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Mus musculus
65 Downloadable Samples
Illumina HiSeq 2000, Illumina HiSeq 1000
Description
We report novel single-cell RNA-Seq, called Quartz-Seq. Quartz-Seq was simplified method compared with previous methods based on poly-A tailing reaction. Overall design: RNA-seq by illumina TruSeq, KAPA library preparation kit, single-cell Quartz-Seq and single-cell Smart-Seq by illumina HiSeq 2000/1000
Publication Title
Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 18 Downloadable Samples
Illumina HumanHT-12 V4.0 expression beadchip
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Chromatin remodeler CHD7 regulates the stem cell identity of human neural progenitors.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Homo sapiens
- 92 Downloadable Samples
- Illumina HiSeq 2500
Description
CHARGE syndrome is a congenital disorder caused by mutations in Chromodomain Helicase DNA-binding domain 7 (CHD7) gene. We performed single cell RNA-seq analysis in CTRL and CHD7-knockdown lt-NES cells. Overall design: Single cell RNA-Seq profiling of control (shCTRL) and CHD7-knockdown (sh410 or sh411) cells.
Publication Title
Chromatin remodeler CHD7 regulates the stem cell identity of human neural progenitors.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Homo sapiens
- 18 Downloadable Samples
Illumina HumanHT-12 V4.0 expression beadchip
Description
We performed a microarray experiment to analyze the transcriptional profile of human iPSC-derived neural stem/progenitor cells to identify CHD7 target genes
Publication Title
Chromatin remodeler CHD7 regulates the stem cell identity of human neural progenitors.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 191 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of three years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest five-fold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of the current data set was sufficient to provide a robust classifier. The classification results showed a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models over-predicted the tumor response, but the variability in predictions were significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically-induced mouse lung tumors as well as a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related endpoints.
Publication Title
Use of short-term transcriptional profiles to assess the long-term cancer-related safety of environmental and industrial chemicals.
Sample Metadata Fields
Sex, Age, Specimen part, Disease, Subject
View Samples- Mus musculus
- 16 Downloadable Samples
- Affymetrix Mouse Gene 2.1 ST Array (mogene21st)
Description
miR-146a acts as a negative feedback regulator of inflammation. To investigate the role of miR-146a in psoriasis psoriasiform skin inflammation was indeuced in Mir-146a-/- and wild type mice (C57BL6J) by topical applciation of imiquimod (IMQ)-cream (Aldara).
Publication Title
MicroRNA-146a suppresses IL-17-mediated skin inflammation and is genetically associated with psoriasis.
Sample Metadata Fields
Age, Specimen part
View Samples- Mus musculus, Homo sapiens, Rattus norvegicus
- 1314 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Age, Specimen part, Race, Compound
View Samples- Homo sapiens
- 549 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
The multiple myeloma (MM) data set (endpoints F, G, H, and I) was contributed by the Myeloma Institute for Research and Therapy at the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA). Gene expression profiling of highly purified bone marrow plasma cells was performed in newly diagnosed patients with MM. The training set consisted of 340 cases enrolled on total therapy 2 (TT2) and the validation set comprised 214 patients enrolled in total therapy 3 (TT3). Plasma cells were enriched by anti-CD138 immunomagnetic bead selection of mononuclear cell fractions of bone marrow aspirates in a central laboratory. All samples applied to the microarray contained more than 85% plasma cells as determined by 2-color flow cytometry (CD38+ and CD45-/dim) performed after selection. Dichotomized overall survival (OS) and eventfree survival (EFS) were determined based on a two-year milestone cutoff. A gene expression model of high-risk multiple myeloma was developed and validated by the data provider and later on validated in three additional independent data sets.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Age
View Samples
GSE24363- Rattus norvegicus
- 410 Downloadable Samples
- Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a)
Description
The NIEHS data set (endpoint C) was provided by the National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health (Research Triangle Park, NC, USA). The study objective was to use microarray gene expression data acquired from the liver of rats exposed to hepatotoxicants to build classifiers for prediction of liver necrosis. The gene expression compendium data set was collected from 418 rats exposed to one of eight compounds (1,2-dichlorobenzene, 1,4-dichlorobenzene, bromobenzene, monocrotaline, N-nitrosomorpholine, thioacetamide, galactosamine, and diquat dibromide). All eight compounds were studied using standardized procedures, i.e. a common array platform (Affymetrix Rat 230 2.0 microarray), experimental procedures and data retrieving and analysis processes.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Specimen part, Compound
View Samples- Homo sapiens
- 267 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
The human breast cancer (BR) data set (endpoints D and E) was contributed by the University of Texas M. D. Anderson Cancer Center (MDACC, Houston, TX, USA). Gene expression data from 230 stage I-III breast cancers were generated from fine needle aspiration specimens of newly diagnosed breast cancers before any therapy. The biopsy specimens were collected sequentially during a prospective pharmacogenomic marker discovery study between 2000 and 2008. These specimens represent 70-90% pure neoplastic cells with minimal stromal contamination. Patients received 6 months of preoperative (neoadjuvant) chemotherapy including paclitaxel, 5-fluorouracil, cyclophosphamide and doxorubicin followed by surgical resection of the cancer. Response to preoperative chemotherapy was categorized as a pathological complete response (pCR = no residual invasive cancer in the breast or lymph nodes) or residual invasive cancer (RD), and used as endpoint D for prediction. Endpoint E is the clinical estrogen-receptor status as established by immunohistochemistry. RNA extraction and gene expression profiling were performed in multiple batches over time using Affymetrix U133A microarrays. Genomic analysis of a subset of this sequentially accrued patient population were reported previously. For each endpoint, the first 130 cases were used as a training set and the next 100 cases were used as an independent validation set.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Age, Specimen part, Race
View Samples