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library(Biobase)## Warning: package 'Biobase' was built under R version 4.0.3## Loading required package: BiocGenerics## Warning: package 'BiocGenerics' was built under R version 4.0.3## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.library(GEOquery)## Warning: package 'GEOquery' was built under R version 4.0.3## Setting options('download.file.method.GEOquery'='auto')## Setting options('GEOquery.inmemory.gpl'=FALSE)library(limma)## Warning: package 'limma' was built under R version 4.0.3##
## Attaching package: 'limma'## The following object is masked from 'package:BiocGenerics':
##
## plotMA======================================================
gset <- GEOquery::getGEO("GSE1397", GSEMatrix =TRUE) # importa datasets de GSE## Found 1 file(s)## GSE1397_series_matrix.txt.gz##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## ID_REF = col_character()
## )
## i Use `spec()` for the full column specifications.## File stored at:## C:\Users\acard\AppData\Local\Temp\Rtmp0sTNzl/GPL96.soft## Warning: 68 parsing failures.
## row col expected actual file
## 22216 SPOT_ID 1/0/T/F/TRUE/FALSE --Control literal data
## 22217 SPOT_ID 1/0/T/F/TRUE/FALSE --Control literal data
## 22218 SPOT_ID 1/0/T/F/TRUE/FALSE --Control literal data
## 22219 SPOT_ID 1/0/T/F/TRUE/FALSE --Control literal data
## 22220 SPOT_ID 1/0/T/F/TRUE/FALSE --Control literal data
## ..... ....... .................. ......... ............
## See problems(...) for more details.gset2 <- GEOquery::getGEO("GSE77962", GSEMatrix = TRUE)## Found 1 file(s)## GSE77962_series_matrix.txt.gz##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double()
## )
## i Use `spec()` for the full column specifications.## File stored at:## C:\Users\acard\AppData\Local\Temp\Rtmp0sTNzl/GPL11532.softgsm1 <- GEOquery::getGEO(GEO = "GSM2062466", GSEMatrix = TRUE) # importa un ensayo GSM## File stored at:## C:\Users\acard\AppData\Local\Temp\Rtmp0sTNzl/GSM2062466.soft======================================================
Primero sacamos a los objetos de la lista
gset <- gset[[1]]
gset2 <- gset2[[1]]Parar mostrar el contenido podemos usar la función show() o simplemente poniendo el nombre
gset## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22283 features, 28 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM22509 GSM22510 ... GSM22705 (28 total)
## varLabels: title geo_accession ... data_row_count (28 total)
## varMetadata: labelDescription
## featureData
## featureNames: 1007_s_at 1053_at ... AFFX-TrpnX-M_at (22283 total)
## fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 16420667
## Annotation: GPL96Tiene 28 muestras, en el microarreglo que tiene 22283 sondas. La plataforma es el modelo GPL96.
gset2## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 33297 features, 152 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM2062466 GSM2062467 ... GSM2062617 (152 total)
## varLabels: title geo_accession ... weight (kg):ch1 (52 total)
## varMetadata: labelDescription
## featureData
## featureNames: 7892501 7892502 ... 8180418 (33297 total)
## fvarLabels: ID GB_LIST ... category (12 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 30380678
## 27840413
## 32015415
## Annotation: GPL11532Este experimento tiene 152 muestras, con 33297 sondas. La plataforma del microarreglo es GLP11532
Nombre de art
# resumen del artículo
Biobase::abstract(gset2)## [1] "Background: Moderate weight loss can ameliorate adverse health effects associated with obesity, reflected by an improved adipose tissue (AT) gene expression profile. However, the effect of rate of weight loss on the AT transcriptome is unknown.\n\nObjective: We investigated the global AT gene expression profile before and after two different rates of weight loss that resulted in similar total weight loss, and after a subsequent weight stabilization period.\n\nDesign: In this randomized controlled trial, 25 male and 28 female individuals (BMI: 28-35 kg/m2) followed either a low-calorie diet (LCD; 1250 kcal/d) for 12 weeks or a very-low-calorie diet (VLCD; 500 kcal/d) for 5 weeks (weight loss (WL) period) and a subsequent weight stable (WS) period of four weeks. The WL period and WS period together is termed dietary intervention (DI) period. Abdominal subcutaneous AT biopsies were collected for microarray analysis, and gene expression changes were calculated for all three periods in the LCD group, VLCD group and between diets (ΔVLCD-ΔLCD).\n\nResults: Weight loss was similar between groups during the WL period (LCD: -8.1±0.5 kg, VLCD: -8.9±0.4 kg, difference p=0.25). Overall, more genes were significantly regulated and changes in gene expression were more extreme in the VLCD group compared to the LCD group. Gene sets related to mitochondrial function, adipogenesis and immunity/inflammation were more strongly upregulated on a VLCD compared to a LCD during the DI period (positive ΔVLCD-ΔLCD). Neuronal- and olfactory-related gene sets were decreased during the WL period and DI period in the VLCD group.\n\nConclusions: The rate of weight loss (LCD vs. VLCD), with similar total weight loss, strongly regulates AT gene expression. Increased mitochondrial function and adipogenesis on a VLCD compared to a LCD reflect potential beneficial diet-induced changes in AT, while differential neuronal and olfactory regulation suggest functions of these genes beyond the current paradigm."# estructura del objeto
Biobase::phenoData(gset2)## An object of class 'AnnotatedDataFrame'
## sampleNames: GSM2062466 GSM2062467 ... GSM2062617 (152 total)
## varLabels: title geo_accession ... weight (kg):ch1 (52 total)
## varMetadata: labelDescription# Plataforma
Biobase::annotation(gset2)## [1] "GPL11532"# Estructura de los datos (nombre de las sondas, y terminos relacionados a estos)
Biobase::featureData(gset2)## An object of class 'AnnotatedDataFrame'
## featureNames: 7892501 7892502 ... 8180418 (33297 total)
## varLabels: ID GB_LIST ... category (12 total)
## varMetadata: Column Description labelDescription# Busca si hay datos perdidos en la base de datos
Biobase::anyMissing(gset2)## [1] FALSE# tabla con los detalles de cada uno de los GSM del GSE
(Biobase::pData(Biobase::phenoData(gset2))[1:3,1:10])## title
## GSM2062466 Abdominal subcutaneous white adipose tissue, at study start, subject 1
## GSM2062467 Abdominal subcutaneous white adipose tissue, after weight loss period, subject 1
## GSM2062468 Abdominal subcutaneous white adipose tissue, after weight stable period, subject 1
## geo_accession status submission_date last_update_date
## GSM2062466 GSM2062466 Public on Feb 07 2017 Feb 16 2016 Feb 07 2017
## GSM2062467 GSM2062467 Public on Feb 07 2017 Feb 16 2016 Feb 07 2017
## GSM2062468 GSM2062468 Public on Feb 07 2017 Feb 16 2016 Feb 07 2017
## type channel_count
## GSM2062466 RNA 1
## GSM2062467 RNA 1
## GSM2062468 RNA 1
## source_name_ch1
## GSM2062466 abdominal subcutaneous white adipose tissue, VLCD, at study start
## GSM2062467 abdominal subcutaneous white adipose tissue, VLCD, after weight loss period
## GSM2062468 abdominal subcutaneous white adipose tissue, VLCD, after weight stable period
## organism_ch1 characteristics_ch1
## GSM2062466 Homo sapiens subject_id: 1
## GSM2062467 Homo sapiens subject_id: 1
## GSM2062468 Homo sapiens subject_id: 1# Nombre de la plataforma
Biobase::annotation(gset2)## [1] "GPL11532"# Busqueda de datos perdidos
Biobase::anyMissing(gset2)## [1] FALSE# Nombre de las columnas del objeto
Biobase::varLabels(gset2)## [1] "title" "geo_accession"
## [3] "status" "submission_date"
## [5] "last_update_date" "type"
## [7] "channel_count" "source_name_ch1"
## [9] "organism_ch1" "characteristics_ch1"
## [11] "characteristics_ch1.1" "characteristics_ch1.2"
## [13] "characteristics_ch1.3" "characteristics_ch1.4"
## [15] "characteristics_ch1.5" "characteristics_ch1.6"
## [17] "characteristics_ch1.7" "characteristics_ch1.8"
## [19] "characteristics_ch1.9" "treatment_protocol_ch1"
## [21] "growth_protocol_ch1" "molecule_ch1"
## [23] "extract_protocol_ch1" "label_ch1"
## [25] "label_protocol_ch1" "taxid_ch1"
## [27] "hyb_protocol" "scan_protocol"
## [29] "description" "data_processing"
## [31] "platform_id" "contact_name"
## [33] "contact_email" "contact_laboratory"
## [35] "contact_department" "contact_institute"
## [37] "contact_address" "contact_city"
## [39] "contact_zip/postal_code" "contact_country"
## [41] "supplementary_file" "data_row_count"
## [43] "age (yrs):ch1" "bmi (kg/m2):ch1"
## [45] "body fat %:ch1" "height (cm):ch1"
## [47] "Sex:ch1" "subject_id:ch1"
## [49] "time point:ch1" "tissue:ch1"
## [51] "treatment:ch1" "weight (kg):ch1"======================================================
Convertimos los encabezados en un vector de nombres
fvarLabels(gset2) <- make.names(fvarLabels(gset2))
fvarLabels(gset2)## [1] "ID" "GB_LIST" "SPOT_ID" "seqname"
## [5] "RANGE_GB" "RANGE_STRAND" "RANGE_START" "RANGE_STOP"
## [9] "total_probes" "gene_assignment" "mrna_assignment" "category"Primero generamos un data.frame con información util para entender los datos
tab.ref <- Biobase::pData(Biobase::phenoData(gset2))[,47:52]
tab.ref## Sex:ch1 subject_id:ch1 time point:ch1
## GSM2062466 female 1 at study start
## GSM2062467 female 1 after weight loss period
## GSM2062468 female 1 after weight stable period
## GSM2062469 female 2 at study start
## GSM2062470 female 2 after weight loss period
## GSM2062471 female 2 after weight stable period
## GSM2062472 male 3 at study start
## GSM2062473 male 3 after weight loss period
## GSM2062474 male 3 after weight stable period
## GSM2062475 female 4 at study start
## GSM2062476 female 4 after weight loss period
## GSM2062477 female 4 after weight stable period
## GSM2062478 male 5 at study start
## GSM2062479 male 5 after weight loss period
## GSM2062480 male 5 after weight stable period
## GSM2062481 female 6 at study start
## GSM2062482 female 6 after weight loss period
## GSM2062483 female 6 after weight stable period
## GSM2062484 female 8 at study start
## GSM2062485 female 8 after weight loss period
## GSM2062486 female 8 after weight stable period
## GSM2062487 female 9 at study start
## GSM2062488 female 9 after weight loss period
## GSM2062489 female 9 after weight stable period
## GSM2062490 female 10 at study start
## GSM2062491 female 10 after weight loss period
## GSM2062492 female 10 after weight stable period
## GSM2062493 male 11 at study start
## GSM2062494 male 11 after weight loss period
## GSM2062495 male 11 after weight stable period
## GSM2062496 female 12 at study start
## GSM2062497 female 12 after weight loss period
## GSM2062498 female 12 after weight stable period
## GSM2062499 female 13 at study start
## GSM2062500 female 13 after weight loss period
## GSM2062501 female 13 after weight stable period
## GSM2062502 male 14 at study start
## GSM2062503 male 14 after weight loss period
## GSM2062504 male 14 after weight stable period
## GSM2062505 male 15 after weight loss period
## GSM2062506 male 15 after weight stable period
## GSM2062507 female 18 at study start
## GSM2062508 female 18 after weight stable period
## GSM2062509 female 19 at study start
## GSM2062510 female 19 after weight loss period
## GSM2062511 female 19 after weight stable period
## GSM2062512 male 20 at study start
## GSM2062513 male 20 after weight loss period
## GSM2062514 male 20 after weight stable period
## GSM2062515 female 21 at study start
## GSM2062516 female 21 after weight loss period
## GSM2062517 female 21 after weight stable period
## GSM2062518 male 22 at study start
## GSM2062519 male 22 after weight loss period
## GSM2062520 male 22 after weight stable period
## GSM2062521 male 23 at study start
## GSM2062522 male 23 after weight loss period
## GSM2062523 male 23 after weight stable period
## GSM2062524 female 24 at study start
## GSM2062525 female 24 after weight loss period
## GSM2062526 female 24 after weight stable period
## GSM2062527 male 25 at study start
## GSM2062528 male 25 after weight loss period
## GSM2062529 male 25 after weight stable period
## GSM2062530 male 26 at study start
## GSM2062531 male 26 after weight loss period
## GSM2062532 male 26 after weight stable period
## GSM2062533 female 27 at study start
## GSM2062534 female 27 after weight loss period
## GSM2062535 female 27 after weight stable period
## GSM2062536 female 28 at study start
## GSM2062537 female 28 after weight loss period
## GSM2062538 female 28 after weight stable period
## GSM2062539 male 29 at study start
## GSM2062540 male 29 after weight loss period
## GSM2062541 male 29 after weight stable period
## GSM2062542 male 30 at study start
## GSM2062543 male 30 after weight loss period
## GSM2062544 male 30 after weight stable period
## GSM2062545 female 31 at study start
## GSM2062546 female 31 after weight loss period
## GSM2062547 female 31 after weight stable period
## GSM2062548 male 33 at study start
## GSM2062549 male 33 after weight loss period
## GSM2062550 male 33 after weight stable period
## GSM2062551 female 34 at study start
## GSM2062552 female 34 after weight loss period
## GSM2062553 female 34 after weight stable period
## GSM2062554 female 35 at study start
## GSM2062555 female 35 after weight loss period
## GSM2062556 female 35 after weight stable period
## GSM2062557 female 36 at study start
## GSM2062558 female 36 after weight loss period
## GSM2062559 female 36 after weight stable period
## GSM2062560 female 37 at study start
## GSM2062561 female 37 after weight loss period
## GSM2062562 female 37 after weight stable period
## GSM2062563 female 38 at study start
## GSM2062564 female 38 after weight loss period
## GSM2062565 female 38 after weight stable period
## GSM2062566 female 39 at study start
## GSM2062567 female 39 after weight loss period
## GSM2062568 female 39 after weight stable period
## GSM2062569 female 40 at study start
## GSM2062570 female 40 after weight loss period
## GSM2062571 female 40 after weight stable period
## GSM2062572 female 41 at study start
## GSM2062573 female 41 after weight loss period
## GSM2062574 female 41 after weight stable period
## GSM2062575 male 42 at study start
## GSM2062576 male 42 after weight loss period
## GSM2062577 male 42 after weight stable period
## GSM2062578 male 43 at study start
## GSM2062579 male 43 after weight loss period
## GSM2062580 male 43 after weight stable period
## GSM2062581 female 45 at study start
## GSM2062582 female 45 after weight loss period
## GSM2062583 female 45 after weight stable period
## GSM2062584 female 46 at study start
## GSM2062585 female 46 after weight loss period
## GSM2062586 female 46 after weight stable period
## GSM2062587 female 47 at study start
## GSM2062588 female 47 after weight loss period
## GSM2062589 female 47 after weight stable period
## GSM2062590 male 48 at study start
## GSM2062591 male 48 after weight loss period
## GSM2062592 male 48 after weight stable period
## GSM2062593 male 49 at study start
## GSM2062594 male 49 after weight loss period
## GSM2062595 male 49 after weight stable period
## GSM2062596 female 50 at study start
## GSM2062597 female 50 after weight loss period
## GSM2062598 female 50 after weight stable period
## GSM2062599 male 51 at study start
## GSM2062600 male 51 after weight loss period
## GSM2062601 male 51 after weight stable period
## GSM2062602 male 52 at study start
## GSM2062603 male 52 after weight loss period
## GSM2062604 male 52 after weight stable period
## GSM2062605 male 53 at study start
## GSM2062606 male 53 after weight loss period
## GSM2062607 male 54 after weight loss period
## GSM2062608 male 54 after weight stable period
## GSM2062609 male 55 at study start
## GSM2062610 male 55 after weight loss period
## GSM2062611 male 55 after weight stable period
## GSM2062612 male 56 at study start
## GSM2062613 male 56 after weight stable period
## GSM2062614 male 59 at study start
## GSM2062615 male 59 after weight loss period
## GSM2062616 male 61 at study start
## GSM2062617 male 61 after weight stable period
## tissue:ch1 treatment:ch1
## GSM2062466 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062467 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062468 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062469 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062470 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062471 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062472 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062473 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062474 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062475 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062476 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062477 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062478 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062479 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062480 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062481 abdominal subcutaneous white adipose low-calorie diet
## GSM2062482 abdominal subcutaneous white adipose low-calorie diet
## GSM2062483 abdominal subcutaneous white adipose low-calorie diet
## GSM2062484 abdominal subcutaneous white adipose low-calorie diet
## GSM2062485 abdominal subcutaneous white adipose low-calorie diet
## GSM2062486 abdominal subcutaneous white adipose low-calorie diet
## GSM2062487 abdominal subcutaneous white adipose low-calorie diet
## GSM2062488 abdominal subcutaneous white adipose low-calorie diet
## GSM2062489 abdominal subcutaneous white adipose low-calorie diet
## GSM2062490 abdominal subcutaneous white adipose low-calorie diet
## GSM2062491 abdominal subcutaneous white adipose low-calorie diet
## GSM2062492 abdominal subcutaneous white adipose low-calorie diet
## GSM2062493 abdominal subcutaneous white adipose low-calorie diet
## GSM2062494 abdominal subcutaneous white adipose low-calorie diet
## GSM2062495 abdominal subcutaneous white adipose low-calorie diet
## GSM2062496 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062497 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062498 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062499 abdominal subcutaneous white adipose low-calorie diet
## GSM2062500 abdominal subcutaneous white adipose low-calorie diet
## GSM2062501 abdominal subcutaneous white adipose low-calorie diet
## GSM2062502 abdominal subcutaneous white adipose low-calorie diet
## GSM2062503 abdominal subcutaneous white adipose low-calorie diet
## GSM2062504 abdominal subcutaneous white adipose low-calorie diet
## GSM2062505 abdominal subcutaneous white adipose low-calorie diet
## GSM2062506 abdominal subcutaneous white adipose low-calorie diet
## GSM2062507 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062508 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062509 abdominal subcutaneous white adipose low-calorie diet
## GSM2062510 abdominal subcutaneous white adipose low-calorie diet
## GSM2062511 abdominal subcutaneous white adipose low-calorie diet
## GSM2062512 abdominal subcutaneous white adipose low-calorie diet
## GSM2062513 abdominal subcutaneous white adipose low-calorie diet
## GSM2062514 abdominal subcutaneous white adipose low-calorie diet
## GSM2062515 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062516 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062517 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062518 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062519 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062520 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062521 abdominal subcutaneous white adipose low-calorie diet
## GSM2062522 abdominal subcutaneous white adipose low-calorie diet
## GSM2062523 abdominal subcutaneous white adipose low-calorie diet
## GSM2062524 abdominal subcutaneous white adipose low-calorie diet
## GSM2062525 abdominal subcutaneous white adipose low-calorie diet
## GSM2062526 abdominal subcutaneous white adipose low-calorie diet
## GSM2062527 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062528 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062529 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062530 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062531 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062532 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062533 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062534 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062535 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062536 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062537 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062538 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062539 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062540 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062541 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062542 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062543 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062544 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062545 abdominal subcutaneous white adipose low-calorie diet
## GSM2062546 abdominal subcutaneous white adipose low-calorie diet
## GSM2062547 abdominal subcutaneous white adipose low-calorie diet
## GSM2062548 abdominal subcutaneous white adipose low-calorie diet
## GSM2062549 abdominal subcutaneous white adipose low-calorie diet
## GSM2062550 abdominal subcutaneous white adipose low-calorie diet
## GSM2062551 abdominal subcutaneous white adipose low-calorie diet
## GSM2062552 abdominal subcutaneous white adipose low-calorie diet
## GSM2062553 abdominal subcutaneous white adipose low-calorie diet
## GSM2062554 abdominal subcutaneous white adipose low-calorie diet
## GSM2062555 abdominal subcutaneous white adipose low-calorie diet
## GSM2062556 abdominal subcutaneous white adipose low-calorie diet
## GSM2062557 abdominal subcutaneous white adipose low-calorie diet
## GSM2062558 abdominal subcutaneous white adipose low-calorie diet
## GSM2062559 abdominal subcutaneous white adipose low-calorie diet
## GSM2062560 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062561 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062562 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062563 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062564 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062565 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062566 abdominal subcutaneous white adipose low-calorie diet
## GSM2062567 abdominal subcutaneous white adipose low-calorie diet
## GSM2062568 abdominal subcutaneous white adipose low-calorie diet
## GSM2062569 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062570 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062571 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062572 abdominal subcutaneous white adipose low-calorie diet
## GSM2062573 abdominal subcutaneous white adipose low-calorie diet
## GSM2062574 abdominal subcutaneous white adipose low-calorie diet
## GSM2062575 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062576 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062577 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062578 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062579 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062580 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062581 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062582 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062583 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062584 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062585 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062586 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062587 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062588 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062589 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062590 abdominal subcutaneous white adipose low-calorie diet
## GSM2062591 abdominal subcutaneous white adipose low-calorie diet
## GSM2062592 abdominal subcutaneous white adipose low-calorie diet
## GSM2062593 abdominal subcutaneous white adipose low-calorie diet
## GSM2062594 abdominal subcutaneous white adipose low-calorie diet
## GSM2062595 abdominal subcutaneous white adipose low-calorie diet
## GSM2062596 abdominal subcutaneous white adipose low-calorie diet
## GSM2062597 abdominal subcutaneous white adipose low-calorie diet
## GSM2062598 abdominal subcutaneous white adipose low-calorie diet
## GSM2062599 abdominal subcutaneous white adipose low-calorie diet
## GSM2062600 abdominal subcutaneous white adipose low-calorie diet
## GSM2062601 abdominal subcutaneous white adipose low-calorie diet
## GSM2062602 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062603 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062604 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062605 abdominal subcutaneous white adipose low-calorie diet
## GSM2062606 abdominal subcutaneous white adipose low-calorie diet
## GSM2062607 abdominal subcutaneous white adipose low-calorie diet
## GSM2062608 abdominal subcutaneous white adipose low-calorie diet
## GSM2062609 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062610 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062611 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062612 abdominal subcutaneous white adipose low-calorie diet
## GSM2062613 abdominal subcutaneous white adipose low-calorie diet
## GSM2062614 abdominal subcutaneous white adipose low-calorie diet
## GSM2062615 abdominal subcutaneous white adipose low-calorie diet
## GSM2062616 abdominal subcutaneous white adipose very-low-calorie diet
## GSM2062617 abdominal subcutaneous white adipose very-low-calorie diet
## weight (kg):ch1
## GSM2062466 83.38
## GSM2062467 75.16
## GSM2062468 73.79
## GSM2062469 80.94
## GSM2062470 71.92
## GSM2062471 70.97
## GSM2062472 99.62
## GSM2062473 88.02
## GSM2062474 90.15
## GSM2062475 80.34
## GSM2062476 75.32
## GSM2062477 75.43
## GSM2062478 96.67
## GSM2062479 84.37
## GSM2062480 84.02
## GSM2062481 82.09
## GSM2062482 76.28
## GSM2062483 78.15
## GSM2062484 78.25
## GSM2062485 72.01
## GSM2062486 72.59
## GSM2062487 97.56
## GSM2062488 88.19
## GSM2062489 87.62
## GSM2062490 100.01
## GSM2062491 90.55
## GSM2062492 89.53
## GSM2062493 94.28
## GSM2062494 85.28
## GSM2062495 84.55
## GSM2062496 85.04
## GSM2062497 78.93
## GSM2062498 79.9
## GSM2062499 102.92
## GSM2062500 91.8
## GSM2062501 91.78
## GSM2062502 119.63
## GSM2062503 108.88
## GSM2062504 109.55
## GSM2062505 76.59
## GSM2062506 76.4
## GSM2062507 96.25
## GSM2062508 85.83
## GSM2062509 86.03
## GSM2062510 81.74
## GSM2062511 82.1
## GSM2062512 94.98
## GSM2062513 86.5
## GSM2062514 85.54
## GSM2062515 87.4
## GSM2062516 82.49
## GSM2062517 82.73
## GSM2062518 107.12
## GSM2062519 97.41
## GSM2062520 101.47
## GSM2062521 99.85
## GSM2062522 88.13
## GSM2062523 88
## GSM2062524 91.06
## GSM2062525 81.55
## GSM2062526 80.92
## GSM2062527 94.24
## GSM2062528 86.36
## GSM2062529 85.83
## GSM2062530 114.08
## GSM2062531 103.22
## GSM2062532 101.92
## GSM2062533 81.43
## GSM2062534 74.36
## GSM2062535 73.81
## GSM2062536 79.23
## GSM2062537 72.44
## GSM2062538 70.31
## GSM2062539 98.66
## GSM2062540 89.44
## GSM2062541 87.32
## GSM2062542 104
## GSM2062543 94.29
## GSM2062544 94.69
## GSM2062545 90.06
## GSM2062546 85.12
## GSM2062547 84.24
## GSM2062548 93.66
## GSM2062549 87.42
## GSM2062550 86.96
## GSM2062551 72.87
## GSM2062552 64.16
## GSM2062553 63.36
## GSM2062554 82.83
## GSM2062555 76.1
## GSM2062556 76.45
## GSM2062557 79.31
## GSM2062558 70.14
## GSM2062559 68.96
## GSM2062560 90.1
## GSM2062561 82.32
## GSM2062562 81.92
## GSM2062563 96.72
## GSM2062564 86.38
## GSM2062565 84.86
## GSM2062566 92.75
## GSM2062567 88.67
## GSM2062568 89.07
## GSM2062569 83.6
## GSM2062570 76.4
## GSM2062571 75.17
## GSM2062572 76.21
## GSM2062573 67.21
## GSM2062574 67.54
## GSM2062575 93.56
## GSM2062576 80.87
## GSM2062577 80.69
## GSM2062578 105.43
## GSM2062579 93.87
## GSM2062580 93.71
## GSM2062581 85.66
## GSM2062582 78.4
## GSM2062583 78.05
## GSM2062584 98.1
## GSM2062585 88.68
## GSM2062586 86.41
## GSM2062587 79.63
## GSM2062588 72.45
## GSM2062589 70.99
## GSM2062590 105.07
## GSM2062591 95.98
## GSM2062592 96.41
## GSM2062593 99.32
## GSM2062594 89.64
## GSM2062595 88.68
## GSM2062596 101.96
## GSM2062597 97.57
## GSM2062598 97.63
## GSM2062599 94.28
## GSM2062600 86.1
## GSM2062601 85.46
## GSM2062602 91.85
## GSM2062603 81.91
## GSM2062604 83.72
## GSM2062605 94.09
## GSM2062606 84.81
## GSM2062607 100.79
## GSM2062608 100.38
## GSM2062609 86.7
## GSM2062610 74.46
## GSM2062611 74.21
## GSM2062612 93.36
## GSM2062613 89.98
## GSM2062614 82.42
## GSM2062615 75.01
## GSM2062616 94.26
## GSM2062617 88.5Usamos la columna time point:ch1 para definir los grupos experimentales
sml <- as.factor(tab.ref$`time point:ch1`)
levels(sml) <- c("t1", "t2", "t0")
sml <- as.character(sml)
head(cbind(tab.ref$`time point:ch1`, sml),10)## sml
## [1,] "at study start" "t0"
## [2,] "after weight loss period" "t1"
## [3,] "after weight stable period" "t2"
## [4,] "at study start" "t0"
## [5,] "after weight loss period" "t1"
## [6,] "after weight stable period" "t2"
## [7,] "at study start" "t0"
## [8,] "after weight loss period" "t1"
## [9,] "after weight stable period" "t2"
## [10,] "at study start" "t0"table(tab.ref$`time point:ch1`)##
## after weight loss period after weight stable period
## 50 51
## at study start
## 51Vamos a comparar el estado basal “t0” con el estado final “t2”. Entonces necesitamos conservar esos experimentos.
sel <- which(sml == "t0" | sml == "t2")
sml <- sml[sel]
gset2.sel <- gset2[,sel]
gset2.sel## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 33297 features, 102 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM2062466 GSM2062468 ... GSM2062617 (102 total)
## varLabels: title geo_accession ... weight (kg):ch1 (52 total)
## varMetadata: labelDescription
## featureData
## featureNames: 7892501 7892502 ... 8180418 (33297 total)
## fvarLabels: ID GB_LIST ... category (12 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 30380678
## 27840413
## 32015415
## Annotation: GPL11532Este nuevo grupo de datos solo tiene 102 muestras y no las 152 muestras deel objeto original
Biobase::exprs(gset2.sel) <- log2(Biobase::exprs(gset2.sel))
exprs(gset2)[1:10, 1:10]## GSM2062466 GSM2062467 GSM2062468 GSM2062469 GSM2062470 GSM2062471
## 7892501 4.428059 4.175376 4.025431 4.058749 5.077221 4.167342
## 7892502 3.015303 3.382805 4.266405 3.859811 3.524342 3.983906
## 7892503 2.469030 1.683800 1.353440 1.833336 1.773266 2.073515
## 7892504 7.469548 7.575583 8.319473 7.877318 7.463798 7.777693
## 7892505 2.655132 1.349682 1.610853 1.264390 2.165488 1.744837
## 7892506 3.249786 2.036092 2.997583 3.740663 4.503293 3.034553
## 7892507 4.477538 3.443418 1.898563 2.522465 2.255274 2.785241
## 7892508 4.670769 5.668864 4.620866 4.818567 5.228200 4.565037
## 7892509 9.371950 8.928887 9.044602 9.420649 9.138381 9.007842
## 7892510 3.829357 5.110848 4.364140 4.125794 4.725425 4.407576
## GSM2062472 GSM2062473 GSM2062474 GSM2062475
## 7892501 5.217643 5.940471 4.454951 4.561600
## 7892502 3.177080 3.634795 2.864439 2.929677
## 7892503 2.035071 1.663281 1.647068 2.473372
## 7892504 8.129641 7.505768 8.378240 7.843780
## 7892505 2.018746 1.284328 1.381185 1.591711
## 7892506 3.711080 3.380133 3.368932 2.352224
## 7892507 1.681038 2.673393 3.213846 2.674602
## 7892508 4.197106 5.718841 4.363664 3.552247
## 7892509 9.092477 8.810397 8.168989 8.725467
## 7892510 3.654858 3.996811 4.334708 2.816610exprs(gset2.sel)[1:10, 1:10]## GSM2062466 GSM2062468 GSM2062469 GSM2062471 GSM2062472 GSM2062474
## 7892501 2.146675 2.0091432 2.0210351 2.0591274 2.3833981 2.1554096
## 7892502 1.592303 2.0930208 1.9485303 1.9941834 1.6677012 1.5182527
## 7892503 1.303944 0.4366313 0.8744709 1.0520785 1.0250792 0.7199005
## 7892504 2.901021 3.0564922 2.9777045 2.9593422 3.0231916 3.0666473
## 7892505 1.408783 0.6878247 0.3384420 0.8030919 1.0134591 0.4659069
## 7892506 1.700345 1.5837997 1.9032939 1.6014842 1.8918389 1.7522912
## 7892507 2.162706 0.9249077 1.3348345 1.4778020 0.7493524 1.6843008
## 7892508 2.223660 2.2081633 2.2686042 2.1906265 2.0693949 2.1255400
## 7892509 3.228349 3.1770570 3.2358265 3.1711815 3.1846734 3.0301576
## 7892510 1.937102 2.1256974 2.0446717 2.1399855 1.8698152 2.1159349
## GSM2062475 GSM2062477 GSM2062478 GSM2062480
## 7892501 2.1895399 2.3262887 1.4334180 2.4491141
## 7892502 1.5507415 1.7606308 1.6663763 2.0226608
## 7892503 1.3064795 1.7254773 1.5966113 0.2659277
## 7892504 2.9715490 3.0216918 3.2546946 2.9357117
## 7892505 0.6705781 0.5992727 0.9822516 0.6072214
## 7892506 1.2340256 1.6206185 1.2253831 2.1652501
## 7892507 1.4193240 1.8783526 1.7767849 1.6645434
## 7892508 1.8287318 2.4302321 1.6542023 2.5525036
## 7892509 3.1252323 3.2200353 3.1265988 3.2013015
## 7892510 1.4939597 2.1676196 1.7497367 2.0433382Primero cambiamos el nombre anotado en la descripción por los nombres de grupo. Para ello generamos un factor de los código de grupo sml (“t0” y “t2”), al cual llamaremos como fl. Luego lo asignamos estos resultados a la descripción. Finalmente usamos la función model.matrix() para generar el modelo de matriz.
gset2.sel$description[1:10]## [1] "AP01_1_VLCD_A01.CEL" "AP01_3_VLCD_A03.CEL" "AP02_1_VLCD_A04.CEL"
## [4] "AP02_3_VLCD_A06_2.CEL" "AP03_1_VLCD_A07.CEL" "AP03_3_VLCD_A09.CEL"
## [7] "AP04_1_VLCD_A10.CEL" "AP04_3_VLCD_A12.CEL" "AP05_1_VLCD_B01.CEL"
## [10] "AP05_3_VLCD_B03.CEL"fl <- as.factor(sml)
gset2.sel$description <- fl
gset2.sel$description[1:10]## [1] t0 t2 t0 t2 t0 t2 t0 t2 t0 t2
## Levels: t0 t2design <- model.matrix(~ description + 0, gset2.sel)
colnames(design) <- levels(fl)
design[1:10,]## t0 t2
## GSM2062466 1 0
## GSM2062468 0 1
## GSM2062469 1 0
## GSM2062471 0 1
## GSM2062472 1 0
## GSM2062474 0 1
## GSM2062475 1 0
## GSM2062477 0 1
## GSM2062478 1 0
## GSM2062480 0 1Ahora generamos el modelo de regresión lineal
fit <- lmFit(gset2.sel, design)
summary(fit)## Length Class Mode
## coefficients 66594 -none- numeric
## rank 1 -none- numeric
## assign 2 -none- numeric
## qr 5 qr list
## df.residual 33297 -none- numeric
## sigma 33297 -none- numeric
## cov.coefficients 4 -none- numeric
## stdev.unscaled 66594 -none- numeric
## pivot 2 -none- numeric
## genes 12 data.frame list
## Amean 33297 -none- numeric
## method 1 -none- character
## design 204 -none- numericPara ello generamos una matriz de contraste makeContrast() y con estat calculamoms las veces de cambio con la función contrast.fit()
cont.matrix <- makeContrasts(t2-t0, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2$qr$qr[1:10,]## t0 t2
## GSM2062466 -7.141428 0.000000
## GSM2062468 0.000000 -7.141428
## GSM2062469 0.140028 0.000000
## GSM2062471 0.000000 0.140028
## GSM2062472 0.140028 0.000000
## GSM2062474 0.000000 0.140028
## GSM2062475 0.140028 0.000000
## GSM2062477 0.000000 0.140028
## GSM2062478 0.140028 0.000000
## GSM2062480 0.000000 0.140028Primero hacemos una prueba empirica de Bayes
fit2 <- eBayes(fit2, 0.01)Luego aplicamos una prueba de corrección FDR y ordenamos los datos en una tabla
tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
names(tT)## [1] "ID" "GB_LIST" "SPOT_ID" "seqname"
## [5] "RANGE_GB" "RANGE_STRAND" "RANGE_START" "RANGE_STOP"
## [9] "total_probes" "gene_assignment" "mrna_assignment" "category"
## [13] "logFC" "AveExpr" "t" "P.Value"
## [17] "adj.P.Val" "B"tT[1:10,c(2,12:17)]## GB_LIST
## 8162940 NM_005502,AF285167,BC146856,AB445477,AB055982,AF165281,BC141816,BC142693,AB208839,AK024328,AF258627,AK130814,BC034824
## 8117020 NM_013262,AF187016
## 7995907 NM_000078,M30185
## 8153890 NM_138367,AK091638
## 8113504 NM_004772,NM_001142478,NM_001142482,NM_001142476,NM_001142477,NM_001142479,NM_001142480,NM_001142481,NM_001142483,NM_001142474,NM_001142475,U30521
## 8029530 NM_000041,K00396,AK130027
## 8129985 NM_006718,NM_001080951,NM_001080952,NM_001080953,NM_001080954,NM_002656,NM_001080956,NM_001080955,NR_002768,AJ311395
## 8025828 NM_000527,NM_001195798,NM_001195799,NM_001195800,NM_001195803,NM_001195802,BC014514
## 8027566 NM_001806,BC007582
## 8102792 NM_019035,AK292018
## category logFC AveExpr t P.Value adj.P.Val
## 8162940 main 0.06666646 3.022428 6.685869 1.242644e-09 4.137631e-05
## 8117020 main 0.06287193 2.945982 6.401047 4.754999e-09 7.916361e-05
## 7995907 main 0.23333217 2.482309 6.311873 7.205073e-09 7.996910e-05
## 8153890 main 0.04622307 2.691991 5.478665 3.095728e-07 2.576961e-03
## 8113504 main 0.05804069 2.814585 5.368877 4.987178e-07 3.321161e-03
## 8029530 main 0.09306653 3.216542 5.316511 6.250183e-07 3.468539e-03
## 8129985 main 0.05337820 2.955687 5.017984 2.214266e-06 1.053263e-02
## 8025828 main -0.08927095 2.785342 -4.934059 3.137674e-06 1.207828e-02
## 8027566 main -0.03285473 2.756959 -4.918628 3.344184e-06 1.207828e-02
## 8102792 main 0.05005513 3.216703 4.881611 3.894941e-06 1.207828e-02Primero definimos la plataforma del microarreglo
(gpl <- Biobase::annotation(gset2.sel))## [1] "GPL11532"En este caso la plataforma es GLP11532. Ahora para descargar las anotaciones específicas de estas sondas usamos la función getGEO()
annot <- getGEO(gpl, AnnotGPL=TRUE)## File stored at:## C:\Users\acard\AppData\Local\Temp\Rtmp0sTNzl/GPL11532.annot.gzGEOquery::Table(annot)[1:3,]## ID
## 1 7896736
## 2 7896738
## 3 7896740
## Gene title
## 1
## 2
## 3 olfactory receptor family 4 subfamily F member 17///olfactory receptor family 4 subfamily F member 5///olfactory receptor family 4 subfamily F member 4
## Gene symbol Gene ID UniGene title UniGene symbol
## 1
## 2
## 3 OR4F17///OR4F5///OR4F4 81099///79501///26682
## UniGene ID
## 1
## 2
## 3
## Nucleotide Title
## 1
## 2
## 3 Homo sapiens olfactory receptor family 4 subfamily F member 17 (OR4F17), mRNA///Homo sapiens olfactory receptor family 4 subfamily F member 4 (OR4F4), mRNA///Homo sapiens olfactory receptor family 4 subfamily F member 5 (OR4F5), mRNA///Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds///Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds
## GI
## 1
## 2
## 3 52546738///57528065///53828739///187953368///187952254
## GenBank Accession
## 1
## 2
## 3 NM_001005240///NM_001004195///NM_001005484///BC136848///BC136907
## Platform_CLONEID Platform_ORF Platform_SPOTID Chromosome location
## 1 NA NA chr1:53049-54936
## 2 NA NA chr1:63015-63887
## 3 NA NA chr1:69091-70008 19p13.3///1p36.33///15q26.3
## Chromosome annotation
## 1
## 2
## 3 Chromosome 19, NC_000019.10 (107152..111690)///Chromosome 1, NC_000001.11 (69091..70008)///Chromosome 15, NC_000015.10 (101922142..101923059, complement)
## GO:Function
## 1
## 2
## 3 G-protein coupled receptor activity///olfactory receptor activity///transmembrane signaling receptor activity///G-protein coupled receptor activity///olfactory receptor activity///transmembrane signaling receptor activity///G-protein coupled receptor activity///olfactory receptor activity///transmembrane signaling receptor activity
## GO:Process
## 1
## 2
## 3 G-protein coupled receptor signaling pathway///detection of chemical stimulus involved in sensory perception///detection of chemical stimulus involved in sensory perception of smell///G-protein coupled receptor signaling pathway///detection of chemical stimulus involved in sensory perception///detection of chemical stimulus involved in sensory perception of smell///G-protein coupled receptor signaling pathway///detection of chemical stimulus involved in sensory perception///detection of chemical stimulus involved in sensory perception of smell
## GO:Component
## 1
## 2
## 3 integral component of membrane///plasma membrane///integral component of membrane///plasma membrane///integral component of membrane///plasma membrane
## GO:Function ID
## 1
## 2
## 3 GO:0004930///GO:0004984///GO:0004888///GO:0004930///GO:0004984///GO:0004888///GO:0004930///GO:0004984///GO:0004888
## GO:Process ID
## 1
## 2
## 3 GO:0007186///GO:0050907///GO:0050911///GO:0007186///GO:0050907///GO:0050911///GO:0007186///GO:0050907///GO:0050911
## GO:Component ID
## 1
## 2
## 3 GO:0016021///GO:0005886///GO:0016021///GO:0005886///GO:0016021///GO:0005886Ahora extraemos los nombres de los genes de las sondas correspondientes de los atributos de objeto annot y los coloacamos como una data.frame en un objeto llamado ncbifd
ncbifd <- data.frame(attr(dataTable(annot), "table"))
names(ncbifd)## [1] "ID" "Gene.title" "Gene.symbol"
## [4] "Gene.ID" "UniGene.title" "UniGene.symbol"
## [7] "UniGene.ID" "Nucleotide.Title" "GI"
## [10] "GenBank.Accession" "Platform_CLONEID" "Platform_ORF"
## [13] "Platform_SPOTID" "Chromosome.location" "Chromosome.annotation"
## [16] "GO.Function" "GO.Process" "GO.Component"
## [19] "GO.Function.ID" "GO.Process.ID" "GO.Component.ID"head(ncbifd,3)## ID
## 1 7896736
## 2 7896738
## 3 7896740
## Gene.title
## 1
## 2
## 3 olfactory receptor family 4 subfamily F member 17///olfactory receptor family 4 subfamily F member 5///olfactory receptor family 4 subfamily F member 4
## Gene.symbol Gene.ID UniGene.title UniGene.symbol
## 1
## 2
## 3 OR4F17///OR4F5///OR4F4 81099///79501///26682
## UniGene.ID
## 1
## 2
## 3
## Nucleotide.Title
## 1
## 2
## 3 Homo sapiens olfactory receptor family 4 subfamily F member 17 (OR4F17), mRNA///Homo sapiens olfactory receptor family 4 subfamily F member 4 (OR4F4), mRNA///Homo sapiens olfactory receptor family 4 subfamily F member 5 (OR4F5), mRNA///Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds///Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds
## GI
## 1
## 2
## 3 52546738///57528065///53828739///187953368///187952254
## GenBank.Accession
## 1
## 2
## 3 NM_001005240///NM_001004195///NM_001005484///BC136848///BC136907
## Platform_CLONEID Platform_ORF Platform_SPOTID Chromosome.location
## 1 NA NA chr1:53049-54936
## 2 NA NA chr1:63015-63887
## 3 NA NA chr1:69091-70008 19p13.3///1p36.33///15q26.3
## Chromosome.annotation
## 1
## 2
## 3 Chromosome 19, NC_000019.10 (107152..111690)///Chromosome 1, NC_000001.11 (69091..70008)///Chromosome 15, NC_000015.10 (101922142..101923059, complement)
## GO.Function
## 1
## 2
## 3 G-protein coupled receptor activity///olfactory receptor activity///transmembrane signaling receptor activity///G-protein coupled receptor activity///olfactory receptor activity///transmembrane signaling receptor activity///G-protein coupled receptor activity///olfactory receptor activity///transmembrane signaling receptor activity
## GO.Process
## 1
## 2
## 3 G-protein coupled receptor signaling pathway///detection of chemical stimulus involved in sensory perception///detection of chemical stimulus involved in sensory perception of smell///G-protein coupled receptor signaling pathway///detection of chemical stimulus involved in sensory perception///detection of chemical stimulus involved in sensory perception of smell///G-protein coupled receptor signaling pathway///detection of chemical stimulus involved in sensory perception///detection of chemical stimulus involved in sensory perception of smell
## GO.Component
## 1
## 2
## 3 integral component of membrane///plasma membrane///integral component of membrane///plasma membrane///integral component of membrane///plasma membrane
## GO.Function.ID
## 1
## 2
## 3 GO:0004930///GO:0004984///GO:0004888///GO:0004930///GO:0004984///GO:0004888///GO:0004930///GO:0004984///GO:0004888
## GO.Process.ID
## 1
## 2
## 3 GO:0007186///GO:0050907///GO:0050911///GO:0007186///GO:0050907///GO:0050911///GO:0007186///GO:0050907///GO:0050911
## GO.Component.ID
## 1
## 2
## 3 GO:0016021///GO:0005886///GO:0016021///GO:0005886///GO:0016021///GO:0005886Finalmente reemplazamos las anotaciones de nuestro objeto por las de la plataforma
# replace original platform annotation
colnames(tT)## [1] "ID" "GB_LIST" "SPOT_ID" "seqname"
## [5] "RANGE_GB" "RANGE_STRAND" "RANGE_START" "RANGE_STOP"
## [9] "total_probes" "gene_assignment" "mrna_assignment" "category"
## [13] "logFC" "AveExpr" "t" "P.Value"
## [17] "adj.P.Val" "B"fvarLabels(gset2.sel)## [1] "ID" "GB_LIST" "SPOT_ID" "seqname"
## [5] "RANGE_GB" "RANGE_STRAND" "RANGE_START" "RANGE_STOP"
## [9] "total_probes" "gene_assignment" "mrna_assignment" "category"tT <- tT[setdiff(colnames(tT), setdiff(fvarLabels(gset2.sel), "ID"))]
colnames(tT)## [1] "ID" "logFC" "AveExpr" "t" "P.Value" "adj.P.Val"
## [7] "B"colnames(ncbifd)## [1] "ID" "Gene.title" "Gene.symbol"
## [4] "Gene.ID" "UniGene.title" "UniGene.symbol"
## [7] "UniGene.ID" "Nucleotide.Title" "GI"
## [10] "GenBank.Accession" "Platform_CLONEID" "Platform_ORF"
## [13] "Platform_SPOTID" "Chromosome.location" "Chromosome.annotation"
## [16] "GO.Function" "GO.Process" "GO.Component"
## [19] "GO.Function.ID" "GO.Process.ID" "GO.Component.ID"tT <- merge(tT, ncbifd, by="ID")
head(tT)## ID logFC AveExpr t P.Value adj.P.Val B
## 1 7892703 -0.05836107 2.631622 -3.667390 0.0003911748 0.09370466 -0.3273478
## 2 7893112 0.18986676 1.727679 3.735525 0.0003087598 0.08194541 -0.1076165
## 3 7893164 0.04099345 3.125884 3.452566 0.0008092809 0.12138120 -0.9996973
## 4 7893722 0.08824893 2.305622 3.706298 0.0003418591 0.08431765 -0.2022463
## 5 7893804 0.18364563 2.153936 3.775303 0.0002685742 0.07990425 0.0220761
## 6 7893876 0.06010353 2.709580 3.410522 0.0009298190 0.12886532 -1.1275685
## Gene.title Gene.symbol Gene.ID UniGene.title UniGene.symbol UniGene.ID
## 1
## 2
## 3
## 4
## 5
## 6
## Nucleotide.Title GI GenBank.Accession Platform_CLONEID Platform_ORF
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## Platform_SPOTID Chromosome.location Chromosome.annotation GO.Function
## 1 --normgene->exon
## 2 --normgene->intron
## 3 --normgene->exon
## 4 --normgene->intron
## 5 --normgene->intron
## 6 --normgene->intron
## GO.Process GO.Component GO.Function.ID GO.Process.ID GO.Component.ID
## 1
## 2
## 3
## 4
## 5
## 6tT <- tT[order(tT$P.Value), ] # restore correct order
names(tT)## [1] "ID" "logFC" "AveExpr"
## [4] "t" "P.Value" "adj.P.Val"
## [7] "B" "Gene.title" "Gene.symbol"
## [10] "Gene.ID" "UniGene.title" "UniGene.symbol"
## [13] "UniGene.ID" "Nucleotide.Title" "GI"
## [16] "GenBank.Accession" "Platform_CLONEID" "Platform_ORF"
## [19] "Platform_SPOTID" "Chromosome.location" "Chromosome.annotation"
## [22] "GO.Function" "GO.Process" "GO.Component"
## [25] "GO.Function.ID" "GO.Process.ID" "GO.Component.ID"tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title","Chromosome.location"))
head(tT)## ID adj.P.Val P.Value t B logFC
## 231 8162940 4.137631e-05 1.242644e-09 6.685869 11.715914 0.06666646
## 181 8117020 7.916361e-05 4.754999e-09 6.401047 10.425354 0.06287193
## 98 7995907 7.996910e-05 7.205073e-09 6.311873 10.025993 0.23333217
## 218 8153890 2.576961e-03 3.095728e-07 5.478665 6.421709 0.04622307
## 174 8113504 3.321161e-03 4.987178e-07 5.368877 5.966212 0.05804069
## 125 8029530 3.468539e-03 6.250183e-07 5.316511 5.750734 0.09306653
## Gene.symbol Gene.title
## 231 ABCA1 ATP binding cassette subfamily A member 1
## 181 MYLIP myosin regulatory light chain interacting protein
## 98 CETP cholesteryl ester transfer protein
## 218 ZNF251 zinc finger protein 251
## 174 NREP neuronal regeneration related protein
## 125 APOE///HMGA1 apolipoprotein E///high mobility group AT-hook 1
## Chromosome.location
## 231 9q31.1
## 181 6p22.3
## 98 16q21
## 218 8q24.3
## 174 5q22.1
## 125 19q13.2///6p21Guardamos la tabla que se generó
write.table(tT, file="C:/Users/acard/CloudStation/R-project/mara_nat_project/result/GSE77962_t0_t2.txt", row.names=F, sep="\t")======================================================
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