R/deprecation.R
search_datasets.RdThis function is deprecated in favor of get_datasets
search_datasets(
query,
taxon = NA_character_,
offset = 0L,
limit = 20L,
sort = "+id",
raw = getOption("gemma.raw", FALSE),
memoised = getOption("gemma.memoised", FALSE),
file = getOption("gemma.file", NA_character_),
overwrite = getOption("gemma.overwrite", FALSE),
attributes = getOption("gemma.attributes", TRUE),
...
)The search query. Either plain text ('traumatic'), or an ontology term URI ('http://purl.obolibrary.org/obo/UBERON_0002048'). Datasets that contain the given string in their short or full name will also be matched. Can be multiple identifiers separated by commas.
Can either be Taxon ID, Taxon NCBI ID, or one of its string identifiers: scientific name, common name.
It is recommended to use Taxon ID for efficiency.
Please note, that not all taxa have all the possible identifiers available.
Use the get_taxa_by_ids function to retrieve the necessary information. For convenience, below is a list of officially supported taxa:
| ID | Comm.name | Scient.name | NcbiID |
| 1 | human | Homo sapiens | 9606 |
| 2 | mouse | Mus musculus | 10090 |
| 3 | rat | Rattus norvegicus | 10116 |
| 11 | yeast | Saccharomyces cerevisiae | 4932 |
| 12 | zebrafish | Danio rerio | 7955 |
| 13 | fly | Drosophila melanogaster | 7227 |
| 14 | worm | Caenorhabditis elegans | 6239 |
The offset of the first retrieved result.
Optional, defaults to 20. Limits the result to specified amount
of objects. Has a maximum value of 100. Use together with offset and
the totalElements attribute in the output to
compile all data if needed.
Order results by the given property and direction. The '+' sign indicate ascending order whereas the '-' indicate descending.
TRUE to receive results as-is from Gemma, or FALSE to enable
parsing. Raw results usually contain additional fields and flags that are
omitted in the parsed results.
Whether or not to save to cache for future calls with the
same inputs and use the result saved in cache if a result is already saved.
Doing options(gemma.memoised = TRUE) will ensure that the cache is always
used. Use forget_gemma_memoised to clear the cache.
The name of a file to save the results to, or NULL to not write
results to a file. If raw == TRUE, the output will be a JSON file. Otherwise,
it will be a RDS file.
Whether or not to overwrite if a file exists at the specified filename.
If TRUE additional information from the call will be added
into the output object's attributes such as offset and available elements.
Kept for compatibility, ignored.
A data table with information about the queried dataset(s). A list if
raw = TRUE. Returns an empty list if no datasets matched.
The fields of the output data.table are:
experiment.shortName: Shortname given to the dataset within Gemma. Often corresponds to accession ID
experiment.name: Full title of the dataset
experiment.ID: Internal ID of the dataset.
experiment.description: Description of the dataset
experiment.troubled: Did an automatic process within gemma or a curator mark the dataset as "troubled"
experiment.accession: Accession ID of the dataset in the external database it was taken from
experiment.database: The name of the database where the dataset was taken from
experiment.URI: URI of the original database
experiment.sampleCount: Number of samples in the dataset
experiment.batchEffectText: A text field describing whether the dataset has batch effects
experiment.batchCorrected: Whether batch correction has been performed on the dataset.
experiment.batchConfound: 0 if batch info isn't available, -1 if batch counfoud is detected, 1 if batch information is available and no batch confound found
experiment.batchEffect: -1 if batch p value < 0.0001, 1 if batch p value > 0.1, 0 if otherwise and when there is no batch information is available or when the data is confounded with batches.
experiment.rawData: -1 if no raw data available, 1 if raw data was available. When available, Gemma reprocesses raw data to get expression values and batches
geeq.qScore: Data quality score given to the dataset by Gemma.
geeq.sScore: Suitability score given to the dataset by Gemma. Refers to factors like batches, platforms and other aspects of experimental design
taxon.name: Name of the species
taxon.scientific: Scientific name for the taxon
taxon.ID: Internal identifier given to the species by Gemma
taxon.NCBI: NCBI ID of the taxon
taxon.database.name: Underlying database used in Gemma for the taxon
taxon.database.ID: ID of the underyling database used in Gemma for the taxon
search_datasets("bipolar", taxon = "human")
#> Warning: search_datasets is deprecated. please use get_datasets instead
#> experiment.shortName
#> <char>
#> 1: GSE5389
#> 2: GSE4030
#> 3: GSE5388
#> 4: GSE7036
#> 5: McLean Hippocampus
#> 6: McLean_PFC
#> 7: stanley_feinberg
#> 8: stanley_kato
#> 9: stanley_sklarA
#> 10: stanley_sklarB
#> 11: stanley_vawter
#> 12: stanley_young
#> 13: stanley_altarA
#> 14: stanley_altarB
#> 15: stanley_altarC
#> 16: stanley_bahn
#> 17: stanley_chen
#> 18: stanley_dobrin
#> 19: GSE12679
#> 20: GSE12654
#> experiment.shortName
#> experiment.name
#> <char>
#> 1: Adult postmortem brain tissue (orbitofrontal cortex) from subjects with bipolar disorder and healthy controls
#> 2: bunge-affy-arabi-162779
#> 3: Adult postmortem brain tissue (dorsolateral prefrontal cortex) from subjects with bipolar disorder and healthy controls
#> 4: Expression profiling in monozygotic twins discordant for bipolar disorder
#> 5: McLean Hippocampus
#> 6: McLean_PFC
#> 7: Stanley consortium collection Cerebellum - Feinberg
#> 8: Stanley array collection DLPFC - Kato
#> 9: Stanley consortium collection DLPFC - SklarA
#> 10: Stanley consortium collection Cerebellum - SklarB
#> 11: Stanley array collection DLPFC - Vawter
#> 12: Stanley array collection DLPFC - Young
#> 13: Stanley array collection DLPFC - Altar A
#> 14: Stanley consortium collection DLPFC - Altar B
#> 15: Stanley consortium collection DLPFC - Altar C
#> 16: Stanley array collection DLPFC - Bahn
#> 17: Stanley consortium collection DLPFC - Chen
#> 18: Stanley array collection DLPFC - Dobrin
#> 19: Laser capture microdissection of endothelial and neuronal cells from human dorsolateral prefrontal cortex
#> 20: Gene expression from human prefrontal cortex (BA10)
#> experiment.name
#> experiment.ID
#> <int>
#> 1: 326
#> 2: 348
#> 3: 594
#> 4: 660
#> 5: 670
#> 6: 672
#> 7: 831
#> 8: 833
#> 9: 834
#> 10: 835
#> 11: 836
#> 12: 837
#> 13: 838
#> 14: 839
#> 15: 840
#> 16: 841
#> 17: 842
#> 18: 843
#> 19: 946
#> 20: 1204
#> experiment.ID
#> experiment.description
#> <char>
#> 1: Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (orbitofrontal cortex) from patients with bipolar disorder and matched healthy controls. Orbitofrontal cortex tissue from a cohort of 30 subjects was investigated and the final analysis included 10 bipolar and 11 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nNote: [] samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: \nLast Updated (by provider): Jul 27 2006\nContributors: Sabine Bahn Margaret M Ryan Matthew T Wayland Maree J Webster Stephen J Huffaker Helen E Lockstone\nIncludes GDS2191.\n Update date: Aug 28 2006.\n Dataset description GDS2191: Analysis of postmortem orbitofrontal cortex from 10 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease.
#> 2: Schwann cells, expanded in number by exposure to growth factors in vitro, could be useful in nervous system repair. Our previous results suggest that long term exposure to heregulin and forskolin changes the functional properties of the human Schwann cells, including the ability to myelinate axons after transplantation. Here, we propose to determine the molecular changes in the Schwann cells that occur as a result of extended growth with mitogenic factors. We believe that the information obtained in these studies will provide clues about mechanisms underlying the already observed changes in function. This information will aid in the prediction of the safety and efficacy of neural repair approaches that use cultured, expanded Schwann cells. Finally this data may provide clues into the mechanisms underlying normal human Schwann cell function. To use gene array analysis to compare gene expression profiles in early and late passage human Schwann cells exposed to the growth factors heregulin and forskolin. Observed changes in the function of human Schwann cells, including their capacity for growth and differentiation, after prolonged exposure to heregulin and forskolin, are caused by changes in the gene expression profiles in these cells. Nerves from four different donors were obtained within 12-15 h postmortem, with full consent for research from the families of the donors (faxed). IRB approval not required because subjects were officially dead at nerve harvest (IRB confirmation faxed). The nerve tissue obtained consisted of the lower 15 cm of nerve extending from mid calf to ankle. Endoneurial fascicles were dissected from short (1-2 cm) nerve fragments and cultured with heregulin and forskolin for 1 week. The nerve fascicles were then treated overnight with collagenase and dispase and dissociated by gentle trituration. Cells were plated at low density on laminin coated dishes and maintained in culture with mitogens for 3 passages. Schwann cell purities during this period were > 95%. RNA was extracted from the cells at passage 1 ( i.e. after 2 population doublings) and passage 3 (i.e. after 8 population doublings) from the following donors: SN HSC 330, age 51; SN HSC 317, age 29; SN HSC 329, age 20 and SN HSC 351, age 42. Thus eight samples (2 groups, 4 independent samples per group) are to be analyzed. Because both groups in this comparison have been exposed to mitogens, differences in gene expression profiles will be interpreted as indicative of changes caused by prolonged versus short term exposure to mitogens. More RNA was prepared from each group of cells than was needed for gene array analysis to allow confirmation of differentially-expressed gene transcripts by real-time RT-PCR. Since the main purpose of this project is to detect any major changes in the molecular properties of the Schwann cells, we propose that these samples be analyzed using Affymetrix U133 + 2 arrays containing the complete human genome and request Option 1: Start to Finish Profiling of all samples. We have contacted Dr Stanley Nelson at UCLA and discussed the project with him. We request that the samples be analyzed in the UCLA facility.\nNote: [] samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: \nLast Updated (by provider): Jan 13 2006\nContributors: Mary B Bunge\nIncludes GDS1869.\n Update date: Jun 14 2006.\n Dataset description GDS1869: Analysis of passage 1 and passage 3 Schwann cell cultures obtained from four donors and maintained in the presence of the heregulin and forskolin. Heregulin and forskolin synergistically drive Schwann cell proliferation in vitro.
#> 3: Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (dorsolateral prefrontal cortex) from patients with bipolar disorder and matched healthy controls. A cohort of 70 subjects was investigated and the final analysis included 30 bipolar and 31 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nLast Updated (by provider): Jan 16 2007\nContributors: Helen E Lockstone Stephen J Huffaker Matthew T Wayland Sabine Bahn Maree J Webster Margaret M Ryan\nIncludes GDS2190.\n Update date: Aug 28 2006.\n Dataset description GDS2190: Analysis of postmortem dorsolateral prefrontal cortex from 30 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease.
#> 4: To identify genes dysregulated in bipolar disorder (BD1) we carried out global gene expression profiling using whole-genome microarrays. To minimize genetic variation in gene expression levels between cases and controls we compared expression profiles in lymphoblastoid cell lines from monozygotic twin pairs discordant for the disease. We identified 82 genes that were differentially expressed by ? 1.3-fold in 3 BD1 cases compared to their co-twins, and which were statistically (p ? 0.05) differentially expressed between the groups of BD1 cases and controls. Using qRT-PCR we confirmed the differential expression of some of these genes, including: KCNK1, MAL, PFN2, TCF7, PGK1, and PI4KCB, in at least 2 of the twin pairs. In contrast to the findings of a previous study by Kakiuchi and colleagues with similar discordant BD1 twin design1 our data do not support the dysregulation of XBP1 and HSPA5. From pathway and gene ontology analysis we identified up-regulation of the WNT signalling pathway and the biological process of apoptosis. The differentially regulated genes and pathways identified in this study may provide insights into the biology of BD1.\nLast Updated (by provider): Jun 20 2007\nContributors: Louisa Windus Nicholas Matigian Bryan Mowry Cheryl Filippich John McGrath Heather Smith Nicholas Hayward Christos Pantelis
#> 5: Hippocampus of schizophrenic, bipolar, and control subjects. Analyzed from CEL files.
#> 6: Prefrontal cortex of schizophrenic, bipolar, and control subjects. This is the "McLean 66"
#> 7: 50 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the Cerebellum.
#> 8: 102 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
#> 9: 47 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the DLPFC Broadmann area 8/9.
#> 10: 46 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the Cerebellum.
#> 11: 98 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
#> 12: 105 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
#> 13: 98 samples of individuals from three different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
#> 14: 39 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the DLPFC Broadmann area 46/10.
#> 15: 44 samples of individuals from four different diagnostic groups: depression, bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46/10.
#> 16: 99 samples of individuals from three different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
#> 17: 27 samples of individuals from two different diagnostic groups: bipolar, and controls. Samples taken from the DLPFC Brodmann area 6.
#> 18: 78 samples of individuals from three different diagnostic groups: bipolar, schizophrenia and controls. Samples taken from the DLPFC Broadmann area 46.
#> 19: We used laser capture microdissection to isolate both microvascular endothelial cells and neurons from post mortem brain tissue from patients with schizophrenia and bipolar disorder and healthy controls. RNA was isolated from these cell populations, amplified, and analysed using Affymetrix HG133plus2.0 GeneChips. In the first instance, we used the dataset to compare the neuronal and endothelial data, in order to demonstrate that the predicted differences between cell types could be detected using this methodology. \nLast Updated (by provider): Dec 18 2008\nContributors: Margaret M Ryan Thomas Giger Martin J Lan Matthew T Wayland Mark Kotter Michael L Mimmack Laura W Harris Lan Wang Irene Wuethrich Helen Lockstone Sabine Bahn
#> 20: We performed the oligonucleotide microarray analysis in bipolar disorder, major depression, schizophrenia, and control subjects using postmortem prefrontal cortices provided by the Stanley Foundation Brain Collection. By comparing the gene expression profiles of similar but distinctive mental disorders, we explored the uniqueness of bipolar disorder and its similarity to other mental disorders at the molecular level. Notably, most of the altered gene expressions in each disease were not shared by one another, suggesting the molecular distinctiveness of these mental disorders. We found a tendency of downregulation of the genes encoding receptor, channels or transporters, and upregulation of the genes encoding stress response proteins or molecular chaperons in bipolar disorder. Altered expressions in bipolar disorder shared by other mental disorders mainly consisted of upregulation of the genes encoding proteins for transcription or translation. The genes identified in this study would be useful for the understanding of the pathophysiology of bipolar disorder, as well as the common pathophysiological background in major mental disorders at the molecular level.\nLast Updated (by provider): Mar 15 2009\nContributors: Tadafumi Kato Kazuya Iwamoto Chihiro Kakiuchi Kazuhiko Ikeda Miki Bundo
#> experiment.description
#> experiment.troubled experiment.accession experiment.database
#> <lgcl> <char> <char>
#> 1: FALSE GSE5389 GEO
#> 2: FALSE GSE4030 GEO
#> 3: FALSE GSE5388 GEO
#> 4: FALSE GSE7036 GEO
#> 5: FALSE <NA> <NA>
#> 6: FALSE <NA> <NA>
#> 7: FALSE <NA> <NA>
#> 8: FALSE <NA> <NA>
#> 9: FALSE <NA> <NA>
#> 10: FALSE <NA> <NA>
#> 11: FALSE <NA> <NA>
#> 12: FALSE <NA> <NA>
#> 13: FALSE <NA> <NA>
#> 14: FALSE <NA> <NA>
#> 15: FALSE <NA> <NA>
#> 16: FALSE <NA> <NA>
#> 17: FALSE <NA> <NA>
#> 18: FALSE <NA> <NA>
#> 19: FALSE GSE12679 GEO
#> 20: FALSE GSE12654 GEO
#> experiment.troubled experiment.accession experiment.database
#> experiment.URI
#> <char>
#> 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5389
#> 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4030
#> 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5388
#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7036
#> 5: <NA>
#> 6: <NA>
#> 7: <NA>
#> 8: <NA>
#> 9: <NA>
#> 10: <NA>
#> 11: <NA>
#> 12: <NA>
#> 13: <NA>
#> 14: <NA>
#> 15: <NA>
#> 16: <NA>
#> 17: <NA>
#> 18: <NA>
#> 19: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12679
#> 20: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12654
#> experiment.URI
#> experiment.sampleCount experiment.lastUpdated experiment.batchEffectText
#> <int> <POSc> <char>
#> 1: 21 2024-07-05 07:33:34 NO_BATCH_EFFECT_SUCCESS
#> 2: 8 2023-12-19 13:21:14 SINGLE_BATCH_SUCCESS
#> 3: 61 2023-12-20 07:59:57 NO_BATCH_EFFECT_SUCCESS
#> 4: 6 2023-12-20 21:45:32 SINGLE_BATCH_SUCCESS
#> 5: 23 2023-09-08 07:38:04 NO_BATCH_INFO
#> 6: 66 2023-09-21 21:43:05 NO_BATCH_INFO
#> 7: 50 2024-08-15 17:25:45 NO_BATCH_INFO
#> 8: 102 2023-11-30 00:38:00 NO_BATCH_INFO
#> 9: 47 2023-09-07 23:15:06 NO_BATCH_INFO
#> 10: 46 2023-09-07 23:15:42 NO_BATCH_INFO
#> 11: 98 2024-08-15 17:42:31 NO_BATCH_INFO
#> 12: 105 2023-12-06 22:52:55 NO_BATCH_INFO
#> 13: 98 2023-12-06 18:45:01 NO_BATCH_INFO
#> 14: 39 2023-06-09 16:41:44 NO_BATCH_INFO
#> 15: 44 2023-09-07 23:17:50 NO_BATCH_INFO
#> 16: 99 2023-12-07 20:23:13 NO_BATCH_INFO
#> 17: 27 2023-09-07 23:19:35 NO_BATCH_INFO
#> 18: 78 2023-12-11 18:39:23 NO_BATCH_INFO
#> 19: 32 2023-12-16 10:02:53 BATCH_EFFECT_FAILURE
#> 20: 50 2023-12-16 09:50:20 BATCH_EFFECT_FAILURE
#> experiment.sampleCount experiment.lastUpdated experiment.batchEffectText
#> experiment.batchCorrected experiment.batchConfound experiment.batchEffect
#> <lgcl> <num> <num>
#> 1: FALSE 1 1
#> 2: FALSE 1 1
#> 3: FALSE 1 1
#> 4: FALSE 1 1
#> 5: FALSE 0 0
#> 6: FALSE 0 0
#> 7: FALSE 0 0
#> 8: FALSE 0 0
#> 9: FALSE 0 0
#> 10: FALSE 0 0
#> 11: FALSE 0 0
#> 12: FALSE 0 0
#> 13: FALSE 0 0
#> 14: FALSE 0 0
#> 15: FALSE 0 0
#> 16: FALSE 0 0
#> 17: FALSE 0 0
#> 18: FALSE 0 0
#> 19: FALSE -1 0
#> 20: FALSE 1 0
#> experiment.batchCorrected experiment.batchConfound experiment.batchEffect
#> experiment.rawData geeq.qScore geeq.sScore taxon.name taxon.scientific
#> <num> <num> <num> <char> <char>
#> 1: 1 0.8517696 0.8750 human Homo sapiens
#> 2: 1 0.4264563 0.8375 human Homo sapiens
#> 3: -1 0.9948828 0.6250 human Homo sapiens
#> 4: 1 0.7121314 0.8375 human Homo sapiens
#> 5: -1 0.2819105 0.2500 human Homo sapiens
#> 6: -1 0.1370016 0.1250 human Homo sapiens
#> 7: -1 0.1411402 0.0000 human Homo sapiens
#> 8: -1 0.1385422 0.1250 human Homo sapiens
#> 9: -1 0.1346463 0.2500 human Homo sapiens
#> 10: -1 0.1370357 0.2500 human Homo sapiens
#> 11: -1 0.1354051 -0.1250 human Homo sapiens
#> 12: -1 -0.1806104 0.0625 human Homo sapiens
#> 13: -1 0.1379511 0.1250 human Homo sapiens
#> 14: -1 -0.1582131 0.0625 human Homo sapiens
#> 15: -1 0.1402020 0.1250 human Homo sapiens
#> 16: -1 0.1368111 0.3750 human Homo sapiens
#> 17: -1 0.1404539 0.1875 human Homo sapiens
#> 18: -1 0.1368095 0.1875 human Homo sapiens
#> 19: 1 0.5457480 1.0000 human Homo sapiens
#> 20: 1 0.8539396 0.6875 human Homo sapiens
#> experiment.rawData geeq.qScore geeq.sScore taxon.name taxon.scientific
#> taxon.ID taxon.NCBI taxon.database.name taxon.database.ID
#> <int> <int> <char> <int>
#> 1: 1 9606 hg38 87
#> 2: 1 9606 hg38 87
#> 3: 1 9606 hg38 87
#> 4: 1 9606 hg38 87
#> 5: 1 9606 hg38 87
#> 6: 1 9606 hg38 87
#> 7: 1 9606 hg38 87
#> 8: 1 9606 hg38 87
#> 9: 1 9606 hg38 87
#> 10: 1 9606 hg38 87
#> 11: 1 9606 hg38 87
#> 12: 1 9606 hg38 87
#> 13: 1 9606 hg38 87
#> 14: 1 9606 hg38 87
#> 15: 1 9606 hg38 87
#> 16: 1 9606 hg38 87
#> 17: 1 9606 hg38 87
#> 18: 1 9606 hg38 87
#> 19: 1 9606 hg38 87
#> 20: 1 9606 hg38 87
#> taxon.ID taxon.NCBI taxon.database.name taxon.database.ID