The goal of rgeo is to provide a unified interface for most
interactions between R and GEO
database.
- 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_transandshow_geo.
You can install the development version of rgeo 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/rgeo")library(rgeo)
library(magrittr)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 search_geo. search_geo
will parse the searching results and return a data.frame object
containing all the records based on the search term. The internal of
search_geo 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 <- search_geo(
"diabetes[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]"
)
head(diabetes_gse_records[1:5])
#> Title
#> 1 Neutrophil extracellular traps induce glomerular endothelial cell dysfunction and pyroptosis in diabetic kidney disease
#> 2 The expression of mRNA and lncRNA in peripheral blood mononuclear cells (PBMCs) of diabetes mellitus, diabetic retinopathy (DR), diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN) patients
#> 3 Mitochondrial DNA atlas reveals physiopathology of type 2 diabetes
#> 4 DNA methylation profiling in cord blood neonatal monocytes from women with pre-gestational obesity
#> 5 Transcriptomic signatures responding to PKM2 activator TEPP-46 in the hyperglycemic human renal proximal epithelial tubular cells
#> 6 Global microRNA and protein expression in human term placenta.
#> Summary
#> 1 Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Neutrophil extracellular traps (NETs) are a network structure composed of loose chromatin and embedded with multiple proteins. Here, we observed increased NETs deposition in the glomeruli of DKD patients and diabetic mice (streptozotocin-induced or db/db mice). After degrading NETs with DNase I, diabetic mice exhibited attenuated glomerulopathy and glomerular endothelial cell (GEC) injury. more...
#> 2 DR, DPN and DN are common complications in diabetes, and the differentially expressed mRNAs and lncRNAs in these diabetic complications may help to identify the molecular markers for the onset and progression of diseases. In our study, high-throughput sequencing technique was used to analyze the expression profile of mRNA and lncRNA in the peripheral blood of health control, T2DM, DR, DPN and DN patients, in order to determine the differentially expressed transcriptomic profiles changes in diabetic complications and identify the shared and specific biological signaling pathways related to DR, DPN and DN.
#> 3 Type 2 diabetes (T2D), one of the most common metabolic diseases, is the result of insulin resistance or impaired insulin secretion by mitochondrial dysfunctions. Mitochondrial DNA (mtDNA) polymorphisms play an important role in physiological and pathological characteristics of T2D, however, their mechanism is poorly understood. To directly identify candidate mtDNA variants associated with T2D at the genome-wide level, we constructed forty libraries from ten patients with T2D and thirty control individuals for deep sequencing. more...
#> 4 Obesity represents a global burden with an increasing worldwide prevalence, especially in women of reproductive age. Obesity in women, defined as a body mass index (BMI) > 30 kg/m2, has a worldwide prevalence ~21%, however, it exceeds 30% in countries such as Chile, Mexico, United States, and the United Kingdom. Growing evidence support the notion that pre-gestational obesity confers an increased risk for the development of diabetes, obesity and chronic inflammatory diseases in the offspring later in life. more...
#> 5 Pyruvate kinase M2 (PKM2), as the terminal and last rate-limiting enzyme of the glycolytic pathway, is an ideal enzyme for regulating metabolic phenotype. PKM2 tetramer activation has shown a protective role against diabetic kidney disease (DKD). However, the molecular mechanisms involved in diabetic tubular has not been investigated so far. In this study, we performed transcriptome gene expression profiling in human renal proximal tubular epithelial cell line (HK-2 cells) treated with high D-glucose (HG) for 7 days before the addition of 10-μM TEPP-46, an activator of PKM2 tetramerization, for a further 1 day in the presence of HG. more...
#> 6 Description of the global expression of microRNAs (miRNAs) and proteins in healthy human term placentas may increase our knowledge of molecular biological pathways that are important for normal fetal growth and development in term pregnancy. The aim of this study was to explore the global expression of miRNAs and proteins, and to point out functions of importance in healthy term placentas. Placental samples (n = 19) were identified in a local biobank. more...
#> Organism Type
#> 1 Homo sapiens Expression profiling by high throughput sequencing
#> 2 Homo sapiens Expression profiling by high throughput sequencing
#> 3 Homo sapiens Genome variation profiling by high throughput sequencing
#> 4 Homo sapiens Methylation profiling by genome tiling array
#> 5 Homo sapiens Expression profiling by high throughput sequencing
#> 6 Homo sapiens Non-coding RNA profiling by high throughput sequencing
#> FTP download
#> 1 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE189nnn/GSE189875/
#> 2 GEO (GTF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185011/
#> 3 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136892/
#> 4 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE212nnn/GSE212174/
#> 5 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE205nnn/GSE205674/
#> 6 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE211nnn/GSE211791/Then, 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
#> 1 The expression of mRNA and lncRNA in peripheral blood mononuclear cells (PBMCs) of diabetes mellitus, diabetic retinopathy (DR), diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN) patients
#> 2 Secretory Leukocyte Peptidase Inhibitor (SLPI) is a Novel Predictor of Tubulointerstitial Injury and Renal Outcome in Patients with Diabetic Nephropathy
#> 3 Bulk RNA-seq on mouse model of diabetic nephropathy and in vitro model of SRSF7 knockdown
#> 4 RNA-seq profiling of tubulointerstitial tissue reveals a potential therapeutic role of dual anti-phosphatase 1 in kidney diseases
#> 5 Human Tubular Epithelial Cells Activate a Coordinated Stress Response after Serum Exposure [RNAseq-pid2019]
#> 6 Human Tubular Epithelial Cells Activate a Coordinated Stress Response after Serum Exposure [RNAseq-pid1830]
#> Summary
#> 1 DR, DPN and DN are common complications in diabetes, and the differentially expressed mRNAs and lncRNAs in these diabetic complications may help to identify the molecular markers for the onset and progression of diseases. In our study, high-throughput sequencing technique was used to analyze the expression profile of mRNA and lncRNA in the peripheral blood of health control, T2DM, DR, DPN and DN patients, in order to determine the differentially expressed transcriptomic profiles changes in diabetic complications and identify the shared and specific biological signaling pathways related to DR, DPN and DN.
#> 2 Tubulointerstitial injury plays an important role in diabetic nephropathy (DN) progression; however, no reliable urinary molecule has been used to predict tubulointerstitial injury and renal outcome of DN clinically. In this study, based on tubulointerstitial transcriptome, we identified secretory leukocyte peptidase inhibitor (SLPI) as the molecule associated with renal fibrosis and prognosis of DN. more...
#> 3 In this dataset, we utilized the db/db, uninephrectomy and renin-hypertension mouse model. We performed bulk RNA-seq and compared vehicle to ACE inhibitor, Rosiglitizone, SGLT2 inhibitor, ACEi + Rosiglitizone and ACEi + SGLT2i at two time points (2 days and 2 weeks). To study the mechanism, we also performed bulk RNA-seq on human primary tubular epithelial cells with or without SRSF7 siRNA knockdown.
#> 4 We profiled manually microdissected tubulointerstitial tissue from 43 IgA nephropathy, 3 diabetes mellitus nephropathy, 3 focal segmental glomerulosclerosis, 3 lupus nephritis, 4 membranous nephropathy, and 9 minimal change disease biopsy cores and 22 nephrectomy controls by RNA sequencing. The 3 outliers which were not included in our main analysis were also uploaded in this database.
#> 5 Proteinuria, the spillage of serum proteins into the urine, is a feature of glomerulonephritides, podocyte disorders and diabetic nephropathy. However, the response of tubular epithelial cells to serum protein exposure has not been systematically characterized. Using transcriptomic profiling we studied serum-induced changes in primary human tubular epithelial cells cultured in 3D microphysiological devices. more...
#> 6 Proteinuria, the spillage of serum proteins into the urine, is a feature of glomerulonephritides, podocyte disorders and diabetic nephropathy. However, the response of tubular epithelial cells to serum protein exposure has not been systematically characterized. Using transcriptomic profiling we studied serum-induced changes in primary human tubular epithelial cells cultured in 3D microphysiological devices. more...
#> Organism Type
#> 1 Homo sapiens Expression profiling by high throughput sequencing
#> 2 Homo sapiens Expression profiling by high throughput sequencing
#> 3 Mus musculus; 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
#> 6 Homo sapiens Expression profiling by high throughput sequencing
#> FTP download
#> 1 GEO (GTF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185011/
#> 2 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE158nnn/GSE158230/
#> 3 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE199nnn/GSE199437/
#> 4 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE175nnn/GSE175759/
#> 5 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159586/
#> 6 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159554/After filtering, we got 19 candidate datasets. This can reduce a lot of time of us comparing with refining datasets by reading the summary records.
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. rgeo
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.
get_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 <- get_geo("gpl98", tempdir())
#> Downloading GPL98.txt 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 <- get_geo("GSM1", tempdir())
#> Downloading GSM1.txt 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 milliongds <- get_geo("GDS10", tempdir())
#> Downloading GDS10.soft.gz 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, rgeo
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 get_geo function.
gse <- get_geo("GSE10", tempdir(), gse_matrix = FALSE)
#> Downloading GSE10_family.soft.gz from FTP site:
#> Found 5 entities...
#> GPL4 (1 of 5 entities)
#> GSM571 (2 of 5 entities)
#> GSM572 (3 of 5 entities)
#> GSM573 (4 of 5 entities)
#> GSM574 (5 of 5 entities)
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 rgeo, as what we need to do
is just set gse_matrix to TRUE in get_geo function, which is also
the default value. When gse_matrix is TRUE, get_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 get_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, get_geo will add
featureData from GPL soft file directly.
gse_matix <- get_geo("GSE10", tempdir())
#> Downloading GSE10_series_matrix.txt.gz from FTP site:
#> Downloading GSE10_family.soft.gz from FTP site:
#> Cannot map GPL4 to a Bioconductor annotation package
#> • Setting `add_gpl` to `TRUE`
#> Downloading GPL4.annot.gz from FTP site:
#>
#> annot file in FTP site for GPL4 is not available, so will use data amount of SOFT file from GEO Accession Site instead.
#> Downloading GPL4.txt 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: GPL4gse_matrix_with_pdata <- get_geo(
"gse53987", 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!
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 ch1_tissue
#> GSM1304852 52 M W 23.50 6.70 6.3 hippocampus
#> GSM1304853 50 F W 11.70 6.40 6.8 hippocampus
#> GSM1304854 28 F W 22.30 6.30 7.7 hippocampus
#> GSM1304855 55 F W 17.50 6.40 7.6 hippocampus
#> GSM1304856 58 M W 27.70 6.80 7.0 hippocampus
#> GSM1304857 28 M W 27.40 6.20 7.7 hippocampus
#> GSM1304858 49 F W 21.50 6.70 8.2 hippocampus
#> GSM1304859 42 F W 31.20 6.50 5.6 hippocampus
#> GSM1304860 43 F W 31.90 6.70 6.3 hippocampus
#> GSM1304861 50 M W 12.10 6.70 7.4 hippocampus
#> GSM1304862 40 M W 18.50 6.40 6.5 hippocampus
#> GSM1304863 39 F W 22.20 6.70 7.9 hippocampus
#> GSM1304864 45 M W 27.20 7.10 8.1 hippocampus
#> GSM1304865 42 M W 12.50 6.70 8.2 hippocampus
#> GSM1304866 65 M W 8.90 6.70 6.6 hippocampus
#> GSM1304867 51 F W 21.50 6.70 7.0 hippocampus
#> GSM1304868 39 M W 24.20 6.60 7.8 hippocampus
#> GSM1304869 48 M W 18.10 6.90 7.0 hippocampus
#> GSM1304870 51 M W 24.20 6.60 7.8 hippocampus
#> GSM1304871 51 F W 7.80 6.60 7.2 hippocampus
#> GSM1304872 36 F W 14.50 6.40 8.0 hippocampus
#> GSM1304873 65 F W 18.50 6.50 7.0 hippocampus
#> GSM1304874 55 M W 28.00 6.10 6.8 hippocampus
#> GSM1304875 22 M W 20.10 6.80 7.1 hippocampus
#> GSM1304876 52 F W 22.60 7.10 7.0 hippocampus
#> GSM1304877 58 F W 22.70 6.40 6.3 hippocampus
#> GSM1304878 40 F B 16.60 6.80 7.9 hippocampus
#> GSM1304879 41 F W 15.40 6.60 8.5 hippocampus
#> GSM1304880 49 M W 21.20 6.50 7.8 hippocampus
#> GSM1304881 48 M W 21.68 6.60 7.3 hippocampus
#> GSM1304882 39 F W 24.50 6.80 8.2 hippocampus
#> GSM1304883 48 M W 24.50 6.50 7.0 hippocampus
#> GSM1304884 43 M W 13.80 6.60 7.6 hippocampus
#> GSM1304885 68 M W 11.80 6.80 6.1 hippocampus
#> GSM1304886 58 F W 18.80 6.60 7.2 hippocampus
#> GSM1304887 43 M W 22.30 6.70 7.9 hippocampus
#> GSM1304888 51 M W 24.60 6.50 7.7 hippocampus
#> GSM1304889 53 F W 11.90 6.70 8.1 hippocampus
#> GSM1304890 26 F W 13.40 6.40 7.5 hippocampus
#> GSM1304891 52 F W 10.30 6.50 6.6 hippocampus
#> GSM1304892 62 M W 26.00 6.50 6.8 hippocampus
#> GSM1304893 29 M W 26.60 6.90 7.8 hippocampus
#> GSM1304894 49 F W 23.40 6.40 6.2 hippocampus
#> GSM1304895 54 F W 17.90 6.20 6.1 hippocampus
#> GSM1304896 28 F B 24.80 6.60 8.2 hippocampus
#> GSM1304897 42 M W 14.30 6.40 6.2 hippocampus
#> GSM1304898 44 M W 19.30 6.50 6.3 hippocampus
#> GSM1304899 40 F W 22.20 6.60 8.0 hippocampus
#> GSM1304900 47 M W 24.00 6.60 5.5 hippocampus
#> GSM1304901 59 M W 13.00 6.60 7.2 hippocampus
#> GSM1304902 47 F W 22.30 6.60 6.5 hippocampus
#> GSM1304903 34 M W 24.40 6.60 8.4 hippocampus
#> GSM1304904 51 M W 28.30 7.30 7.0 hippocampus
#> GSM1304905 49 M W 21.50 5.97 6.0 hippocampus
#> GSM1304906 47 F W 14.37 6.35 6.3 hippocampus
#> ch1_disease state
#> GSM1304852 bipolar disorder
#> GSM1304853 bipolar disorder
#> GSM1304854 bipolar disorder
#> GSM1304855 bipolar disorder
#> GSM1304856 bipolar disorder
#> GSM1304857 bipolar disorder
#> GSM1304858 bipolar disorder
#> GSM1304859 bipolar disorder
#> GSM1304860 bipolar disorder
#> GSM1304861 bipolar disorder
#> GSM1304862 bipolar disorder
#> GSM1304863 bipolar disorder
#> GSM1304864 bipolar disorder
#> GSM1304865 bipolar disorder
#> GSM1304866 bipolar disorder
#> GSM1304867 bipolar disorder
#> GSM1304868 bipolar disorder
#> GSM1304869 bipolar disorder
#> GSM1304870 control
#> GSM1304871 control
#> GSM1304872 control
#> GSM1304873 control
#> GSM1304874 control
#> GSM1304875 control
#> GSM1304876 control
#> GSM1304877 control
#> GSM1304878 control
#> GSM1304879 control
#> GSM1304880 control
#> GSM1304881 control
#> GSM1304882 control
#> GSM1304883 control
#> GSM1304884 control
#> GSM1304885 control
#> GSM1304886 control
#> GSM1304887 control
#> GSM1304888 major depressive disorder
#> GSM1304889 major depressive disorder
#> GSM1304890 major depressive disorder
#> GSM1304891 major depressive disorder
#> GSM1304892 major depressive disorder
#> GSM1304893 major depressive disorder
#> GSM1304894 major depressive disorder
#> GSM1304895 major depressive disorder
#> GSM1304896 major depressive disorder
#> GSM1304897 major depressive disorder
#> GSM1304898 major depressive disorder
#> GSM1304899 major depressive disorder
#> GSM1304900 major depressive disorder
#> GSM1304901 major depressive disorder
#> GSM1304902 major depressive disorder
#> GSM1304903 major depressive disorder
#> GSM1304904 major depressive disorder
#> GSM1304905 schizo
#> GSM1304906 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
#> [ reached 'max' / getOption("max.print") -- omitted 150 rows ]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. get_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 get_geo_suppl, which will return a character
vector containing the path of downloaded files.
gse160724 <- get_geo_suppl(
ids = "GSE160724", tempdir(),
pattern = "counts_anno"
)
#> Downloading GSE160724_counts_anno.txt.gz 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 <- get_geo(
"GSE180383", tempdir(),
gse_matrix = TRUE, add_gpl = FALSE,
pdata_from_soft = FALSE
)
#> Downloading GSE180383_series_matrix.txt.gz from FTP site:
#> Warning: Cannot parse characteristic column correctly
#> • Details see `characteristics_ch1` column in `phenoData`
#> Warning: Please use `set_pdata` or `parse_pdata` function to convert it manually
#> if necessary!
#> Cannot map GPL21359 to a Bioconductor annotation package
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 <- get_geo_suppl(gse180383_smat_gsmids, tempdir())
#> Downloading GSM5461787_trim_RNA_Mono_1_S13_R1_001_countsMatrix.txt.gz from FTP
#> site:
#> Downloading GSM5461788_trim_RNA_Mono_2_S14_R1_001_countsMatrix.txt.gz from FTP
#> site:
#> Downloading GSM5461789_trim_RNA_Mono_3_S15_R1_001_countsMatrix.txt.gz from FTP
#> site:
#> Downloading GSM5461790_trim_RNA_ab_1_S16_R1_001_countsMatrix.txt.gz from FTP
#> site:
#> Downloading GSM5461791_trim_RNA_ab_2_S17_R1_001_countsMatrix.txt.gz from FTP
#> site:
#> Downloading GSM5461792_trim_RNA_ab_3_S18_R1_001_countsMatrix.txt.gz from FTP
#> site:Another 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 <- get_geo(
"GSE180383", 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 <- get_geo_suppl(gse180383_soft_gsmids, tempdir())
#> Downloading 6 GSM suppl files from FTP sitergeo also provide some useful function to help better interact with
GEO.
show_geofunction: 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()
#> R version 4.2.2 (2022-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 22621)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=Chinese (Simplified)_China.utf8
#> [2] LC_CTYPE=Chinese (Simplified)_China.utf8
#> [3] LC_MONETARY=Chinese (Simplified)_China.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=Chinese (Simplified)_China.utf8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] kableExtra_1.3.4 magrittr_2.0.3 rgeo_0.0.1.9000
#>
#> loaded via a namespace (and not attached):
#> [1] pillar_1.8.0 compiler_4.2.2 R.methodsS3_1.8.2
#> [4] R.utils_2.12.0 tools_4.2.2 digest_0.6.29
#> [7] viridisLite_0.4.0 evaluate_0.15 lifecycle_1.0.3
#> [10] tibble_3.1.8 pkgconfig_2.0.3 rlang_1.0.6
#> [13] cli_3.6.0 DBI_1.1.3 rstudioapi_0.13
#> [16] curl_5.0.0 yaml_2.3.5 xfun_0.31
#> [19] fastmap_1.1.0 dplyr_1.0.9 stringr_1.4.0
#> [22] httr_1.4.3 knitr_1.39 xml2_1.3.3
#> [25] generics_0.1.3 vctrs_0.5.1 systemfonts_1.0.4
#> [28] webshot_0.5.3 tidyselect_1.1.2 Biobase_2.56.0
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