Aldi Kraja ;  * October 3, 2008 ;  *--------------------------------------;  options mprint mlogic symbolgen;  %macro split(magicNumber=200,  chroms=1,  chrome=1);  %do j=&chroms %to &chrome ;  libname ind&j "/dsgmnt/dsg200/genetics/data/mydata/impute/c&j";  libname out&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;  %sysexec chmod +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  %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; markname chrom pos M1 1 10001 M2 1 10011 M3 1 10015 M4 1 10021 M5 220001 markname chrom pos M1 1 10001 M2 1 10011 M3 1 10015 M4 1 10021 M5 220001 mapc&j

10   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 markname alele percent M1 110.0 M1 390.0 M2225.0 M2475.0 M320.10 M3499.9 M410.10 M4299.9 genefreqc&j 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 … 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 … mlgenoc&j

11  *------------------------------------------------------------;  * 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;  %sysexec chmod +x./rmscript.sh ;  %sysexec./rmscript.sh ;   %do y=1 %to &dsplit ;  %sysexec rm -f./test&y..sas ;

12  %*---------------------------------;  %* let start to program in parallel;  %*---------------------------------;  data _null_;  file "./test&y..sas" lrecl=36000;  put "options nofmterr; options nomprint nomlogic nosymbolgen; ";  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 " libname snpc&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  put " whohasmarkers=NO) ";  run;   %end ; %* end of y loop for each split dataset ;  data _null_;  file "./mixedscript.sh ";  %do j=1 %to &dsplit;  put "bsub nohup sas -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  *------------------------------------------------------------;  * 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;  libname inncbi "/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./);  libname out&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;  libname freq&k "/dsgmnt/dsg200/genetics/data/mydir/split/c&k";  %do f=&fact %to &fact;  libname fhs&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  %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   %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  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  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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