<\body> Bioconductor is a collection of libraries to help analyze genomic data. Currently, it is a fairly eclectic collection. It seems most complete in tools to help analyze affymatrix arrays. Information about bioconductor is at . click on ``Release 1.3 Packages'' to see what packages are available. Currently the following look interesting: <\itemize-dot> affy - analyzing affymatrix arrays marray - analyzing cDNA arrays SNPtools - download SNP data - how complete? Under ``metadata'' you'll see packages that contain data - GO data, location data, etc. In order to use metadata, we first have to load the right library: <\input| >> library(hgu95av2);library(annotate) <\output> Loading required package: Biobase\ \; Welcome to Bioconductor\ \ \ \ \ \ \ \ \ \ Vignettes contain introductory material. \ To view,\ \ \ \ \ \ \ \ \ \ simply type: openVignette()\ \ \ \ \ \ \ \ \ \ For details on reading vignettes, see \ \ \ \ \ \ \ \ \ the openVignette help page. \; \; Synching your local package management information ... \; \; \ \ \ \ \ \ \ \ Note: reposTools can not access /usr/lib/R/site-library. \ \ \ \ \ \ \ \ This will not affect your R session unless you wish\ \ \ \ \ \ \ \ \ to install/update/remove packages from this directory \ \; \; \ \ \ \ \ \ \ \ Note: reposTools can not access /usr/lib/R/library. \ \ \ \ \ \ \ \ This will not affect your R session unless you wish\ \ \ \ \ \ \ \ \ to install/update/remove packages from this directory \ <\input| >> \; > This library contains annotation for the probes on the affymatrix chip u95A. For annotation libraries, usually a call to a function with the name of the library shows the data contained in the library. <\input| >> hgu95av2() <\output> \; \; Quality control information for \ hgu95av2\ Date built: \ Wed Jan 14 22:11:00 2004 \ Number of probes: 12625\ Probe number missmatch: None\ Probe missmatch: None\ Mappings found for probe based rda files:\ \ \ \ \ \ \ \ \ \ hgu95av2ACCNUM found 12625 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2CHRLOC found 11426 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2CHR found 12183 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2ENZYME found 1619 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2GENENAME found 12208 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2GO found 9437 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2GRIF found 6554 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2HGID found 11337 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2LOCUSID found 12280 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2MAP found 12145 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2NM found 11400 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2NP found 11400 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2OMIM found 9528 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2PATH found 2234 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2PMID found 12033 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2SUMFUNC found 656 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2SYMBOL found 12208 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2UNIGENE found 11940 of 12625\ Mappings found for non-probe based rda files: \ \ \ \ \ \ \ \ \ hgu95av2ENZYME2PROBE found 565 \ \ \ \ \ \ \ \ \ hgu95av2GO2ALLPROBES found 4405 \ \ \ \ \ \ \ \ \ hgu95av2GO2PROBE found 3212 \ \ \ \ \ \ \ \ \ hgu95av2PATH2PROBE found 122 \ \ \ \ \ \ \ \ \ hgu95av2PMID2PROBE found 48146\ <\input| >> \; > These define environments. Environments are areas in which variables are stored: <\input| >> ls() <\output> [1] "last.warning" <\input| >> a=1:100 <\input| >> b="hello" <\input| >> ls() <\output> [1] "a" \ \ \ \ \ \ \ \ \ \ \ "b" \ \ \ \ \ \ \ \ \ \ \ "last.warning" <\input| >> env1=new.env() <\input| >> ls(env=env1) <\output> character(0) <\input| >> with(env1, a\-4 ) <\input| >> a <\output> \ \ [1] \ \ 1 \ \ 2 \ \ 3 \ \ 4 \ \ 5 \ \ 6 \ \ 7 \ \ 8 \ \ 9 \ 10 \ 11 \ 12 \ 13 \ 14 \ 15 \ 16 \ 17 \ 18 \ [19] \ 19 \ 20 \ 21 \ 22 \ 23 \ 24 \ 25 \ 26 \ 27 \ 28 \ 29 \ 30 \ 31 \ 32 \ 33 \ 34 \ 35 \ 36 \ [37] \ 37 \ 38 \ 39 \ 40 \ 41 \ 42 \ 43 \ 44 \ 45 \ 46 \ 47 \ 48 \ 49 \ 50 \ 51 \ 52 \ 53 \ 54 \ [55] \ 55 \ 56 \ 57 \ 58 \ 59 \ 60 \ 61 \ 62 \ 63 \ 64 \ 65 \ 66 \ 67 \ 68 \ 69 \ 70 \ 71 \ 72 \ [73] \ 73 \ 74 \ 75 \ 76 \ 77 \ 78 \ 79 \ 80 \ 81 \ 82 \ 83 \ 84 \ 85 \ 86 \ 87 \ 88 \ 89 \ 90 \ [91] \ 91 \ 92 \ 93 \ 94 \ 95 \ 96 \ 97 \ 98 \ 99 100 <\input| >> with(env1, a) <\output> [1] 4 <\input| >> ls(env=env1) <\output> [1] "a" <\input| >> get("a",env1) <\output> [1] 4 <\input| >> \; > Back to the library: <\input| >> hgu95av2() <\output> \; \; Quality control information for \ hgu95av2\ Date built: \ Wed Jan 14 22:11:00 2004 \ Number of probes: 12625\ Probe number missmatch: None\ Probe missmatch: None\ Mappings found for probe based rda files:\ \ \ \ \ \ \ \ \ \ hgu95av2ACCNUM found 12625 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2CHRLOC found 11426 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2CHR found 12183 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2ENZYME found 1619 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2GENENAME found 12208 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2GO found 9437 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2GRIF found 6554 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2HGID found 11337 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2LOCUSID found 12280 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2MAP found 12145 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2NM found 11400 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2NP found 11400 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2OMIM found 9528 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2PATH found 2234 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2PMID found 12033 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2SUMFUNC found 656 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2SYMBOL found 12208 of 12625 \ \ \ \ \ \ \ \ \ hgu95av2UNIGENE found 11940 of 12625\ Mappings found for non-probe based rda files: \ \ \ \ \ \ \ \ \ hgu95av2ENZYME2PROBE found 565 \ \ \ \ \ \ \ \ \ hgu95av2GO2ALLPROBES found 4405 \ \ \ \ \ \ \ \ \ hgu95av2GO2PROBE found 3212 \ \ \ \ \ \ \ \ \ hgu95av2PATH2PROBE found 122 \ \ \ \ \ \ \ \ \ hgu95av2PMID2PROBE found 48146\ <\input| >> x=ls(env=hgu95av2CHRLOC) <\input| >> length(x) <\output> [1] 12625 <\input| >> x[1] <\output> [1] "1000_at" <\input| >> get(x[2],env=hgu95av2CHRLOC) <\output> \ \ \ \ \ \ \ 1\ 43179957\ <\input| >> multiget(x[1:3],env=hgu95av2CHRLOC) <\output> $"1000_at" \ \ \ \ \ \ \ 16\ -30162733\ \; $"1001_at" \ \ \ \ \ \ \ 1\ 43179957\ \; $"1002_f_at" \ \ \ \ \ \ 10\ 96187049\ <\input| >> ?hgu95av2CHRLOC <\output> hgu95av2CHRLOC \ \ \ \ \ \ \ \ \ \ package:hgu95av2 \ \ \ \ \ \ \ \ \ \ R Documentation \; An annotation data file for CHRLOC in the hgu95av2 package \; Description: \; \ \ \ \ \ This is an R environment (hash table like) object containing key \ \ \ \ \ and value pairs for the mappings between probe identifiers (key) \ \ \ \ \ and transcription startting \ positions of genes on chromosomes \ \ \ \ \ (value). Keys can be accessed using ls(name of the environment) \ \ \ \ \ and values using get(key, name of the environment) or \ \ \ \ \ multiget(keys, name of the environment). Values are named vectors \ \ \ \ \ of length 1 or more depending on whether a given probe id can be \ \ \ \ \ mapped to a single or multiple chromosomes. The names give the \ \ \ \ \ chromosome number of concern. NA is assigned to probe identifiers \ \ \ \ \ that can not be mapped to any chromosomal location data at this \ \ \ \ \ time. Starting positions for genes on the antisense strand have a \ \ \ \ \ leading "-" sign (e. g. -1234567). The starting positions for both \ \ \ \ \ the sense and antisense strand are number of base pairs measured \ \ \ \ \ from the p (5' end of the sense strand) to q (3' end of the sense \ \ \ \ \ strand) arms. \ When a gene can not be placed on a chromosome with \ \ \ \ \ confidence, "random" is appended to the end of the name for a \ \ \ \ \ chromosomal location value. Mappings were obtained using \ \ \ \ \ refLint.txt.gz and refGene.txt.gz file from Golden Path (\URL: \ \ \ \ \ http://www.genome.ucsc.edu/goldenPath/\) from the latest release. \; Details: \; \ \ \ \ \ Mappings were based on data provided by Human Genome Project \; \ \ \ \ \ Source data built: Human Genome Project built: hg16.\URL: \ \ \ \ \ http://www.genome.ucsc.edu/goldenPath/hg16/database/\. Package \ \ \ \ \ built: Wed Jan 14 22:11:01 2004 \; References: \; \ \ \ \ \ \URL: http://www.genome.ucsc.edu/goldenPath/hg16/database/\ \; Examples: \; \ \ \ \ \ \ \ \ \ \ \ \ \ require("annotate") \|\| stop("annotate unavailable") \ \ \ \ \ \ \ \ \ \ \ \ \ xx \- ls(env = hgu95av2CHRLOC) \ \ \ \ \ \ \ \ \ \ \ \ \ if(length(xx) \ 0){ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ # Using get for value of the first key \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ get(xx[1], hgu95av2CHRLOC ) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ #Using multiget for a few keys \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ if(length(xx) \= 3){ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ multiget(xx[1:3], hgu95av2CHRLOC ) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ #Using lookUp of annotate(\ 1.3.4) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ lookUp(xx[1:3],"hgu95av2","CHRLOC") \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ } \ \ \ \ \ \ \ \ \ \ \ \ \ } <\input| >> \; > We see that the name is the chomosome, and the value is the location. The location is negative if the gene is on the antisense strand. Location is measured on the sense strand. Let us look at the GO annotation: <\input| >> x=ls(env=hgu95av2GO) <\input| >> length(x) <\output> [1] 12625 <\input| >> x[1] <\output> [1] "1000_at" <\input| >> x[2] <\output> [1] "1001_at" <\input| >> get(x[1],env=hgu95av2GO) <\output> [1] NA <\input| >> get(x[2],env=hgu95av2GO) <\output> $"GO:0004714" $"GO:0004714"$GOID [1] "GO:0004714" \; $"GO:0004714"$Evidence [1] "TAS" \; $"GO:0004714"$Ontology [1] "MF" \; \; $"GO:0005524" $"GO:0005524"$GOID [1] "GO:0005524" \; $"GO:0005524"$Evidence [1] "IEA" \; $"GO:0005524"$Ontology [1] "MF" \; \; $"GO:0004872" $"GO:0004872"$GOID [1] "GO:0004872" \; $"GO:0004872"$Evidence [1] "IEA" \; $"GO:0004872"$Ontology [1] "MF" \; \; $"GO:0007498" $"GO:0007498"$GOID [1] "GO:0007498" \; $"GO:0007498"$Evidence [1] "TAS" \; $"GO:0007498"$Ontology [1] "BP" \; \; $"GO:0006468" $"GO:0006468"$GOID [1] "GO:0006468" \; $"GO:0006468"$Evidence [1] "IEA" \; $"GO:0006468"$Ontology [1] "BP" \; \; $"GO:0007165" $"GO:0007165"$GOID [1] "GO:0007165" \; $"GO:0007165"$Evidence [1] "TAS" \; $"GO:0007165"$Ontology [1] "BP" \; \; $"GO:0005887" $"GO:0005887"$GOID [1] "GO:0005887" \; $"GO:0005887"$Evidence [1] "TAS" \; $"GO:0005887"$Ontology [1] "CC" \; \; $"GO:0016740" $"GO:0016740"$GOID [1] "GO:0016740" \; $"GO:0016740"$Evidence [1] "IEA" \; $"GO:0016740"$Ontology [1] "MF" <\input| >> a=get(x[2],env=hgu95av2GO) <\input| >> length(a) <\output> [1] 8 <\input| >> \; > So, probe 2 seems to belong to 8 GO groups. With the library GO we can see what they are: <\input| >> library(GO) <\input| >> GO() <\output> \; \; Quality control information for \ GO\ Date built: \ Tue Jan 13 13:48:19 2004 \ \ \ \ \ Mappings found for non-probe based rda files: \ \ \ \ \ \ \ \ \ GOBPCHILDREN found 3751 \ \ \ \ \ \ \ \ \ GOBPPARENTS found 8026 \ \ \ \ \ \ \ \ \ GOCCCHILDREN found 452 \ \ \ \ \ \ \ \ \ GOCCPARENTS found 1363 \ \ \ \ \ \ \ \ \ GOGO2LL found 5785 \ \ \ \ \ \ \ \ \ GOLL2GO found 36295 \ \ \ \ \ \ \ \ \ GOMFCHILDREN found 1376 \ \ \ \ \ \ \ \ \ GOMFPARENTS found 7268 \ \ \ \ \ \ \ \ \ GOTERM found 16660\ <\input| >> a[[1]] <\output> $GOID [1] "GO:0004714" \; $Evidence [1] "TAS" \; $Ontology [1] "MF" <\input| >> a[[1]]$GOID <\output> [1] "GO:0004714" <\input| >> get(a[[1]]$GOID, env=GOTERM) <\output> \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ MF\ "transmembrane receptor protein tyrosine kinase activity"\ <\input| >> \; > What other probes belong to this group? <\input| >> ?hgu95av2GO2ALLPROBES <\output> hgu95av2GO2ALLPROBES \ \ \ \ \ \ \ package:hgu95av2 \ \ \ \ \ \ \ R Documentation \; An annotation data file for GO2ALLPROBES in the hgu95av2 package \; Description: \; \ \ \ \ \ This is an R environment (hash table like) object containing key \ \ \ \ \ and value pairs for the mappings between Gene Ontology ids (key) \ \ \ \ \ and probe identifiers (value) associated with a given GO id and \ \ \ \ \ all the offspring of that GO id. Keys can be accessed using \ \ \ \ \ ls(name of the environment) and values using get(key, name of the \ \ \ \ \ environment) or multiget(keys, name of the environment). Values \ \ \ \ \ may be vectors of length 1 or greater depending on whether a given \ \ \ \ \ GO id can be mapped to only one or more probe identifiers. Names \ \ \ \ \ for values are the evidence codes for the GO ids (if evidence code \ \ \ \ \ was provided by source data). The evidence codes in use include: \ \; IMP inferred from mutant phenotype \ \; IGI inferred from genetic interaction \; IPI inferred from physical interaction \ \; ISS inferred from sequence similarity \ \; IDA inferred from direct assay \ \; IEP inferred from expression pattern \ \; IEA inferred from electronic annotation \ \; TAS traceable author statement \ \; NAS non-traceable author statement \ \; ND no biological data available \ \; IC inferred by curator GO ids can not be mapped to any probe identifier \ \ \ \ \ is assigned a value of NA. Mappings between Gene Ontology ids an \ \ \ \ \ Gene Ontology terms and other information are available in a \ \ \ \ \ separate data package named GO. \; Details: \; \ \ \ \ \ Mappings were based on data provided by LocusLink \; \ \ \ \ \ Source data built: LocusLink built: January 13, 2004.\URL: \ \ \ \ \ ftp://ftp.ncbi.nih.gov/refseq/LocusLink/LL_tmpl.gz\. Package \ \ \ \ \ built: Wed Jan 14 22:11:04 2004 \; References: \; \ \ \ \ \ \URL: ftp://ftp.ncbi.nih.gov/refseq/LocusLink/LL_tmpl.gz\ \; Examples: \; \ \ \ \ \ \ \ \ \ \ \ \ \ require("annotate") \|\| stop("annotate unavailable") \ \ \ \ \ \ \ \ \ \ \ \ \ xx \- ls(env = hgu95av2GO2ALLPROBES) \ \ \ \ \ \ \ \ \ \ \ \ \ if(length(xx) \ 0){ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ # Using get for value of the first key \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ get(xx[1], hgu95av2GO2ALLPROBES ) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ #Using multiget for a few keys \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ if(length(xx) \= 3){ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ multiget(xx[1:3], hgu95av2GO2ALLPROBES ) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ #Using lookUp of annotate(\ 1.3.4) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ lookUp(xx[1:3],"hgu95av2","GO2ALLPROBES") \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ } \ \ \ \ \ \ \ \ \ \ \ \ \ } <\input| >> get(a[[1]]$GOID, env=hgu95av2GO2ALLPROBES) <\output> \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA\ \ \ "1108_s_at" \ \ \ \ "1234_at" \ \ \ \ "1606_at" \ \ \ \ "1485_at" \ \ \ "34331_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ "39930_at" \ \ \ "34573_at" \ \ \ "898_s_at" \ \ \ "39947_at" \ \ \ \ \ "469_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ "1604_at" \ \ "1605_g_at" \ \ \ "34564_at" \ \ "2088_s_at" \ \ \ "41678_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ \ NR \ \ \ \ \ \ \ \ \ \ \ NR \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ \ "902_at" \ \ \ \ "1537_at" \ \ \ "37327_at" \ \ \ \ "1585_at" \ \ "1723_g_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ "1742_at" \ \ "2089_s_at" \ \ \ "32787_at" \ \ \ "33638_at" \ "33639_g_at"\ \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA\ \ \ \ \ "1727_at" \ \ \ \ "1335_at" \ \ \ "31335_at" \ \ \ "34718_at" \ \ "1572_s_at"\ \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E\ \ \ \ "33162_at" \ \ \ \ "2056_at" \ \ "2057_g_at" \ \ \ "36168_at" \ \ \ "424_s_at"\ \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ "31805_at" \ \ \ \ \ "637_at" \ \ "1291_s_at" \ \ \ \ "1608_at" \ \ "1609_g_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ "1812_s_at" \ \ \ "35684_at" \ \ \ \ "1335_at" \ \ \ "31335_at" \ \ \ "34718_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ \ \ E\ "160027_s_at" \ \ \ "40936_at" \ \ "160022_at" \ \ \ \ "1317_at" \ \ "1802_s_at"\ \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ P \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E\ \ \ "1901_s_at" \ \ \ "33218_at" \ "36805_s_at" \ \ \ \ "1354_at" \ \ "1355_g_at"\ \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ \ \ E \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ "33182_at" \ \ \ "36042_at" \ "38280_s_at" \ \ "1771_s_at" \ \ \ "36993_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ "1731_at" \ \ "1968_g_at" \ \ \ \ "1987_at" \ \ \ \ "1988_at" \ \ \ "36157_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ "1761_at" \ \ "1545_g_at" \ \ \ \ "1567_at" \ \ \ \ "1963_at" \ \ "1964_g_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ \ "990_at" \ \ \ "991_g_at" \ \ \ \ "1065_at" \ \ \ "34583_at" \ \ \ \ "1954_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA\ \ \ \ "690_s_at" \ \ "1888_s_at" \ \ \ \ "1731_at" \ \ "1968_g_at" \ \ \ \ "1987_at"\ \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ \ \ P\ \ \ \ \ "1988_at" \ \ \ "36157_at" \ \ "1771_s_at" \ \ \ "36993_at" \ "33853_s_at"\ \ \ \ \ \ \ \ \ \ \ \ \ P \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ "41304_at" \ \ \ "36861_at" \ \ \ "38543_at" \ \ \ \ \ "451_at" \ \ "1233_s_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ "38433_at" \ \ "1007_s_at" \ \ \ "36643_at" \ \ "1432_s_at" \ \ \ "32888_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ "1626_at" \ \ \ "31777_at" \ \ \ \ \ "213_at" \ \ \ \ \ "212_at" \ \ \ \ "1319_at"\ \ \ \ \ \ \ \ \ \ \ \ \ P \ \ \ \ \ \ \ \ \ \ \ \ P \ \ \ \ \ \ \ \ \ \ \ \ P \ \ \ \ \ \ \ \ \ \ IEA \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ "38819_at" \ \ \ \ "1843_at" \ \ \ "40176_at" \ \ \ "593_s_at" \ \ \ "37756_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ \ "539_at" \ \ \ \ "1001_at" \ \ "1063_s_at" \ \ "2086_s_at" \ \ \ "35246_at"\ \ \ \ \ \ \ \ \ \ \ TAS \ \ \ \ \ \ \ \ \ \ TAS\ \ \ \ \ "1786_at" \ \ \ "40648_at"\ <\input| >> \; > Installing a library is very easy in R. You download the file, and do For the annotation libraries, or other ones, you might want to install a library locally. In that case you do the following You also want the following in the file ~/.Renviron: <\input|?] > R_LIBS="$HOME/lib/R:/usr/local/lib/R/site-library" \ <\input| >> library(affy) <\input| >> ?ReadAffy <\output> read.affybatch \ \ \ \ \ \ \ \ \ \ \ \ package:affy \ \ \ \ \ \ \ \ \ \ \ \ R Documentation \; Read CEL files into an AffyBatch \; Description: \; \ \ \ \ \ Read CEL files into an Affybatch \; Usage: \; \ \ \ \ \ read.affybatch(..., filenames = character(0), \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ phenoData = new("phenoData"), \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ description = NULL, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ notes = "", \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ compress = getOption("BioC")$affy$compress.cel, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ rm.mask = FALSE, rm.outliers = FALSE, rm.extra = FALSE, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ verbose = FALSE) \; \ \ \ \ \ ReadAffy(..., filenames=character(0), \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ widget=getOption("BioC")$affy$use.widgets, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ compress=getOption("BioC")$affy$compress.cel, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ celfile.path=getwd(), \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ sampleNames=NULL, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ phenoData=NULL, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ description=NULL, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ notes="", \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ rm.mask=FALSE, rm.outliers=FALSE, rm.extra=FALSE, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ verbose=FALSE) \; Arguments: \; \ \ \ \ \ ...: file names separated by comma. \; filenames: file names in a character vector. \; phenoData: a 'phenoData' object \; description: a 'MIAME' object\ \; \ \ \ notes: notes\ \; compress: are the CEL files compressed ? \; \ rm.mask: should the spots marked as 'MASKS' set to 'NA' ?\ \; rm.outliers: should the spots marked as 'OUTLIERS' set to 'NA'\ \; rm.extra: if 'TRUE', overrides what is in 'rm.mask' and 'rm.oultiers'\ \; \ verbose: verbosity flag\ \; \ \ widget: a logical specifying if widgets should be used. \; celfile.path: a character denoting the path 'ReadAffy' should look for \ \ \ \ \ \ \ \ \ \ cel files \; sampleNames: a character vector of sample names to be used in the \ \ \ \ \ \ \ \ \ \ 'AffyBatch' \; Details: \; \ \ \ \ \ 'ReadAffy' is a wrapper for 'read.affybatch' that permits the user \ \ \ \ \ to read in phenoData, MIAME information, and CEL files using \ \ \ \ \ widgets. One can also define files where to read phenoData and \ \ \ \ \ MIAME information. \; \ \ \ \ \ If the function is call with no arguments 'ReadAffy()' all the CEL \ \ \ \ \ files in the working directory are read and put into an \ \ \ \ \ 'AffyBatch'. However, the arguments give the user great \ \ \ \ \ flexibility. \; \ \ \ \ \ 'phenoData' is read using 'link[Biobase]{read.phenoData}'. If a \ \ \ \ \ character is given it tries to read the file with that name to \ \ \ \ \ obtain the phenoData object as described in \ \ \ \ \ 'link[Biobase]{read.phenoData}'. If left 'NULL' but 'widget=TRUE' \ \ \ \ \ then widgets are used. If left 'NULL' and 'widget=FALSE' then a \ \ \ \ \ default object is created. It will be a data frame with \ \ \ \ \ 'new("phenoData",pData=data.frame(x=1:length(CELfiles)),varLabels= \ \ \ \ \ list(x="arbitrary number"))' \; Value: \; \ \ \ \ \ An 'AffyBatch' object. \; Author(s): \; \ \ \ \ \ Ben Bolstad bolstad@stat.berkeley.edu (read.affybatch), Laurent \ \ \ \ \ Gautier, and Rafael A. Irizarry (ReadAffy) \; See Also: \; \ \ \ \ \ 'AffyBatch' \; Examples: <\input| >> A=ReadAffy(widget=T) <\input| >> A <\output> AffyBatch object size of arrays=640x640 features (6405 kb) cdf=HG_U95Av2 (12625 affyids) number of samples=2 \; number of genes=12625 annotation=hgu95av2 <\input| >> descript <\output> $pData \ \ \ \ \ \ subject species area a_h1a "1" \ \ \ \ "hu" \ \ \ "a"\ a_h1b "1" \ \ \ \ "hu" \ \ \ "b"\ \; $varLabels \ \ \ \ \ \ \ \ Description subject "" \ \ \ \ \ \ \ \ species "" \ \ \ \ \ \ \ \ area \ \ \ "" \ \ \ \ \ \ \ \ > First we want to assess the chips in general: <\input| >> image(A) <\input|Return\ to see next plot: >> \; <\input| >> boxplot(A) <\input| >> deg=AffyRNAdeg(A) <\input| >> plotAffyRNAdeg(deg) > Now we want to calculate expression values <\input| >> exA=expresso(A,widget=T) <\output> Loading required package: tkWidgets\ \; \; Attaching package 'DynDoc': \; \; \ \ \ \ \ \ \ \ The following object(s) are masked from package:base : \; \ \ \ \ \ \ \ \ \ vignette\ \; \; \; Loading required package: tcltk\ \; Error in setCorrections() : Aborted by user <\input| >> exA=rma(A) > Now we have expression values. We can save them: <\input| >> write.table(exA,file="test.txt") <\input| >> exprs2excel(exA,"test.csv") <\input| >> a=read.table("test.csv",sep=",",row.names=1,head=T) <\input| >> a[1:3,] <\output> \ \ \ \ \ \ \ \ \ \ X.mnt.alexandria.home.dirk.teaching.cel.a.h1a.CEL 1000_at \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 8.425136 1001_at \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 5.027576 1002_f_at \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 4.219405 \ \ \ \ \ \ \ \ \ \ X.mnt.alexandria.home.dirk.teaching.cel.a.h1b.CEL 1000_at \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 8.055096 1001_at \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 5.054206 1002_f_at \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 4.385835 <\input| >> \; <\input| >> \; > You can also convert the data to a table using the command. To analyze the expression data, the function iter is useful. It is similar to : <\input| >> iter( exA[1:3,], f=function(x) {x} )\ <\output> \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 1000_at \ 1001_at 1002_f_at /mnt/alexandria/home/dirk/teaching/cel/a_h1a.CEL 8.425136 5.027576 \ 4.219405 /mnt/alexandria/home/dirk/teaching/cel/a_h1b.CEL 8.055096 5.054206 \ 4.385835 <\input| >> \; > A more complicated version of iter allows us to subset the data: <\input| >> a=iter( exA[1:3,], "area", function(x,y) { print(x);print(y) } ) <\output> [1] a b Levels: a b /mnt/alexandria/home/dirk/teaching/cel/a_h1a.CEL\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 8.425136\ /mnt/alexandria/home/dirk/teaching/cel/a_h1b.CEL\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 8.055096\ [1] a b Levels: a b /mnt/alexandria/home/dirk/teaching/cel/a_h1a.CEL\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 5.027576\ /mnt/alexandria/home/dirk/teaching/cel/a_h1b.CEL\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 5.054206\ [1] a b Levels: a b /mnt/alexandria/home/dirk/teaching/cel/a_h1a.CEL\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 4.219405\ /mnt/alexandria/home/dirk/teaching/cel/a_h1b.CEL\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 4.385835 <\input| >> a=iter( exA[1:3,], "area", function(x,y) { a=split(y,x); sapply(a,mean) } ) <\input| >> a <\output> \ \ \ 1000_at \ 1001_at 1002_f_at a 8.425136 5.027576 \ 4.219405 b 8.055096 5.054206 \ 4.385835 <\input| >> a=iter( exA[1:3,], "species", function(x,y) { a=split(y,x); sapply(a,mean) } ) <\input| >> a <\output> \ \ 1000_at \ \ 1001_at 1002_f_at\ \ 8.240116 \ 5.040891 \ 4.302620\ <\input| >> a=iter( exA[1:3,], "species", function(x,y) { a=split(y,x); sapply(a,var) } ) <\input| >> a <\output> \ \ \ \ \ 1000_at \ \ \ \ \ 1001_at \ \ \ 1002_f_at\ 0.0684650913 0.0003545645 0.0138496078\ <\input| >> \; > \; <\input| >> x=1:10 <\input| >> y=sample(4,10,rep=T) <\input| >> split(x,y) <\output> $"1" [1] 1 4 6 \; $"2" [1] \ 2 \ 3 10 \; $"3" [1] 5 \; $"4" [1] 7 8 9 <\input| >> sapply(split(x,y),mean) <\output> \ \ \ \ \ \ \ 1 \ \ \ \ \ \ \ 2 \ \ \ \ \ \ \ 3 \ \ \ \ \ \ \ 4\ 3.666667 5.000000 5.000000 8.000000\ <\input| >> \; > <\input|?] > \; <\input|?] > \; <\input|?] > \; To calculate p-values we use: <\input| >> callA=mas5calls(A) <\output> \; > \; <\initial> <\collection> <\references> <\collection> |1>> |1>> |10>> |10>> <\auxiliary> <\collection> <\associate|toc> Lecture 9 - Bioconductor |Using metadata> |Installing a library> |Analyzing Affy data>