The goal of geokit is to provide a unified interface for most interactions between R and GEO database.
Features
- Low dependency and Consistent design, use
curlto download all files, and utilizedata.tableto implement all reading and preprocessing process. Reducing the dependencies is the initial purpose of this package since I have experienced several times of code running failure after updating packages when usingGEOquery. - Provide a searching interface of GEO database, in this way, we can filter the searching results using
R function. - Provide a downloading interface of GEO database, in this way, we can make full use of R to analyze GEO datasets.
- Enable mapping bettween GPL id and Bioconductor annotation package.
- Provide some useful utils function to work with GEO datasets like
parse_gsm_list,parse_pdata,log_transandgeo_show.
Installation
You can install the development version of geokit from GitHub with:
if (!requireNamespace("pak")) {
install.packages("pak",
repos = sprintf(
"https://r-lib.github.io/p/pak/devel/%s/%s/%s",
.Platform$pkgType, R.Version()$os, R.Version()$arch
)
)
}
pak::pkg_install("Yunuuuu/geokit")Vignettes
Search GEO database - geo_search
The NCBI uses a search term syntax which can be associated with a specific search field enclosed by a pair of square brackets. So, for instance "Homo sapiens[ORGN]" denotes a search for Homo sapiens in the “Organism” field. Details see https://www.ncbi.nlm.nih.gov/geo/info/qqtutorial.html. We can use the same term to query our desirable results in geo_search. geo_search will parse the searching results and return a data.frame object containing all the records based on the search term. The internal of geo_search is based on rentrez package, which provides functions working with the NCBI Eutils API, so we can utilize NCBI API key to increase the downloading speed, details see https://docs.ropensci.org/rentrez/articles/rentrez_tutorial.html#rate-limiting-and-api-keys.
Providing we want GSE GEO records related to human diabetes, we can get these records by following code, the returned object is a data.frame:
diabetes_gse_records <- geo_search(
"diabetes[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]"
)
#> ■■■■■■■■■■ 500/1690 [484/s] | ETA: 2s
#> ■■■■■■■■■■■■■■■■■■■ 1000/1690 [387/s] | ETA: 2s
#> Get records from NCBI for 1690 queries in 4.8s
#>
#> → Parsing GEO records
head(diabetes_gse_records[1:5])
#> Title
#> <char>
#> 1: Coxsackievirus B infection invokes unique cell-type specific responses in primary human pancreatic islets
#> 2: Expression data from type 2 diabetes mellitus adipose-derived stem cells cultured with basic fibroblast growth factor
#> 3: Engineered vasculature induces functional maturation of pluripotent stem cell-derived islet organoids
#> 4: Recessive TMEM167A variants cause neonatal diabetes, microcephaly and epilepsy syndrome
#> 5: Recessive TMEM167A variants cause neonatal diabetes, microcephaly and epilepsy syndrome [scRNA-seq]
#> Summary
#> <char>
#> 1: Coxsackievirus B (CVB) infection has long been considered an environmental factor precipitating Type 1 diabetes (T1D), an autoimmune disease marked by loss of insulin-producing b cells within pancreatic islets. Previous studies have shown CVB infection negatively impacts islet function and viability but do not report on how virus infection individually affects the multiple cell types present in human primary islets. more...
#> 2: Diabetes affects ASCs characteristics such as: proliferation, differentiation and angiogenic capacity. MicroRNAs are able to target genes involved in vascular remodeling and promote or inhibit structural changes in the vessel wall. Adipose tissue-derived stem cells (ASCs) have the capacity to contribute to vascular remodeling. We used microarrays to detail the global miRNA expression profile underlying cell differentiation and identified distinct classes of up-regulated and down-regulated genes during this process.
#> 3: Blood vessels play a critical role in pancreatic islet function, yet current methods for deriving islet organoids from human pluripotent stem cells (SC-islets) lack vasculature. We engineered 3D vascularized SC-islet organoids by assembling SC-islet cells, human primary endothelial cells (ECs) and fibroblasts in a non-perfused model and a microfluidic device with perfused vessels. Vasculature improved stimulus-dependent Ca2+ influx into SC-β-cells, a hallmark of β-cell function that is blunted in non-vascularized SC-islets. more...
#> 4: Understanding the genetic causes of diseases affecting pancreatic β cells and neurons can give insights into pathways essential for both cell types. Microcephaly, epilepsy and diabetes syndrome (MEDS) is a congenital disorder with two known aetiological genes, IER3IP1 and YIPF5. Both genes encode proteins involved in endoplasmic reticulum (ER) to Golgi trafficking. We used genome sequencing to identify 6 individuals with MEDS caused by biallelic variants in the novel disease gene, TMEM167A. more...
#> 5: Understanding the genetic causes of diseases affecting pancreatic β cells and neurons can give insights into pathways essential for both cell types. Microcephaly, epilepsy and diabetes syndrome (MEDS) is a congenital disorder with two known aetiological genes, IER3IP1 and YIPF5. Both genes encode proteins involved in endoplasmic reticulum (ER) to Golgi trafficking. We used genome sequencing to identify 6 individuals with MEDS caused by biallelic variants in the novel disease gene, TMEM167A. more...
#> Organism
#> <char>
#> 1: Homo sapiens
#> 2: Homo sapiens; synthetic construct
#> 3: Homo sapiens
#> 4: Homo sapiens
#> 5: Homo sapiens
#> Type
#> <char>
#> 1: Expression profiling by high throughput sequencing
#> 2: Non-coding RNA profiling by array
#> 3: Expression profiling by high throughput sequencing
#> 4: Expression profiling by high throughput sequencing
#> 5: Expression profiling by high throughput sequencing
#> FTP download
#> <char>
#> 1: GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE274nnn/GSE274264/
#> 2: GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE283nnn/GSE283040/
#> 3: GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE276nnn/GSE276815/
#> 4: GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE302nnn/GSE302570/
#> 5: GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE302nnn/GSE302421/
#> ID SRA Run Selector Project Contains Datasets Platforms
#> <int> <char> <char> <char> <char> <char>
#> 1: 200274264 <NA> <NA> 10 Samples <NA> GPL24676
#> 2: 200283040 <NA> <NA> 16 Samples <NA> GPL19117
#> 3: 200276815 <NA> <NA> 2 Samples <NA> GPL24676
#> 4: 200302570 <NA> <NA> 40 Samples <NA> GPL34284
#> 5: 200302421 <NA> <NA> 3 Samples <NA> GPL24676
#> Series Accession
#> <char>
#> 1: GSE274264
#> 2: GSE283040
#> 3: GSE276815
#> 4: GSE302570
#> 5: GSE302421Then, we can use whatever we’re famaliar to filter the searching results. Providing we want GSE datasets with at least 6 diabetic nephropathy samples containing expression profiling. Here is the example code:
diabetes_nephropathy_gse_records <- diabetes_gse_records |>
dplyr::mutate(
number_of_samples = stringr::str_match(
Contains, "(\\d+) Samples?"
)[, 2L, drop = TRUE],
number_of_samples = as.integer(number_of_samples)
) |>
dplyr::filter(
dplyr::if_any(
c(Title, Summary),
~ stringr::str_detect(.x, "(?i)diabetes|diabetic")
),
dplyr::if_any(
c(Title, Summary),
~ stringr::str_detect(.x, "(?i)nephropathy")
),
stringr::str_detect(Type, "(?i)expression profiling"),
number_of_samples >= 6L
)
head(diabetes_nephropathy_gse_records[1:5])
#> Title
#> <char>
#> 1: Endothelial Kallikrein-Related Peptidase 8 Promotes Diabetic Nephropathy via Reducing SDC4 Expression and Enhancing LIF Release
#> 2: Upregulation of FGF13 promotes type 2 diabetic nephropathy by modulating glomerular endothelial mitochondrial homeostasis
#> 3: Sodium Butyrate Ameliorates Renal Tubular Lipid Accumulation Through the PP2A-TFEB axis in Diabetic Nephropathy
#> 4: Deciphering the Transcriptomic Landscape of Type 2 Diabetes: Insights from Bulk RNA Sequencing and Single-Cell Analysis [RNA-seq]
#> 5: Effect of overexpssion Kallistatin(SERPINA4) in HGC-27 cell line
#> Summary
#> <char>
#> 1: The molecular mechanisms underlying diabetic nephropathy (DN) are poorly defined. We sought to investigate the roles of kallikrein-related peptidases (KLKs) in DN pathogenesis. Screening of renal tissue from diabetic mice revealed KLK8 as the most highly induced gene in KLK family. KLK8 expression was greater in glomerular endothelial cells (GECs) than other glomerular cells in DN patients and diabetic mice. more...
#> 2: Studies of diabetic glomerular injury raise the possibility of developing useful early biomarkers and therapeutic approaches for the treatment of type 2 diabetic nephropathy (T2DN). In this study, it is found that FGF13 expression is induced in glomerular endothelial cells (GECs) during T2DN progression, and endothelial-specific deletion of Fgf13 potentially alleviates T2DN damage. Fgf13 deficiency restores the expression of Parkin both in the cytosolic, mitochondrial, and nuclear fractions under diabetic conditions, resulting in improved mitochondrial homeostasis and endothelial barrier integrity due to promotion of mitophagy and inhibition of apoptosis. more...
#> 3: Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide with limited treatment options. The intricate pathogenesis of dysregulated lipid metabolism leading to the development of DKD remains obscure. Lipophagy, which refers to the autophagic degradation of intracellular lipid droplets, has been found to be impaired in DKD, resulting in renal tubule dysfunction and ectopic lipid deposition (ELD). more...
#> 4: Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by insulin resistance and relative insulin deficiency. It is a significant public health concern worldwide, with an estimated prevalence of over 422 million individuals affected globally. This number is projected to rise, making diabetes one of the leading causes of morbidity and mortality. It is associated with numerous severe microvascular and macrovascular complications, including retinopathy, nephropathy, cardiovascular diseases, and neuropathy, which substantially impact patients' quality of life and healthcare systems. more...
#> 5: Kallistatin has been demonstrated to possess inhibitory effects across several malignancies, including hepatocellular carcinoma, gastric cancer and breast cancer. Subsequent evidence has increasingly suggested that KS has pleiotropic roles in modulating a broad spectrum of diseases, including in diabetic nephropathy, idiopathic pulmonary fibrosis and autoimmune uveitis. However, the precise function and molecular mechanisms underlying tumor-induced immune escape attributed to KS remain unclear, necessitating further investigation to determine its role in this context.For this propose, we establish SERPINA4 stably expressed cell line(and control) in HGC-27 cells, and RNA-seq was performed to reveal the trancriptome changes between there two cell lines.
#> Organism Type
#> <char> <char>
#> 1: Homo sapiens Expression profiling by high throughput sequencing
#> 2: Homo sapiens Expression profiling by high throughput sequencing
#> 3: Homo sapiens Expression profiling by high throughput sequencing
#> 4: Homo sapiens Expression profiling by high throughput sequencing
#> 5: Homo sapiens Expression profiling by high throughput sequencing
#> FTP download
#> <char>
#> 1: GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE255nnn/GSE255028/
#> 2: GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE192nnn/GSE192889/
#> 3: GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE266nnn/GSE266108/
#> 4: GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE280nnn/GSE280402/
#> 5: GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE262nnn/GSE262922/
#> ID SRA Run Selector Project Contains Datasets Platforms
#> <int> <char> <char> <char> <char> <char>
#> 1: 200255028 <NA> <NA> 9 Samples <NA> GPL24676
#> 2: 200192889 <NA> <NA> 9 Samples <NA> GPL24676
#> 3: 200266108 <NA> <NA> 6 Samples <NA> GPL24676
#> 4: 200280402 <NA> <NA> 16 Samples <NA> GPL16791
#> 5: 200262922 <NA> <NA> 6 Samples <NA> GPL20301
#> Series Accession number_of_samples
#> <char> <int>
#> 1: GSE255028 9
#> 2: GSE192889 9
#> 3: GSE266108 6
#> 4: GSE280402 16
#> 5: GSE262922 6After filtering, we got 36 candidate datasets. This can reduce a lot of time of us comparing with refining datasets by reading the summary records.
Download data from GEO database - geo
GEO database mainly provides SOFT (Simple Omnibus Format in Text) formatted files for GPL, GSM and GDS entity. SOFT is designed for rapid batch submission and download of data. SOFT is a simple line-based, plain text format, meaning that SOFT files may be readily generated from common spreadsheet and database applications. A single SOFT file can hold both data tables and accompanying descriptive information for multiple, concatenated Platforms, Samples, and/or Series records. geokit provide a GEOSoft class object to store SOFT file contents, GEOSoft object contains four slots (“accession”, “meta”, “datatable”, and “columns”). accession slot stores the GEO accession ID, meta slot contains the metadata header in the SOFT formatted file, and datatable slot contains the the data table in SOFT file which is the main data for us to use, along with a columns slot providing descriptive column header for the datatable data. We can use the function with the same name of these slots to extract the data.
geo can download SOFT files and preprocess them well, here is some example code to get soft file from GPL, GSM and GDS entity respectively.
gpl <- geo("gpl98", odir = tempdir())
#> Downloading 1 GPL full amount file from GEO Accession Site
gpl
#> An object of GEOSoft
#> datatable: a 8934 * 16 data.frame
#> columns: a 16 * 1 data.frame
#> columnsData: labelDescription
#> meta: Platform_contact_address Platform_contact_city
#> Platform_contact_country ... Platform_title Platform_web_link (26
#> total)
#> accession: GPL98
head(datatable(gpl))
#> ID GB_ACC SPOT_ID Species Scientific Name
#> AA000993_at AA000993_at AA000993 Homo sapiens
#> AA001296_s_at AA001296_s_at AA001296 Homo sapiens
#> AA002245_at AA002245_at AA002245 Homo sapiens
#> AA004231_at AA004231_at AA004231 Homo sapiens
#> AA004333_at AA004333_at AA004333 Homo sapiens
#> AA004987_at AA004987_at AA004987 Homo sapiens
#> Annotation Date Sequence Type Sequence Source
#> AA000993_at Mar 11, 2009 Exemplar sequence GenBank
#> AA001296_s_at Mar 11, 2009 Exemplar sequence GenBank
#> AA002245_at Mar 11, 2009 Exemplar sequence GenBank
#> AA004231_at Mar 11, 2009 Exemplar sequence GenBank
#> AA004333_at Mar 11, 2009 Exemplar sequence GenBank
#> AA004987_at Mar 11, 2009 Exemplar sequence GenBank
#> Target Description
#> AA000993_at ze46h10.r1 Soares retina N2b4HR Homo sapiens cDNA clone 362083 5'.
#> AA001296_s_at zh82b09.r1 Soares fetal liver spleen 1NFLS S1 Homo sapiens cDNA clone 427769 5'.
#> AA002245_at zh85f01.r1 Soares fetal liver spleen 1NFLS S1 Homo sapiens cDNA clone 428089 5'.
#> AA004231_at zh92a03.r1 Soares fetal liver spleen 1NFLS S1 Homo sapiens cDNA clone 428716 5'.
#> AA004333_at zh91a01.r1 Soares fetal liver spleen 1NFLS S1 Homo sapiens cDNA clone 428616 5'.
#> AA004987_at zh94b01.r1 Soares fetal liver spleen 1NFLS S1 Homo sapiens cDNA clone 428905 5'.
#> Representative Public ID
#> AA000993_at AA000993
#> AA001296_s_at AA001296
#> AA002245_at AA002245
#> AA004231_at AA004231
#> AA004333_at AA004333
#> AA004987_at AA004987
#> Gene Title
#> AA000993_at PR domain containing 8
#> AA001296_s_at PHD finger protein 23
#> AA002245_at zinc finger CCCH-type containing 11A
#> AA004231_at
#> AA004333_at
#> AA004987_at BMP2 inducible kinase, mRNA (cDNA clone MGC:33000 IMAGE:5272264)
#> Gene Symbol ENTREZ_GENE_ID RefSeq Transcript ID
#> AA000993_at PRDM8 56978 NM_001099403 /// NM_020226
#> AA001296_s_at PHF23 79142 NM_024297
#> AA002245_at ZC3H11A 9877 NM_014827
#> AA004231_at
#> AA004333_at
#> AA004987_at BMP2K 55589 NM_017593 /// NM_198892
#> Gene Ontology Biological Process
#> AA000993_at 0006350 // transcription // inferred from electronic annotation /// 0006355 // regulation of transcription, DNA-dependent // inferred from electronic annotation
#> AA001296_s_at
#> AA002245_at
#> AA004231_at
#> AA004333_at
#> AA004987_at 0006468 // protein amino acid phosphorylation // inferred from electronic annotation
#> Gene Ontology Cellular Component
#> AA000993_at 0005622 // intracellular // inferred from electronic annotation /// 0005634 // nucleus // inferred from electronic annotation
#> AA001296_s_at
#> AA002245_at
#> AA004231_at
#> AA004333_at
#> AA004987_at 0005634 // nucleus // inferred from electronic annotation
#> Gene Ontology Molecular Function
#> AA000993_at 0003677 // DNA binding // inferred from electronic annotation /// 0008270 // zinc ion binding // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation
#> AA001296_s_at 0005515 // protein binding // inferred from electronic annotation /// 0008270 // zinc ion binding // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation
#> AA002245_at 0003676 // nucleic acid binding // inferred from electronic annotation /// 0008270 // zinc ion binding // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation
#> AA004231_at
#> AA004333_at
#> AA004987_at 0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004674 // protein serine/threonine kinase activity // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation
head(columns(gpl))
#> labelDescription
#> ID Affymetrix Probe Set ID LINK_PRE:"https://www.affymetrix.com/LinkServlet?array=U35K&probeset="
#> GB_ACC GenBank Accession Number LINK_PRE:"http://www.ncbi.nlm.nih.gov/nuccore/?term="
#> SPOT_ID <NA>
#> Species Scientific Name The genus and species of the organism represented by the probe set.
#> Annotation Date The date that the annotations for this probe array were last updated. It will generally be earlier than the date when the annotations were posted on the Affymetrix web site.
#> Sequence Type Indicates whether the sequence is an Exemplar, Consensus or Control sequence. An Exemplar is a single nucleotide sequence taken directly from a public database. This sequence could be an mRNA or EST. A Consensus sequence, is a nucleotide sequence assembled by Affymetrix, based on one or more sequence taken from a public database.
gsm <- geo("GSM1", odir = tempdir())
#> Downloading 1 GSM full amount file from GEO Accession Site
gsm
#> An object of GEOSoft
#> datatable: a 5494 * 3 data.frame
#> columns: a 3 * 1 data.frame
#> columnsData: labelDescription
#> meta: Sample_anchor Sample_channel_count Sample_contact_address ...
#> Sample_title Sample_type (29 total)
#> accession: GSM1
head(datatable(gsm))
#> TAG COUNT TPM
#> AAAAAAAAAA AAAAAAAAAA 17 1741.98
#> AAAAAAATCA AAAAAAATCA 1 102.47
#> AAAAAAATTT AAAAAAATTT 1 102.47
#> AAAAAACAAA AAAAAACAAA 1 102.47
#> AAAAAACTCC AAAAAACTCC 1 102.47
#> AAAAAATAAA AAAAAATAAA 1 102.47
head(columns(gsm))
#> labelDescription
#> TAG Ten base SAGE tag,
#> COUNT TAG NUMBER
#> TPM tags per million
gds <- geo("GDS10", odir = tempdir())
#> Downloading 1 GDS soft file from FTP site
gds
#> An object of GEOSoft
#> datatable: a 39114 * 30 data.frame
#> columns: a 30 * 4 data.frame
#> columnsData: labelDescription subset_dataset_id subset_description
#> subset_type
#> meta: Database_email Database_institute Database_name ...
#> dataset_update_date dataset_value_type (21 total)
#> accession: GDS10
head(datatable(gds))
#> ID_REF IDENTIFIER GSM582 GSM589 GSM583 GSM590 GSM584 GSM591 GSM585 GSM592
#> 1 1 1200011I18Rik 101 54 111 55 87 30 99 43
#> 2 2 2 26 23 30 27 19 22 32 19
#> 3 3 Ccdc28b NA NA NA NA NA NA NA NA
#> 4 4 AA014405 233 162 252 178 214 144 238 147
#> 5 5 Crebrf NA NA NA NA NA NA NA NA
#> 6 6 6 691 661 696 652 609 665 684 672
#> GSM586 GSM593 GSM587 GSM594 GSM588 GSM595 GSM596 GSM603 GSM597 GSM604 GSM598
#> 1 105 56 43 14 112 43 97 36 117 40 125
#> 2 24 25 14 49 32 29 31 22 26 26 35
#> 3 NA NA NA 7 NA 4 10 22 NA 15 NA
#> 4 250 166 86 22 236 139 216 112 241 130 270
#> 5 NA NA NA NA NA 3 NA NA NA NA NA
#> 6 644 679 631 596 609 606 601 557 596 580 601
#> GSM605 GSM599 GSM606 GSM600 GSM607 GSM601 GSM608 GSM602 GSM609
#> 1 45 99 1 109 38 87 18 72 16
#> 2 26 18 13 25 32 28 40 14 41
#> 3 23 NA 29 9 25 11 40 NA 22
#> 4 144 239 148 211 139 208 16 174 15
#> 5 NA NA NA NA NA NA NA NA NA
#> 6 554 562 561 580 568 519 562 497 564
head(columns(gds))
#> labelDescription subset_dataset_id
#> ID_REF Platform reference identifier <NA>
#> IDENTIFIER identifier <NA>
#> GSM582 Value for GSM582: NOD_S1; src: Spleen GDS10; GDS10; GDS10
#> GSM589 Value for GSM589: NOD_S2; src: Spleen GDS10; GDS10; GDS10
#> GSM583 Value for GSM583: Idd3_S1; src: Spleen GDS10; GDS10; GDS10
#> GSM590 Value for GSM590: Idd3_S2; src: Spleen GDS10; GDS10; GDS10
#> subset_description subset_type
#> ID_REF <NA> <NA>
#> IDENTIFIER <NA> <NA>
#> GSM582 spleen; NOD; diabetic tissue; strain; disease state
#> GSM589 spleen; NOD; diabetic tissue; strain; disease state
#> GSM583 spleen; Idd3; diabetic-resistant tissue; strain; disease state
#> GSM590 spleen; Idd3; diabetic-resistant tissue; strain; disease stateFor GSE entity, there is also a soft file associated with it. But the structure is different with GPL, GSM and GDS entity, geokit provide GEOSeries class to keep contents in GSE soft file. Actually, a GSE soft file contains almost all contents in its subsets soft file including both GPL and GSM, so GEOSeries class provides both gpl and gsm slots as a list of GEOSoft. To download GSE soft file, we just set gse_matrix to FALSE in geo function.
gse <- geo("GSE10", odir = tempdir(), gse_matrix = FALSE)
#> Downloading 1 GSE soft file from FTP site
gse
#> An object of GEOSeries
#> gsm: GSM571 GSM572 GSM573 GSM574
#> gpl: GPL4
#> meta: Database_email Database_institute Database_name ... Series_title
#> Series_type (31 total)
#> accession: GSE10It’s more common to use a series matrix file in our usual analysis workflow, we can also handle it easily in geokit, as what we need to do is just set gse_matrix to TRUE in geo function, which is also the default value. When gse_matrix is TRUE, geo will return a ExpressionSet object which can interact with lots of Bioconductor packages. There are two parameters controling the processing details when parsing series matrix file. When parsing phenoData from series matrix files directly, it’s common to fail to discern characteristics_ch* columns, which contain the important traits informations of corresponding samples, since many characteristics_ch* columns in series matrix files often lacks separate strings. pdata_from_soft, which indicates whether retrieve phenoData from GEO Soft file, can help handle this problem well. When the soft file is large and we don’t want to use it, we can set pdata_from_soft to FALSE and use parse_pdata function to parse it manully. Another important parameter is add_gpl, where FALSE indicates geo will try to map the current GPL accession id into a Bioconductor annotation package, then we can use the latest bioconductor annotation package to get the up-to-date featureData, otherwise, geo will add featureData from GPL soft file directly.
gse_matix <- geo("GSE10", odir = tempdir())
#> Downloading 1 GSE matrix file from FTP site
#> Finding 1 {.strong GSE} {.field soft} file already downloaded:
#> 'GSE10_family.soft.gz'
#> → Parsing series soft file 'GSE10_family.soft.gz'
#>
#> ✔ Parsing 1 series soft file successfully!
#>
#> → Parsing 1 series matrix file of GSE10
#>
#> ✔ Parsing 1 GSE series matrix successfully!
#>
#> → Constructing <ExpressionSet>
#>
#> ✔ Found Bioconductor annotation package for "GPL4"
#>
#> Downloading 1 GPL annot file from FTP site
#> ℹ annot file in FTP site for "GPL4" is not available, so will use data amount file from GEO Accession Site instead
#>
#> Downloading 1 GPL data amount file from GEO Accession Site
gse_matix
#> ExpressionSet (storageMode: lockedEnvironment)
#> assayData: 96903 features, 4 samples
#> element names: exprs
#> protocolData: none
#> phenoData
#> sampleNames: GSM571 GSM572 GSM573 GSM574
#> varLabels: anchor channel_count ... type (32 total)
#> varMetadata: labelDescription
#> featureData
#> featureNames: AAAAAAAAAA AAAAAAAAAC ... TTTTTTTTTT (96903 total)
#> fvarLabels: TAG GI
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> pubMedIds: 11756676
#> Annotation: GPL4
gse_matrix_with_pdata <- geo(
"gse53987",
odir = tempdir(),
pdata_from_soft = FALSE,
add_gpl = FALSE
)
#> Downloading 1 GSE matrix file from FTP site
#> → Parsing 1 series matrix file of GSE53987
#> Warning: Cannot parse characteristic column correctly
#> ℹ Details see "characteristics_ch1" column in phenoData
#> ℹ Please use `parse_pdata()` or `parse_gsm_list()` function to convert it
#> manually if necessary!
#> ✔ Parsing 1 GSE series matrix successfully!
#> → Constructing <ExpressionSet>
#> ✔ Found Bioconductor annotation package for "GPL570"
gse_matrix_smp_info <- Biobase::pData(gse_matrix_with_pdata)
gse_matrix_smp_info$characteristics_ch1 <- stringr::str_replace_all(
gse_matrix_smp_info$characteristics_ch1,
"gender|race|pmi|ph|rin|tissue|disease state",
function(x) paste0("; ", x)
)
gse_matrix_smp_info <- parse_pdata(gse_matrix_smp_info)
gse_matrix_smp_info[grepl(
"^ch1_|characteristics_ch1", names(gse_matrix_smp_info)
)]
#> ch1_age ch1_gender ch1_race ch1_pmi ch1_ph ch1_rin
#> GSM1304852 52 M W 23.50 6.70 6.3
#> GSM1304853 50 F W 11.70 6.40 6.8
#> GSM1304854 28 F W 22.30 6.30 7.7
#> GSM1304855 55 F W 17.50 6.40 7.6
#> GSM1304856 58 M W 27.70 6.80 7.0
#> GSM1304857 28 M W 27.40 6.20 7.7
#> GSM1304858 49 F W 21.50 6.70 8.2
#> GSM1304859 42 F W 31.20 6.50 5.6
#> GSM1304860 43 F W 31.90 6.70 6.3
#> GSM1304861 50 M W 12.10 6.70 7.4
#> GSM1304862 40 M W 18.50 6.40 6.5
#> GSM1304863 39 F W 22.20 6.70 7.9
#> GSM1304864 45 M W 27.20 7.10 8.1
#> GSM1304865 42 M W 12.50 6.70 8.2
#> GSM1304866 65 M W 8.90 6.70 6.6
#> GSM1304867 51 F W 21.50 6.70 7.0
#> GSM1304868 39 M W 24.20 6.60 7.8
#> GSM1304869 48 M W 18.10 6.90 7.0
#> GSM1304870 51 M W 24.20 6.60 7.8
#> GSM1304871 51 F W 7.80 6.60 7.2
#> GSM1304872 36 F W 14.50 6.40 8.0
#> GSM1304873 65 F W 18.50 6.50 7.0
#> GSM1304874 55 M W 28.00 6.10 6.8
#> GSM1304875 22 M W 20.10 6.80 7.1
#> GSM1304876 52 F W 22.60 7.10 7.0
#> GSM1304877 58 F W 22.70 6.40 6.3
#> GSM1304878 40 F B 16.60 6.80 7.9
#> GSM1304879 41 F W 15.40 6.60 8.5
#> GSM1304880 49 M W 21.20 6.50 7.8
#> GSM1304881 48 M W 21.68 6.60 7.3
#> GSM1304882 39 F W 24.50 6.80 8.2
#> GSM1304883 48 M W 24.50 6.50 7.0
#> GSM1304884 43 M W 13.80 6.60 7.6
#> GSM1304885 68 M W 11.80 6.80 6.1
#> GSM1304886 58 F W 18.80 6.60 7.2
#> GSM1304887 43 M W 22.30 6.70 7.9
#> GSM1304888 51 M W 24.60 6.50 7.7
#> GSM1304889 53 F W 11.90 6.70 8.1
#> GSM1304890 26 F W 13.40 6.40 7.5
#> GSM1304891 52 F W 10.30 6.50 6.6
#> GSM1304892 62 M W 26.00 6.50 6.8
#> GSM1304893 29 M W 26.60 6.90 7.8
#> GSM1304894 49 F W 23.40 6.40 6.2
#> GSM1304895 54 F W 17.90 6.20 6.1
#> GSM1304896 28 F B 24.80 6.60 8.2
#> GSM1304897 42 M W 14.30 6.40 6.2
#> GSM1304898 44 M W 19.30 6.50 6.3
#> GSM1304899 40 F W 22.20 6.60 8.0
#> GSM1304900 47 M W 24.00 6.60 5.5
#> GSM1304901 59 M W 13.00 6.60 7.2
#> GSM1304902 47 F W 22.30 6.60 6.5
#> GSM1304903 34 M W 24.40 6.60 8.4
#> GSM1304904 51 M W 28.30 7.30 7.0
#> GSM1304905 49 M W 21.50 5.97 6.0
#> GSM1304906 47 F W 14.37 6.35 6.3
#> GSM1304907 25 F B 20.10 6.73 5.6
#> GSM1304908 62 M W 22.70 7.14 6.3
#> GSM1304909 44 F W 24.50 6.63 7.8
#> GSM1304910 46 F W 23.80 6.61 6.9
#> GSM1304911 50 M W 11.00 6.23 7.2
#> GSM1304912 46 M W 15.80 6.19 6.2
#> GSM1304913 41 F W 20.10 6.27 6.7
#> GSM1304914 47 M W 28.90 6.58 6.7
#> GSM1304915 37 M B 5.98 6.07 6.4
#> GSM1304916 58 M W 7.70 6.22 6.7
#> GSM1304917 44 F B 18.70 6.20 6.4
#> GSM1304918 38 M W 28.80 6.56 6.6
#> GSM1304919 52 M B 27.10 6.68 6.3
#> GSM1304920 52 M W 23.50 6.70 7.2
#> GSM1304921 50 F W 11.70 6.40 8.6
#> GSM1304922 28 F W 22.30 6.30 8.6
#> GSM1304923 55 F W 17.50 6.40 8.0
#> GSM1304924 58 M W 27.70 6.80 7.5
#> GSM1304925 28 M W 27.40 6.20 7.9
#> GSM1304926 49 F W 21.50 6.70 8.1
#> GSM1304927 56 F W 24.50 6.10 6.9
#> GSM1304928 50 M W 12.10 6.70 7.6
#> GSM1304929 40 M W 18.50 6.40 7.9
#> GSM1304930 39 F W 22.20 6.70 7.8
#> GSM1304931 45 M W 27.20 7.10 7.3
#> GSM1304932 42 M W 12.50 6.70 7.6
#> GSM1304933 65 M W 8.90 6.70 6.9
#> GSM1304934 51 F W 21.50 6.70 7.7
#> GSM1304935 39 M W 24.20 6.60 7.3
#> GSM1304936 48 M W 18.10 6.90 8.2
#> GSM1304937 51 M W 24.20 6.60 7.9
#> GSM1304938 51 F W 7.80 6.60 8.6
#> GSM1304939 36 F W 14.50 6.40 8.6
#> GSM1304940 65 F W 18.50 6.50 8.3
#> GSM1304941 55 M W 28.00 6.10 7.9
#> GSM1304942 22 M W 20.10 6.80 8.1
#> GSM1304943 52 F W 22.60 7.10 8.2
#> GSM1304944 58 F W 22.70 6.40 8.0
#> GSM1304945 40 F B 16.60 6.80 8.2
#> GSM1304946 41 F W 15.40 6.60 8.2
#> GSM1304947 49 M W 21.20 6.50 7.9
#> GSM1304948 48 M W 21.68 6.60 7.5
#> GSM1304949 39 F W 24.50 6.80 7.4
#> GSM1304950 48 M W 24.50 6.50 6.8
#> GSM1304951 43 M W 13.80 6.60 7.5
#> GSM1304952 68 M W 11.80 6.80 6.7
#> GSM1304953 58 F W 18.80 6.60 8.7
#> GSM1304954 43 M W 22.30 6.70 8.0
#> GSM1304955 46 M W 22.00 6.30 6.6
#> GSM1304956 51 M W 24.60 6.50 7.8
#> GSM1304957 53 F W 11.90 6.70 8.4
#> GSM1304958 26 F W 13.40 6.40 8.4
#> GSM1304959 52 F W 10.30 6.50 8.1
#> GSM1304960 62 M W 26.00 6.50 7.8
#> GSM1304961 29 M W 26.60 6.90 8.2
#> GSM1304962 49 F W 23.40 6.40 7.6
#> GSM1304963 54 F W 17.90 6.20 7.5
#> GSM1304964 28 F B 24.80 6.60 7.9
#> GSM1304965 42 M W 14.30 6.40 8.4
#> GSM1304966 40 F W 22.20 6.60 7.7
#> GSM1304967 47 M W 24.00 6.60 6.8
#> GSM1304968 44 M W 11.00 6.50 7.2
#> GSM1304969 59 M W 13.00 6.60 7.7
#> GSM1304970 47 F W 22.30 6.60 6.7
#> GSM1304971 34 M W 24.40 6.60 7.8
#> GSM1304972 51 M W 28.30 7.30 7.7
#> GSM1304973 49 M W 21.50 5.97 7.0
#> GSM1304974 47 F W 14.37 6.35 9.0
#> GSM1304975 25 F B 20.10 6.73 7.2
#> GSM1304976 41 F W 17.10 6.90 8.3
#> GSM1304977 62 M W 22.70 7.14 8.1
#> GSM1304978 47 F B 20.10 7.30 8.1
#> GSM1304979 44 F W 24.50 6.63 7.6
#> GSM1304980 46 F W 23.80 6.61 8.0
#> GSM1304981 50 M W 11.00 6.23 8.4
#> GSM1304982 41 F W 20.10 6.27 7.4
#> GSM1304983 47 M W 28.90 6.58 7.0
#> GSM1304984 37 M B 5.98 6.07 6.3
#> GSM1304985 58 M W 7.70 6.22 7.3
#> GSM1304986 44 F B 18.70 6.20 7.6
#> GSM1304987 52 M B 27.10 6.68 7.4
#> GSM1304988 50 M W 12.10 6.70 8.6
#> GSM1304989 40 M W 18.50 6.40 8.4
#> GSM1304990 39 F W 22.20 6.70 9.1
#> GSM1304991 45 M W 27.20 7.10 8.7
#> GSM1304992 42 M W 12.50 6.70 8.7
#> GSM1304993 65 M W 8.90 6.70 8.3
#> GSM1304994 51 F W 21.50 6.70 8.4
#> GSM1304995 39 M W 24.20 6.60 8.5
#> GSM1304996 48 M W 18.10 6.90 8.8
#> GSM1304997 52 M W 23.50 6.70 9.1
#> GSM1304998 50 F W 11.70 6.40 8.4
#> GSM1304999 28 F W 22.30 6.30 9.0
#> GSM1305000 55 F W 17.50 6.40 6.0
#> GSM1305001 58 M W 27.70 6.80 6.6
#> GSM1305002 49 F W 21.50 6.70 8.7
#> GSM1305003 56 F W 24.50 6.10 7.7
#> GSM1305004 42 F W 31.20 6.50 6.8
#> GSM1305005 49 M W 21.20 6.50 8.4
#> GSM1305006 48 M W 21.68 6.60 7.5
#> GSM1305007 39 F W 24.50 6.80 7.5
#> GSM1305008 48 M W 24.50 6.50 7.6
#> GSM1305009 43 M W 13.80 6.60 8.7
#> GSM1305010 68 M W 11.80 6.80 8.5
#> GSM1305011 58 F W 18.80 6.60 8.6
#> GSM1305012 43 M W 22.30 6.70 8.5
#> GSM1305013 46 M W 22.00 6.30 7.0
#> GSM1305014 51 M W 24.20 6.60 8.3
#> GSM1305015 51 F W 7.80 6.60 9.0
#> GSM1305016 36 F W 14.50 6.40 9.3
#> GSM1305017 65 F W 18.50 6.50 7.4
#> GSM1305018 55 M W 28.00 6.10 7.6
#> GSM1305019 22 M W 20.10 6.80 7.4
#> GSM1305020 52 F W 22.60 7.10 8.8
#> GSM1305021 58 F W 22.70 6.40 9.0
#> GSM1305022 40 F B 16.60 6.80 8.7
#> GSM1305023 42 M W 14.30 6.40 8.7
#> GSM1305024 44 M W 19.30 6.50 8.5
#> GSM1305025 47 M W 24.00 6.60 7.3
#> GSM1305026 44 M W 11.00 6.50 7.7
#> GSM1305027 59 M W 13.00 6.60 8.4
#> GSM1305028 47 F W 22.30 6.60 8.2
#> GSM1305029 34 M W 24.40 6.60 9.1
#> GSM1305030 51 M W 28.30 7.30 8.6
#> GSM1305031 51 M W 24.60 6.50 8.3
#> GSM1305032 53 F W 11.90 6.70 8.8
#> GSM1305033 26 F W 13.40 6.40 9.2
#> GSM1305034 52 F W 10.30 6.50 6.7
#> GSM1305035 62 M W 26.00 6.50 7.5
#> GSM1305036 29 M W 26.60 6.90 9.2
#> GSM1305037 49 F W 23.40 6.40 6.7
#> GSM1305038 54 F W 17.90 6.20 9.0
#> GSM1305039 50 M W 11.00 6.23 8.5
#> GSM1305040 46 M W 15.80 6.19 7.8
#> GSM1305041 41 F W 20.10 6.27 8.6
#> GSM1305042 47 M W 28.90 6.58 8.4
#> GSM1305043 37 M B 5.98 6.07 6.9
#> GSM1305044 58 M W 7.70 6.22 6.7
#> GSM1305045 44 F B 18.70 6.20 6.9
#> GSM1305046 38 M W 28.80 6.56 6.8
#> GSM1305047 52 M B 27.10 6.68 8.5
#> GSM1305048 49 M W 21.50 5.97 8.4
#> GSM1305049 47 F W 14.37 6.35 8.9
#> GSM1305050 25 F B 20.10 6.73 7.3
#> GSM1305051 41 F W 17.10 6.90 7.3
#> GSM1305052 62 M W 22.70 7.14 7.8
#> GSM1305053 32 M W 30.80 6.18 7.1
#> GSM1305054 47 F B 20.10 7.30 8.8
#> GSM1305055 50 F B 22.90 6.25 8.0
#> GSM1305056 44 F W 24.50 6.63 9.0
#> ch1_tissue ch1_disease state
#> GSM1304852 hippocampus bipolar disorder
#> GSM1304853 hippocampus bipolar disorder
#> GSM1304854 hippocampus bipolar disorder
#> GSM1304855 hippocampus bipolar disorder
#> GSM1304856 hippocampus bipolar disorder
#> GSM1304857 hippocampus bipolar disorder
#> GSM1304858 hippocampus bipolar disorder
#> GSM1304859 hippocampus bipolar disorder
#> GSM1304860 hippocampus bipolar disorder
#> GSM1304861 hippocampus bipolar disorder
#> GSM1304862 hippocampus bipolar disorder
#> GSM1304863 hippocampus bipolar disorder
#> GSM1304864 hippocampus bipolar disorder
#> GSM1304865 hippocampus bipolar disorder
#> GSM1304866 hippocampus bipolar disorder
#> GSM1304867 hippocampus bipolar disorder
#> GSM1304868 hippocampus bipolar disorder
#> GSM1304869 hippocampus bipolar disorder
#> GSM1304870 hippocampus control
#> GSM1304871 hippocampus control
#> GSM1304872 hippocampus control
#> GSM1304873 hippocampus control
#> GSM1304874 hippocampus control
#> GSM1304875 hippocampus control
#> GSM1304876 hippocampus control
#> GSM1304877 hippocampus control
#> GSM1304878 hippocampus control
#> GSM1304879 hippocampus control
#> GSM1304880 hippocampus control
#> GSM1304881 hippocampus control
#> GSM1304882 hippocampus control
#> GSM1304883 hippocampus control
#> GSM1304884 hippocampus control
#> GSM1304885 hippocampus control
#> GSM1304886 hippocampus control
#> GSM1304887 hippocampus control
#> GSM1304888 hippocampus major depressive disorder
#> GSM1304889 hippocampus major depressive disorder
#> GSM1304890 hippocampus major depressive disorder
#> GSM1304891 hippocampus major depressive disorder
#> GSM1304892 hippocampus major depressive disorder
#> GSM1304893 hippocampus major depressive disorder
#> GSM1304894 hippocampus major depressive disorder
#> GSM1304895 hippocampus major depressive disorder
#> GSM1304896 hippocampus major depressive disorder
#> GSM1304897 hippocampus major depressive disorder
#> GSM1304898 hippocampus major depressive disorder
#> GSM1304899 hippocampus major depressive disorder
#> GSM1304900 hippocampus major depressive disorder
#> GSM1304901 hippocampus major depressive disorder
#> GSM1304902 hippocampus major depressive disorder
#> GSM1304903 hippocampus major depressive disorder
#> GSM1304904 hippocampus major depressive disorder
#> GSM1304905 hippocampus schizo
#> GSM1304906 hippocampus schizo
#> GSM1304907 hippocampus schizo
#> GSM1304908 hippocampus schizo
#> GSM1304909 hippocampus schizo
#> GSM1304910 hippocampus schizo
#> GSM1304911 hippocampus schizo
#> GSM1304912 hippocampus schizo
#> GSM1304913 hippocampus schizo
#> GSM1304914 hippocampus schizo
#> GSM1304915 hippocampus schizo
#> GSM1304916 hippocampus schizo
#> GSM1304917 hippocampus schizo
#> GSM1304918 hippocampus schizo
#> GSM1304919 hippocampus schizo
#> GSM1304920 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304921 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304922 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304923 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304924 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304925 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304926 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304927 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304928 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304929 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304930 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304931 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304932 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304933 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304934 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304935 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304936 Pre-frontal cortex (BA46) bipolar disorder
#> GSM1304937 Pre-frontal cortex (BA46) control
#> GSM1304938 Pre-frontal cortex (BA46) control
#> GSM1304939 Pre-frontal cortex (BA46) control
#> GSM1304940 Pre-frontal cortex (BA46) control
#> GSM1304941 Pre-frontal cortex (BA46) control
#> GSM1304942 Pre-frontal cortex (BA46) control
#> GSM1304943 Pre-frontal cortex (BA46) control
#> GSM1304944 Pre-frontal cortex (BA46) control
#> GSM1304945 Pre-frontal cortex (BA46) control
#> GSM1304946 Pre-frontal cortex (BA46) control
#> GSM1304947 Pre-frontal cortex (BA46) control
#> GSM1304948 Pre-frontal cortex (BA46) control
#> GSM1304949 Pre-frontal cortex (BA46) control
#> GSM1304950 Pre-frontal cortex (BA46) control
#> GSM1304951 Pre-frontal cortex (BA46) control
#> GSM1304952 Pre-frontal cortex (BA46) control
#> GSM1304953 Pre-frontal cortex (BA46) control
#> GSM1304954 Pre-frontal cortex (BA46) control
#> GSM1304955 Pre-frontal cortex (BA46) control
#> GSM1304956 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304957 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304958 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304959 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304960 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304961 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304962 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304963 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304964 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304965 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304966 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304967 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304968 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304969 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304970 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304971 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304972 Pre-frontal cortex (BA46) major depressive disorder
#> GSM1304973 Pre-frontal cortex (BA46) schizo
#> GSM1304974 Pre-frontal cortex (BA46) schizo
#> GSM1304975 Pre-frontal cortex (BA46) schizo
#> GSM1304976 Pre-frontal cortex (BA46) schizo
#> GSM1304977 Pre-frontal cortex (BA46) schizo
#> GSM1304978 Pre-frontal cortex (BA46) schizo
#> GSM1304979 Pre-frontal cortex (BA46) schizo
#> GSM1304980 Pre-frontal cortex (BA46) schizo
#> GSM1304981 Pre-frontal cortex (BA46) schizo
#> GSM1304982 Pre-frontal cortex (BA46) schizo
#> GSM1304983 Pre-frontal cortex (BA46) schizo
#> GSM1304984 Pre-frontal cortex (BA46) schizo
#> GSM1304985 Pre-frontal cortex (BA46) schizo
#> GSM1304986 Pre-frontal cortex (BA46) schizo
#> GSM1304987 Pre-frontal cortex (BA46) schizo
#> GSM1304988 Associative striatum bipolar disorder
#> GSM1304989 Associative striatum bipolar disorder
#> GSM1304990 Associative striatum bipolar disorder
#> GSM1304991 Associative striatum bipolar disorder
#> GSM1304992 Associative striatum bipolar disorder
#> GSM1304993 Associative striatum bipolar disorder
#> GSM1304994 Associative striatum bipolar disorder
#> GSM1304995 Associative striatum bipolar disorder
#> GSM1304996 Associative striatum bipolar disorder
#> GSM1304997 Associative striatum bipolar disorder
#> GSM1304998 Associative striatum bipolar disorder
#> GSM1304999 Associative striatum bipolar disorder
#> GSM1305000 Associative striatum bipolar disorder
#> GSM1305001 Associative striatum bipolar disorder
#> GSM1305002 Associative striatum bipolar disorder
#> GSM1305003 Associative striatum bipolar disorder
#> GSM1305004 Associative striatum bipolar disorder
#> GSM1305005 Associative striatum control
#> GSM1305006 Associative striatum control
#> GSM1305007 Associative striatum control
#> GSM1305008 Associative striatum control
#> GSM1305009 Associative striatum control
#> GSM1305010 Associative striatum control
#> GSM1305011 Associative striatum control
#> GSM1305012 Associative striatum control
#> GSM1305013 Associative striatum control
#> GSM1305014 Associative striatum control
#> GSM1305015 Associative striatum control
#> GSM1305016 Associative striatum control
#> GSM1305017 Associative striatum control
#> GSM1305018 Associative striatum control
#> GSM1305019 Associative striatum control
#> GSM1305020 Associative striatum control
#> GSM1305021 Associative striatum control
#> GSM1305022 Associative striatum control
#> GSM1305023 Associative striatum major depressive disorder
#> GSM1305024 Associative striatum major depressive disorder
#> GSM1305025 Associative striatum major depressive disorder
#> GSM1305026 Associative striatum major depressive disorder
#> GSM1305027 Associative striatum major depressive disorder
#> GSM1305028 Associative striatum major depressive disorder
#> GSM1305029 Associative striatum major depressive disorder
#> GSM1305030 Associative striatum major depressive disorder
#> GSM1305031 Associative striatum major depressive disorder
#> GSM1305032 Associative striatum major depressive disorder
#> GSM1305033 Associative striatum major depressive disorder
#> GSM1305034 Associative striatum major depressive disorder
#> GSM1305035 Associative striatum major depressive disorder
#> GSM1305036 Associative striatum major depressive disorder
#> GSM1305037 Associative striatum major depressive disorder
#> GSM1305038 Associative striatum major depressive disorder
#> GSM1305039 Associative striatum schizo
#> GSM1305040 Associative striatum schizo
#> GSM1305041 Associative striatum schizo
#> GSM1305042 Associative striatum schizo
#> GSM1305043 Associative striatum schizo
#> GSM1305044 Associative striatum schizo
#> GSM1305045 Associative striatum schizo
#> GSM1305046 Associative striatum schizo
#> GSM1305047 Associative striatum schizo
#> GSM1305048 Associative striatum schizo
#> GSM1305049 Associative striatum schizo
#> GSM1305050 Associative striatum schizo
#> GSM1305051 Associative striatum schizo
#> GSM1305052 Associative striatum schizo
#> GSM1305053 Associative striatum schizo
#> GSM1305054 Associative striatum schizo
#> GSM1305055 Associative striatum schizo
#> GSM1305056 Associative striatum schizo
#> characteristics_ch1
#> GSM1304852 age: 52; gender: M; race: W; pmi: 23.5; ph: 6.7; rin: 6.3; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304853 age: 50; gender: F; race: W; pmi: 11.7; ph: 6.4; rin: 6.8; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304854 age: 28; gender: F; race: W; pmi: 22.3; ph: 6.3; rin: 7.7; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304855 age: 55; gender: F; race: W; pmi: 17.5; ph: 6.4; rin: 7.6; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304856 age: 58; gender: M; race: W; pmi: 27.7; ph: 6.8; rin: 7; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304857 age: 28; gender: M; race: W; pmi: 27.4; ph: 6.2; rin: 7.7; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304858 age: 49; gender: F; race: W; pmi: 21.5; ph: 6.7; rin: 8.2; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304859 age: 42; gender: F; race: W; pmi: 31.2; ph: 6.5; rin: 5.6; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304860 age: 43; gender: F; race: W; pmi: 31.9; ph: 6.7; rin: 6.3; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304861 age: 50; gender: M; race: W; pmi: 12.1; ph: 6.7; rin: 7.4; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304862 age: 40; gender: M; race: W; pmi: 18.5; ph: 6.4; rin: 6.5; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304863 age: 39; gender: F; race: W; pmi: 22.2; ph: 6.7; rin: 7.9; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304864 age: 45; gender: M; race: W; pmi: 27.2; ph: 7.1; rin: 8.1; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304865 age: 42; gender: M; race: W; pmi: 12.5; ph: 6.7; rin: 8.2; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304866 age: 65; gender: M; race: W; pmi: 8.9; ph: 6.7; rin: 6.6; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304867 age: 51; gender: F; race: W; pmi: 21.5; ph: 6.7; rin: 7; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304868 age: 39; gender: M; race: W; pmi: 24.2; ph: 6.6; rin: 7.8; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304869 age: 48; gender: M; race: W; pmi: 18.1; ph: 6.9; rin: 7; tissue: hippocampus; disease state: bipolar disorder
#> GSM1304870 age: 51; gender: M; race: W; pmi: 24.2; ph: 6.6; rin: 7.8; tissue: hippocampus; disease state: control
#> GSM1304871 age: 51; gender: F; race: W; pmi: 7.8; ph: 6.6; rin: 7.2; tissue: hippocampus; disease state: control
#> GSM1304872 age: 36; gender: F; race: W; pmi: 14.5; ph: 6.4; rin: 8; tissue: hippocampus; disease state: control
#> GSM1304873 age: 65; gender: F; race: W; pmi: 18.5; ph: 6.5; rin: 7; tissue: hippocampus; disease state: control
#> GSM1304874 age: 55; gender: M; race: W; pmi: 28; ph: 6.1; rin: 6.8; tissue: hippocampus; disease state: control
#> GSM1304875 age: 22; gender: M; race: W; pmi: 20.1; ph: 6.8; rin: 7.1; tissue: hippocampus; disease state: control
#> GSM1304876 age: 52; gender: F; race: W; pmi: 22.6; ph: 7.1; rin: 7; tissue: hippocampus; disease state: control
#> GSM1304877 age: 58; gender: F; race: W; pmi: 22.7; ph: 6.4; rin: 6.3; tissue: hippocampus; disease state: control
#> GSM1304878 age: 40; gender: F; race: B; pmi: 16.6; ph: 6.8; rin: 7.9; tissue: hippocampus; disease state: control
#> GSM1304879 age: 41; gender: F; race: W; pmi: 15.4; ph: 6.6; rin: 8.5; tissue: hippocampus; disease state: control
#> GSM1304880 age: 49; gender: M; race: W; pmi: 21.2; ph: 6.5; rin: 7.8; tissue: hippocampus; disease state: control
#> GSM1304881 age: 48; gender: M; race: W; pmi: 21.68; ph: 6.6; rin: 7.3; tissue: hippocampus; disease state: control
#> GSM1304882 age: 39; gender: F; race: W; pmi: 24.5; ph: 6.8; rin: 8.2; tissue: hippocampus; disease state: control
#> GSM1304883 age: 48; gender: M; race: W; pmi: 24.5; ph: 6.5; rin: 7; tissue: hippocampus; disease state: control
#> GSM1304884 age: 43; gender: M; race: W; pmi: 13.8; ph: 6.6; rin: 7.6; tissue: hippocampus; disease state: control
#> GSM1304885 age: 68; gender: M; race: W; pmi: 11.8; ph: 6.8; rin: 6.1; tissue: hippocampus; disease state: control
#> GSM1304886 age: 58; gender: F; race: W; pmi: 18.8; ph: 6.6; rin: 7.2; tissue: hippocampus; disease state: control
#> GSM1304887 age: 43; gender: M; race: W; pmi: 22.3; ph: 6.7; rin: 7.9; tissue: hippocampus; disease state: control
#> GSM1304888 age: 51; gender: M; race: W; pmi: 24.6; ph: 6.5; rin: 7.7; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304889 age: 53; gender: F; race: W; pmi: 11.9; ph: 6.7; rin: 8.1; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304890 age: 26; gender: F; race: W; pmi: 13.4; ph: 6.4; rin: 7.5; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304891 age: 52; gender: F; race: W; pmi: 10.3; ph: 6.5; rin: 6.6; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304892 age: 62; gender: M; race: W; pmi: 26; ph: 6.5; rin: 6.8; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304893 age: 29; gender: M; race: W; pmi: 26.6; ph: 6.9; rin: 7.8; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304894 age: 49; gender: F; race: W; pmi: 23.4; ph: 6.4; rin: 6.2; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304895 age: 54; gender: F; race: W; pmi: 17.9; ph: 6.2; rin: 6.1; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304896 age: 28; gender: F; race: B; pmi: 24.8; ph: 6.6; rin: 8.2; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304897 age: 42; gender: M; race: W; pmi: 14.3; ph: 6.4; rin: 6.2; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304898 age: 44; gender: M; race: W; pmi: 19.3; ph: 6.5; rin: 6.3; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304899 age: 40; gender: F; race: W; pmi: 22.2; ph: 6.6; rin: 8; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304900 age: 47; gender: M; race: W; pmi: 24; ph: 6.6; rin: 5.5; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304901 age: 59; gender: M; race: W; pmi: 13; ph: 6.6; rin: 7.2; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304902 age: 47; gender: F; race: W; pmi: 22.3; ph: 6.6; rin: 6.5; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304903 age: 34; gender: M; race: W; pmi: 24.4; ph: 6.6; rin: 8.4; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304904 age: 51; gender: M; race: W; pmi: 28.3; ph: 7.3; rin: 7; tissue: hippocampus; disease state: major depressive disorder
#> GSM1304905 age: 49; gender: M; race: W; pmi: 21.5; ph: 5.97; rin: 6; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304906 age: 47; gender: F; race: W; pmi: 14.37; ph: 6.35; rin: 6.3; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304907 age: 25; gender: F; race: B; pmi: 20.1; ph: 6.73; rin: 5.6; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304908 age: 62; gender: M; race: W; pmi: 22.7; ph: 7.14; rin: 6.3; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304909 age: 44; gender: F; race: W; pmi: 24.5; ph: 6.63; rin: 7.8; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304910 age: 46; gender: F; race: W; pmi: 23.8; ph: 6.61; rin: 6.9; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304911 age: 50; gender: M; race: W; pmi: 11; ph: 6.23; rin: 7.2; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304912 age: 46; gender: M; race: W; pmi: 15.8; ph: 6.19; rin: 6.2; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304913 age: 41; gender: F; race: W; pmi: 20.1; ph: 6.27; rin: 6.7; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304914 age: 47; gender: M; race: W; pmi: 28.9; ph: 6.58; rin: 6.7; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304915 age: 37; gender: M; race: B; pmi: 5.98; ph: 6.07; rin: 6.4; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304916 age: 58; gender: M; race: W; pmi: 7.7; ph: 6.22; rin: 6.7; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304917 age: 44; gender: F; race: B; pmi: 18.7; ph: 6.2; rin: 6.4; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304918 age: 38; gender: M; race: W; pmi: 28.8; ph: 6.56; rin: 6.6; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304919 age: 52; gender: M; race: B; pmi: 27.1; ph: 6.68; rin: 6.3; tissue: hippocampus; disease state: schizo; phrenia
#> GSM1304920 age: 52; gender: M; race: W; pmi: 23.5; ph: 6.7; rin: 7.2; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304921 age: 50; gender: F; race: W; pmi: 11.7; ph: 6.4; rin: 8.6; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304922 age: 28; gender: F; race: W; pmi: 22.3; ph: 6.3; rin: 8.6; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304923 age: 55; gender: F; race: W; pmi: 17.5; ph: 6.4; rin: 8; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304924 age: 58; gender: M; race: W; pmi: 27.7; ph: 6.8; rin: 7.5; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304925 age: 28; gender: M; race: W; pmi: 27.4; ph: 6.2; rin: 7.9; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304926 age: 49; gender: F; race: W; pmi: 21.5; ph: 6.7; rin: 8.1; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304927 age: 56; gender: F; race: W; pmi: 24.5; ph: 6.1; rin: 6.9; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304928 age: 50; gender: M; race: W; pmi: 12.1; ph: 6.7; rin: 7.6; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304929 age: 40; gender: M; race: W; pmi: 18.5; ph: 6.4; rin: 7.9; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304930 age: 39; gender: F; race: W; pmi: 22.2; ph: 6.7; rin: 7.8; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304931 age: 45; gender: M; race: W; pmi: 27.2; ph: 7.1; rin: 7.3; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304932 age: 42; gender: M; race: W; pmi: 12.5; ph: 6.7; rin: 7.6; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304933 age: 65; gender: M; race: W; pmi: 8.9; ph: 6.7; rin: 6.9; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304934 age: 51; gender: F; race: W; pmi: 21.5; ph: 6.7; rin: 7.7; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304935 age: 39; gender: M; race: W; pmi: 24.2; ph: 6.6; rin: 7.3; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304936 age: 48; gender: M; race: W; pmi: 18.1; ph: 6.9; rin: 8.2; tissue: Pre-frontal cortex (BA46); disease state: bipolar disorder
#> GSM1304937 age: 51; gender: M; race: W; pmi: 24.2; ph: 6.6; rin: 7.9; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304938 age: 51; gender: F; race: W; pmi: 7.8; ph: 6.6; rin: 8.6; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304939 age: 36; gender: F; race: W; pmi: 14.5; ph: 6.4; rin: 8.6; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304940 age: 65; gender: F; race: W; pmi: 18.5; ph: 6.5; rin: 8.3; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304941 age: 55; gender: M; race: W; pmi: 28; ph: 6.1; rin: 7.9; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304942 age: 22; gender: M; race: W; pmi: 20.1; ph: 6.8; rin: 8.1; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304943 age: 52; gender: F; race: W; pmi: 22.6; ph: 7.1; rin: 8.2; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304944 age: 58; gender: F; race: W; pmi: 22.7; ph: 6.4; rin: 8; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304945 age: 40; gender: F; race: B; pmi: 16.6; ph: 6.8; rin: 8.2; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304946 age: 41; gender: F; race: W; pmi: 15.4; ph: 6.6; rin: 8.2; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304947 age: 49; gender: M; race: W; pmi: 21.2; ph: 6.5; rin: 7.9; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304948 age: 48; gender: M; race: W; pmi: 21.68; ph: 6.6; rin: 7.5; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304949 age: 39; gender: F; race: W; pmi: 24.5; ph: 6.8; rin: 7.4; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304950 age: 48; gender: M; race: W; pmi: 24.5; ph: 6.5; rin: 6.8; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304951 age: 43; gender: M; race: W; pmi: 13.8; ph: 6.6; rin: 7.5; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304952 age: 68; gender: M; race: W; pmi: 11.8; ph: 6.8; rin: 6.7; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304953 age: 58; gender: F; race: W; pmi: 18.8; ph: 6.6; rin: 8.7; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304954 age: 43; gender: M; race: W; pmi: 22.3; ph: 6.7; rin: 8; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304955 age: 46; gender: M; race: W; pmi: 22; ph: 6.3; rin: 6.6; tissue: Pre-frontal cortex (BA46); disease state: control
#> GSM1304956 age: 51; gender: M; race: W; pmi: 24.6; ph: 6.5; rin: 7.8; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304957 age: 53; gender: F; race: W; pmi: 11.9; ph: 6.7; rin: 8.4; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304958 age: 26; gender: F; race: W; pmi: 13.4; ph: 6.4; rin: 8.4; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304959 age: 52; gender: F; race: W; pmi: 10.3; ph: 6.5; rin: 8.1; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304960 age: 62; gender: M; race: W; pmi: 26; ph: 6.5; rin: 7.8; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304961 age: 29; gender: M; race: W; pmi: 26.6; ph: 6.9; rin: 8.2; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304962 age: 49; gender: F; race: W; pmi: 23.4; ph: 6.4; rin: 7.6; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304963 age: 54; gender: F; race: W; pmi: 17.9; ph: 6.2; rin: 7.5; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304964 age: 28; gender: F; race: B; pmi: 24.8; ph: 6.6; rin: 7.9; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304965 age: 42; gender: M; race: W; pmi: 14.3; ph: 6.4; rin: 8.4; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304966 age: 40; gender: F; race: W; pmi: 22.2; ph: 6.6; rin: 7.7; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304967 age: 47; gender: M; race: W; pmi: 24; ph: 6.6; rin: 6.8; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304968 age: 44; gender: M; race: W; pmi: 11; ph: 6.5; rin: 7.2; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304969 age: 59; gender: M; race: W; pmi: 13; ph: 6.6; rin: 7.7; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304970 age: 47; gender: F; race: W; pmi: 22.3; ph: 6.6; rin: 6.7; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304971 age: 34; gender: M; race: W; pmi: 24.4; ph: 6.6; rin: 7.8; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304972 age: 51; gender: M; race: W; pmi: 28.3; ph: 7.3; rin: 7.7; tissue: Pre-frontal cortex (BA46); disease state: major depressive disorder
#> GSM1304973 age: 49; gender: M; race: W; pmi: 21.5; ph: 5.97; rin: 7; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304974 age: 47; gender: F; race: W; pmi: 14.37; ph: 6.35; rin: 9; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304975 age: 25; gender: F; race: B; pmi: 20.1; ph: 6.73; rin: 7.2; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304976 age: 41; gender: F; race: W; pmi: 17.1; ph: 6.9; rin: 8.3; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304977 age: 62; gender: M; race: W; pmi: 22.7; ph: 7.14; rin: 8.1; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304978 age: 47; gender: F; race: B; pmi: 20.1; ph: 7.3; rin: 8.1; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304979 age: 44; gender: F; race: W; pmi: 24.5; ph: 6.63; rin: 7.6; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304980 age: 46; gender: F; race: W; pmi: 23.8; ph: 6.61; rin: 8; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304981 age: 50; gender: M; race: W; pmi: 11; ph: 6.23; rin: 8.4; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304982 age: 41; gender: F; race: W; pmi: 20.1; ph: 6.27; rin: 7.4; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304983 age: 47; gender: M; race: W; pmi: 28.9; ph: 6.58; rin: 7; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304984 age: 37; gender: M; race: B; pmi: 5.98; ph: 6.07; rin: 6.3; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304985 age: 58; gender: M; race: W; pmi: 7.7; ph: 6.22; rin: 7.3; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304986 age: 44; gender: F; race: B; pmi: 18.7; ph: 6.2; rin: 7.6; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304987 age: 52; gender: M; race: B; pmi: 27.1; ph: 6.68; rin: 7.4; tissue: Pre-frontal cortex (BA46); disease state: schizo; phrenia
#> GSM1304988 age: 50; gender: M; race: W; pmi: 12.1; ph: 6.7; rin: 8.6; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304989 age: 40; gender: M; race: W; pmi: 18.5; ph: 6.4; rin: 8.4; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304990 age: 39; gender: F; race: W; pmi: 22.2; ph: 6.7; rin: 9.1; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304991 age: 45; gender: M; race: W; pmi: 27.2; ph: 7.1; rin: 8.7; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304992 age: 42; gender: M; race: W; pmi: 12.5; ph: 6.7; rin: 8.7; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304993 age: 65; gender: M; race: W; pmi: 8.9; ph: 6.7; rin: 8.3; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304994 age: 51; gender: F; race: W; pmi: 21.5; ph: 6.7; rin: 8.4; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304995 age: 39; gender: M; race: W; pmi: 24.2; ph: 6.6; rin: 8.5; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304996 age: 48; gender: M; race: W; pmi: 18.1; ph: 6.9; rin: 8.8; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304997 age: 52; gender: M; race: W; pmi: 23.5; ph: 6.7; rin: 9.1; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304998 age: 50; gender: F; race: W; pmi: 11.7; ph: 6.4; rin: 8.4; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1304999 age: 28; gender: F; race: W; pmi: 22.3; ph: 6.3; rin: 9; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1305000 age: 55; gender: F; race: W; pmi: 17.5; ph: 6.4; rin: 6; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1305001 age: 58; gender: M; race: W; pmi: 27.7; ph: 6.8; rin: 6.6; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1305002 age: 49; gender: F; race: W; pmi: 21.5; ph: 6.7; rin: 8.7; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1305003 age: 56; gender: F; race: W; pmi: 24.5; ph: 6.1; rin: 7.7; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1305004 age: 42; gender: F; race: W; pmi: 31.2; ph: 6.5; rin: 6.8; tissue: Associative striatum; disease state: bipolar disorder
#> GSM1305005 age: 49; gender: M; race: W; pmi: 21.2; ph: 6.5; rin: 8.4; tissue: Associative striatum; disease state: control
#> GSM1305006 age: 48; gender: M; race: W; pmi: 21.68; ph: 6.6; rin: 7.5; tissue: Associative striatum; disease state: control
#> GSM1305007 age: 39; gender: F; race: W; pmi: 24.5; ph: 6.8; rin: 7.5; tissue: Associative striatum; disease state: control
#> GSM1305008 age: 48; gender: M; race: W; pmi: 24.5; ph: 6.5; rin: 7.6; tissue: Associative striatum; disease state: control
#> GSM1305009 age: 43; gender: M; race: W; pmi: 13.8; ph: 6.6; rin: 8.7; tissue: Associative striatum; disease state: control
#> GSM1305010 age: 68; gender: M; race: W; pmi: 11.8; ph: 6.8; rin: 8.5; tissue: Associative striatum; disease state: control
#> GSM1305011 age: 58; gender: F; race: W; pmi: 18.8; ph: 6.6; rin: 8.6; tissue: Associative striatum; disease state: control
#> GSM1305012 age: 43; gender: M; race: W; pmi: 22.3; ph: 6.7; rin: 8.5; tissue: Associative striatum; disease state: control
#> GSM1305013 age: 46; gender: M; race: W; pmi: 22; ph: 6.3; rin: 7; tissue: Associative striatum; disease state: control
#> GSM1305014 age: 51; gender: M; race: W; pmi: 24.2; ph: 6.6; rin: 8.3; tissue: Associative striatum; disease state: control
#> GSM1305015 age: 51; gender: F; race: W; pmi: 7.8; ph: 6.6; rin: 9; tissue: Associative striatum; disease state: control
#> GSM1305016 age: 36; gender: F; race: W; pmi: 14.5; ph: 6.4; rin: 9.3; tissue: Associative striatum; disease state: control
#> GSM1305017 age: 65; gender: F; race: W; pmi: 18.5; ph: 6.5; rin: 7.4; tissue: Associative striatum; disease state: control
#> GSM1305018 age: 55; gender: M; race: W; pmi: 28; ph: 6.1; rin: 7.6; tissue: Associative striatum; disease state: control
#> GSM1305019 age: 22; gender: M; race: W; pmi: 20.1; ph: 6.8; rin: 7.4; tissue: Associative striatum; disease state: control
#> GSM1305020 age: 52; gender: F; race: W; pmi: 22.6; ph: 7.1; rin: 8.8; tissue: Associative striatum; disease state: control
#> GSM1305021 age: 58; gender: F; race: W; pmi: 22.7; ph: 6.4; rin: 9; tissue: Associative striatum; disease state: control
#> GSM1305022 age: 40; gender: F; race: B; pmi: 16.6; ph: 6.8; rin: 8.7; tissue: Associative striatum; disease state: control
#> GSM1305023 age: 42; gender: M; race: W; pmi: 14.3; ph: 6.4; rin: 8.7; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305024 age: 44; gender: M; race: W; pmi: 19.3; ph: 6.5; rin: 8.5; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305025 age: 47; gender: M; race: W; pmi: 24; ph: 6.6; rin: 7.3; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305026 age: 44; gender: M; race: W; pmi: 11; ph: 6.5; rin: 7.7; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305027 age: 59; gender: M; race: W; pmi: 13; ph: 6.6; rin: 8.4; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305028 age: 47; gender: F; race: W; pmi: 22.3; ph: 6.6; rin: 8.2; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305029 age: 34; gender: M; race: W; pmi: 24.4; ph: 6.6; rin: 9.1; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305030 age: 51; gender: M; race: W; pmi: 28.3; ph: 7.3; rin: 8.6; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305031 age: 51; gender: M; race: W; pmi: 24.6; ph: 6.5; rin: 8.3; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305032 age: 53; gender: F; race: W; pmi: 11.9; ph: 6.7; rin: 8.8; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305033 age: 26; gender: F; race: W; pmi: 13.4; ph: 6.4; rin: 9.2; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305034 age: 52; gender: F; race: W; pmi: 10.3; ph: 6.5; rin: 6.7; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305035 age: 62; gender: M; race: W; pmi: 26; ph: 6.5; rin: 7.5; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305036 age: 29; gender: M; race: W; pmi: 26.6; ph: 6.9; rin: 9.2; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305037 age: 49; gender: F; race: W; pmi: 23.4; ph: 6.4; rin: 6.7; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305038 age: 54; gender: F; race: W; pmi: 17.9; ph: 6.2; rin: 9; tissue: Associative striatum; disease state: major depressive disorder
#> GSM1305039 age: 50; gender: M; race: W; pmi: 11; ph: 6.23; rin: 8.5; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305040 age: 46; gender: M; race: W; pmi: 15.8; ph: 6.19; rin: 7.8; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305041 age: 41; gender: F; race: W; pmi: 20.1; ph: 6.27; rin: 8.6; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305042 age: 47; gender: M; race: W; pmi: 28.9; ph: 6.58; rin: 8.4; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305043 age: 37; gender: M; race: B; pmi: 5.98; ph: 6.07; rin: 6.9; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305044 age: 58; gender: M; race: W; pmi: 7.7; ph: 6.22; rin: 6.7; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305045 age: 44; gender: F; race: B; pmi: 18.7; ph: 6.2; rin: 6.9; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305046 age: 38; gender: M; race: W; pmi: 28.8; ph: 6.56; rin: 6.8; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305047 age: 52; gender: M; race: B; pmi: 27.1; ph: 6.68; rin: 8.5; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305048 age: 49; gender: M; race: W; pmi: 21.5; ph: 5.97; rin: 8.4; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305049 age: 47; gender: F; race: W; pmi: 14.37; ph: 6.35; rin: 8.9; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305050 age: 25; gender: F; race: B; pmi: 20.1; ph: 6.73; rin: 7.3; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305051 age: 41; gender: F; race: W; pmi: 17.1; ph: 6.9; rin: 7.3; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305052 age: 62; gender: M; race: W; pmi: 22.7; ph: 7.14; rin: 7.8; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305053 age: 32; gender: M; race: W; pmi: 30.8; ph: 6.18; rin: 7.1; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305054 age: 47; gender: F; race: B; pmi: 20.1; ph: 7.3; rin: 8.8; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305055 age: 50; gender: F; race: B; pmi: 22.9; ph: 6.25; rin: 8; tissue: Associative striatum; disease state: schizo; phrenia
#> GSM1305056 age: 44; gender: F; race: W; pmi: 24.5; ph: 6.63; rin: 9; tissue: Associative striatum; disease state: schizo; phreniaDownload supplementary data from GEO database - geo_suppl
GEO stores raw data and processed sequence data files as the external supplementary data files. Sometimes, we may want to preprocess and normalize the rawdata by ourselves, in addition, it’s not uncommon that a GSE entity series matrix won’t contain the expression matrix, which is almost the case of high-throughout sequencing data. geo_suppl is designed for these conditions. Usually, the expression matrix will be provided in the GSE supplementary files or in the GSM supplementary files.
If the expression matrix is given in the GSE supplementary files, we can download it directly use geo_suppl, which will return a character vector containing the path of downloaded files.
gse160724 <- geo_suppl(
ids = "GSE160724", odir = tempdir(),
pattern = "counts_anno"
)
#> Downloading 1 GSE suppl file from FTP site
gse160724_dt <- data.table::fread(gse160724)
head(gse160724_dt[1:5])
#> gene_id NC_1 NC_2 shSRSF1_1 shSRSF1_2
#> <char> <int> <int> <int> <int>
#> 1: A1BG 189 179 299 310
#> 2: A1CF 0 0 0 0
#> 3: A2M 0 0 0 0
#> 4: A2ML1 0 0 0 0
#> 5: A3GALT2 0 1 0 0
#> Dbxref
#> <char>
#> 1: GeneID:1,Genbank:NM_130786.3,HGNC:HGNC:5,MIM:138670
#> 2: GeneID:29974,Genbank:NM_138933.2,HGNC:HGNC:24086
#> 3: GeneID:2,Genbank:NM_001347423.1,HGNC:HGNC:7,MIM:103950
#> 4: GeneID:144568,Genbank:NM_144670.5,HGNC:HGNC:23336,MIM:610627
#> 5: GeneID:127550,Genbank:NM_001080438.1,HGNC:HGNC:30005
#> product
#> <char>
#> 1: alpha-1-B glycoprotein
#> 2: APOBEC1 complementation factor
#> 3: alpha-2-macroglobulin
#> 4: alpha-2-macroglobulin like 1
#> 5: alpha 1,3-galactosyltransferase 2
#> GO_id
#> <char>
#> 1:
#> 2: GO:0003723,GO:0003727,GO:0005654,GO:0005737,GO:0005783,GO:0006397,GO:0016554,GO:0016556,GO:0030895,GO:0050821
#> 3:
#> 4: GO:0004867,GO:0005615,GO:0030414,GO:0052548,GO:0070062
#> 5: GO:0005794,GO:0005975,GO:0006688,GO:0009247,GO:0016021,GO:0016757,GO:0030259,GO:0031982,GO:0032580,GO:0046872,GO:0047276
#> GO_term
#> <char>
#> 1:
#> 2: RNA binding|single-stranded RNA binding|nucleoplasm|cytoplasm|endoplasmic reticulum|mRNA processing|cytidine to uridine editing|mRNA modification|apolipoprotein B mRNA editing enzyme complex|protein stabilization
#> 3:
#> 4: serine-type endopeptidase inhibitor activity|extracellular space|peptidase inhibitor activity|regulation of endopeptidase activity|extracellular exosome
#> 5: Golgi apparatus|carbohydrate metabolic process|glycosphingolipid biosynthetic process|glycolipid biosynthetic process|integral component of membrane|transferase activity, transferring glycosyl groups|lipid glycosylation|vesicle|Golgi cisterna membrane|metal ion binding|N-acetyllactosaminide 3-alpha-galactosyltransferase activity
#> pathway pathway_description
#> <char> <char>
#> 1:
#> 2:
#> 3: hsa04610 Complement and coagulation cascades
#> 4:
#> 5: hsa00603 Glycosphingolipid biosynthesis - globo and isoglobo seriesIf the expression matrix is given in the GSM supplementary files, in this way, we start from derive all GSM accession ids and then download all GSM supplementary files and combine them into a expression matrix. Although no expression matrix in the series matrix file, it still contains the samples informations.
gse180383_smat <- geo(
"GSE180383",
odir = tempdir(),
gse_matrix = TRUE, add_gpl = FALSE,
pdata_from_soft = FALSE
)
#> Downloading 1 GSE matrix file from FTP site
#> → Parsing 1 series matrix file of GSE180383
#> Warning: Cannot parse characteristic column correctly
#> ℹ Details see "characteristics_ch1" column in phenoData
#> ℹ Please use `parse_pdata()` or `parse_gsm_list()` function to convert it
#> manually if necessary!
#> ✔ Parsing 1 GSE series matrix successfully!
#> → Constructing <ExpressionSet>
#> ✔ Found Bioconductor annotation package for "GPL21359"
gse180383_smat_cli <- Biobase::pData(gse180383_smat)
head(gse180383_smat_cli[1:5])
#> title geo_accession status
#> GSM5461787 Monoecious WT RNA-seq rep1 GSM5461787 Public on Feb 15 2022
#> GSM5461788 Monoecious WT RNA-seq rep2 GSM5461788 Public on Feb 15 2022
#> GSM5461789 Monoecious WT RNA-seq rep3 GSM5461789 Public on Feb 15 2022
#> GSM5461790 Cmlhp1abRNA-seq rep1 GSM5461790 Public on Feb 15 2022
#> GSM5461791 Cmlhp1abRNA-seq rep2 GSM5461791 Public on Feb 15 2022
#> GSM5461792 Cmlhp1abRNA-seq rep3 GSM5461792 Public on Feb 15 2022
#> submission_date last_update_date
#> GSM5461787 Jul 19 2021 Feb 15 2022
#> GSM5461788 Jul 19 2021 Feb 15 2022
#> GSM5461789 Jul 19 2021 Feb 15 2022
#> GSM5461790 Jul 19 2021 Feb 15 2022
#> GSM5461791 Jul 19 2021 Feb 15 2022
#> GSM5461792 Jul 19 2021 Feb 15 2022
gse180383_smat_gsmids <- gse180383_smat_cli[["geo_accession"]]
gse180383_smat_gsm_suppl <- geo_suppl(gse180383_smat_gsmids, odir = tempdir())
#> Downloading 6 GSM suppl files from FTP siteAnother way, we can also derive sample accession ids from GSE soft files, which is what our laboratory prefers to since we can easily get exact sample traits information as described in the above by utilizing parse_gsm_list function.
gse180383_soft <- geo(
"GSE180383",
odir = tempdir(),
gse_matrix = FALSE
)
#> Downloading 1 GSE soft file from FTP site
gse180383_soft_cli <- parse_gsm_list(gsm(gse180383_soft))
#> Warning: More than one characters ":" found in meta characteristics data
#> ℹ Details see: "characteristics_ch1" column in returned data.
#> ℹ Please use `parse_pdata()` or combine `strsplit()` and `parse_gsm_list()`
#> function to convert it manually if necessary!
head(gse180383_soft_cli[1:5])
#> channel_count
#> GSM5461787 1
#> GSM5461788 1
#> GSM5461789 1
#> GSM5461790 1
#> GSM5461791 1
#> GSM5461792 1
#> ch1_cultivar
#> GSM5461787 Charantais type: Cucumis melo L. subsp. melo var cantalupensis
#> GSM5461788 Charantais type: Cucumis melo L. subsp. melo var cantalupensis
#> GSM5461789 Charantais type: Cucumis melo L. subsp. melo var cantalupensis
#> GSM5461790 Charantais type: Cucumis melo L. subsp. melo var cantalupensis
#> GSM5461791 Charantais type: Cucumis melo L. subsp. melo var cantalupensis
#> GSM5461792 Charantais type: Cucumis melo L. subsp. melo var cantalupensis
#> ch1_genotypes
#> GSM5461787 CharMONO inbreed line
#> GSM5461788 CharMONO inbreed line
#> GSM5461789 CharMONO inbreed line
#> GSM5461790 CharMONO cmlhp1ab double mutant carrying EMS mutations for Cmlhp1a (G1970A, genomic position from ATG ) and cmlhp1b (C1930T genomic position from ATG )
#> GSM5461791 CharMONO cmlhp1ab double mutant carrying EMS mutations for Cmlhp1a (G1970A, genomic position from ATG ) and cmlhp1b (C1930T genomic position from ATG )
#> GSM5461792 CharMONO cmlhp1ab double mutant carrying EMS mutations for Cmlhp1a (G1970A, genomic position from ATG ) and cmlhp1b (C1930T genomic position from ATG )
#> characteristics_ch1
#> GSM5461787 cultivar: Charantais type: Cucumis melo L. subsp. melo var cantalupensis; genotypes: CharMONO inbreed line
#> GSM5461788 cultivar: Charantais type: Cucumis melo L. subsp. melo var cantalupensis; genotypes: CharMONO inbreed line
#> GSM5461789 cultivar: Charantais type: Cucumis melo L. subsp. melo var cantalupensis; genotypes: CharMONO inbreed line
#> GSM5461790 cultivar: Charantais type: Cucumis melo L. subsp. melo var cantalupensis; genotypes: CharMONO cmlhp1ab double mutant carrying EMS mutations for Cmlhp1a (G1970A, genomic position from ATG ) and cmlhp1b (C1930T genomic position from ATG )
#> GSM5461791 cultivar: Charantais type: Cucumis melo L. subsp. melo var cantalupensis; genotypes: CharMONO cmlhp1ab double mutant carrying EMS mutations for Cmlhp1a (G1970A, genomic position from ATG ) and cmlhp1b (C1930T genomic position from ATG )
#> GSM5461792 cultivar: Charantais type: Cucumis melo L. subsp. melo var cantalupensis; genotypes: CharMONO cmlhp1ab double mutant carrying EMS mutations for Cmlhp1a (G1970A, genomic position from ATG ) and cmlhp1b (C1930T genomic position from ATG )
#> contact_address
#> GSM5461787 630 Rue Noetzlin
#> GSM5461788 630 Rue Noetzlin
#> GSM5461789 630 Rue Noetzlin
#> GSM5461790 630 Rue Noetzlin
#> GSM5461791 630 Rue Noetzlin
#> GSM5461792 630 Rue Noetzlin
gse180383_soft_gsmids <- names(gsm(gse180383_soft))
gse180383_soft_gsm_suppl <- geo_suppl(gse180383_soft_gsmids, odir = tempdir())
#> Finding 6 {.strong GSM} {.field suppl} file already downloaded:
#> 'GSM5461787_trim_RNA_Mono_1_S13_R1_001_countsMatrix.txt.gz',
#> 'GSM5461788_trim_RNA_Mono_2_S14_R1_001_countsMatrix.txt.gz',
#> 'GSM5461789_trim_RNA_Mono_3_S15_R1_001_countsMatrix.txt.gz',
#> 'GSM5461790_trim_RNA_ab_1_S16_R1_001_countsMatrix.txt.gz',
#> 'GSM5461791_trim_RNA_ab_2_S17_R1_001_countsMatrix.txt.gz', and
#> 'GSM5461792_trim_RNA_ab_3_S18_R1_001_countsMatrix.txt.gz'Other utilities
geokit also provide some useful function to help better interact with GEO.
-
geo_showfunction: Require a geo entity id and open GEO Accession site in the default browser. -
log_transfunction: Require a expression matrix and this function will check whether this expression matrix has experienced logarithmic transformation, if it hasn’t,log_transwill do it. This is a helper function used inGEO2R.
sessionInfo
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libmkl_rt.so; LAPACK version 3.8.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Asia/Shanghai
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] geokit_0.0.1.9000
#>
#> loaded via a namespace (and not attached):
#> [1] vctrs_0.6.5 cli_3.6.5 knitr_1.50 rlang_1.1.6
#> [5] xfun_0.52 stringi_1.8.4 generics_0.1.3 textshaping_0.4.0
#> [9] data.table_1.16.99 glue_1.8.0 htmltools_0.5.8.1 ragg_1.3.3
#> [13] fansi_1.0.6 rmarkdown_2.29 evaluate_1.0.3 tibble_3.2.1
#> [17] fastmap_1.2.0 yaml_2.3.10 lifecycle_1.0.4 stringr_1.5.1
#> [21] compiler_4.4.2 dplyr_1.1.4 pkgconfig_2.0.3 systemfonts_1.1.0
#> [25] digest_0.6.37 R6_2.6.1 tidyselect_1.2.1 utf8_1.2.5
#> [29] pillar_1.9.0 magrittr_2.0.3 withr_3.0.2 tools_4.4.2