Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Addressing transcriptomic assay heterogeneity for predictive modeling in cancer (RNA-seq)

ABSTRACT: The goal of this study was to evaluate how best to preprocess RNA-seq and NanoString data to, if possible, combine these data for larger cohort sizes in predictive modeling of clinical features. High-grade serous Ovarian Cancer is a highly heterogeneous disease with most data split into Microarray, NanoString, and RNA-seq. NanoString and RNA-seq showed the greatest similarities in dynamic range across these datasets. Therefore, we performed bulk RNA-seq and NanoString (PanCancer IO360 panel) on sequential samples of 26 patients to evaluate how comparable gene expression patterns were captured and if preprocessing steps could improve this. In the preprocessing steps for RNA-seq, we considered counting over genes or exons to which NanoString probes mapped.

ORGANISM(S): Homo sapiens

PROVIDER: GSE323347 | GEO | 2026/03/31

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	GSE323347_RNAMatch_str_gtf_genes.txt.gz	Txt
	GSE323347_RNAMatch_str_gtf_probeexons.txt.gz	Txt

Items per page:

1 - 3 of 3

Similar Datasets

Addressing transcriptomic assay heterogeneity for predictive modeling in cancer (NanoString)

Project description:The goal of this study was to evaluate how best to preprocess RNA-seq and NanoString data to, if possible, combine these data for larger cohort sizes in predictive modeling of clinical features. High-grade serous Ovarian Cancer is a highly heterogeneous disease with most data split into Microarray, NanoString, and RNA-seq. NanoString and RNA-seq showed the greatest similarities in dynamic range across these datasets. Therefore, we performed bulk RNA-seq and NanoString (PanCancer IO360 panel) on sequential samples of 26 patients to evaluate how comparable gene expression patterns were captured and if preprocessing steps could improve this. In the preprocessing steps for RNA-seq, we considered counting over genes or exons to which NanoString probes mapped.

2026-03-31 | GSE322784 | GEO

Spatially Resolved Transcriptomics Deconvolutes Histological Prognostic Subgroups in Patients with Colorectal Cancer and Synchronous Liver Metastases

Project description:This bulk mRNA data was generated using the Nanostring ncounter platform using an io360 pancancer panel of 900 genes as part of a wider project which included spatial transcriptomic data generated using the Nanostring GeoMx platform. In this study we compared tissue surgical resected from patients with colorectal cancer and liver metastases.

2023-02-02 | GSE224235 | GEO

Parity-conditioned Hedgehog/VDR-linked NanoString profiling of histologically normal human breast tissue (IO360)

Project description:Histologically normal human breast FFPE specimens were profiled by NanoString nCounter to study parity-conditioned basal/progenitor, Hedgehog, and vitamin D receptor-associated biology in normal breast tissue. This workbook contains the PanCancer IO 360 (IO360) arm of the study and is paired with a companion workbook for the other NanoString panel. Each of the 74 analyzed specimens contributes one GEO sample to this platform-specific workbook and one GEO sample to the companion panel workbook.

2026-03-17 | GSE324676 | GEO

Carolina Breast Cancer Study tumor expression [NanoString nSolver-normalized]

Project description:Transcription profiling by NanoString nCounter of primary breast tumors from 1219 patients from the Carolina Breast Cancer Study (CBCS) using the NanoString nCounter platform and normalized with NanoString nSolver software. The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility, and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization, and iterative data visualization and biological validation. The approach was evaluated using a large cohort from the Carolina Breast Cancer Study. The iterative process developed here eliminates technical variation more reliably than the NanoString commercial package, without diminishing biological variation, especially in long-term longitudinal multi-phase or multi-site cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that preprocessing of gene expression data is an important component of study design. The normalized data here is processed through the RUVSeq-based iterative framework

2020-07-16 | GSE148418 | GEO

Consensus Preprocessing of GEO and ArrayExpress Normal and Diseased Lung Tissue Microarray Data

Project description:The Affymetrix oligonucleotide microarrays measure gene expression by quantifying intensity of fluorescently labeled gene fragments that bind to sets of 25-mer oligonucleotide probes on the chip with specific sequences tailored to be complementary to the target genes. Each gene is associated with a "probe set" containing several pairs (usually 11) of "perfect match" (perfectly complementary to target sequence) and "mismatch" (different base at position 13 of 25) probes. The raw measurements of each probe set consist of a set of intensities from the probes, which require in silico preprocessing by (1) correcting for background variability, (2) normalizing intensities across samples, and (3) summarizing intensities across the probe set into a single expression value. The output of the summarization step corresponds to the background-adjusted value for the mRNA of interest. We preprocess using GCRMA, which corrects for background variability by accounting for optical noise, probe affinity, and mismatch probe adjustment; normalizes intensities by quantile normalization; and summarizes intensities using a median polish method. To minimize preprocessing batch effects, it is desirable to preprocess all samples in the dataset together. However, preprocessing across multple platforms requires a consolidation of probes with identical sequences, precluding global preprocessing on datasets with multiple platforms using the standard preprocessing pipelines. To address this problem, we have developed and applied a custom preprocessing pipeline to combine the raw .CEL files from multiple platforms that share the same probe sets.

2014-09-18 | GSE60486 | GEO

Carolina Breast Cancer Study tumor expression [RUVSeq-normalized]

Project description:Transcription profiling by NanoString nCounter of primary breast tumors from 1219 patients from the Carolina Breast Cancer Study (CBCS) using the NanoString nCounter platform and normalized with a RUVSeq-based iterative framework. The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility, and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization, and iterative data visualization and biological validation. The approach was evaluated using a large cohort from the Carolina Breast Cancer Study. The iterative process developed here eliminates technical variation more reliably than the NanoString commercial package, without diminishing biological variation, especially in long-term longitudinal multi-phase or multi-site cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that preprocessing of gene expression data is an important component of study design. The normalized data here is processed through the RUVSeq-based iterative framework

2020-07-16 | GSE148425 | GEO

MYC-HCC treated with immunotherapy

Project description:Murine MYC-HCC were treated with control IgG or anti PDL1 or anti CTLA4, or their combination. Tumors were sequenced using Nanostring IO360.

2023-02-01 | GSE215006 | GEO

Transcriptomic analysis of orthotopic pancreatic tumors treated by Axl inhibitor TP-0903 or combination with chemo/immunotherapy using the nCounter PanCancer Immune Profiling panel (Nanostring Technologies) [mouse]

Project description:RNA isolated from KPfC orthotopic tumors was analyzed using Mouse PanCancer Immune Profiling Panel (NanoString Technologies).

2021-11-01 | GSE185110 | GEO

NanoString PanCancer IO 360 expression profiling of histologically normal breast lobules from NHBW and NHWW donors (IO360)

Project description:NanoString PanCancer IO 360 expression profiling of histologically normal breast lobules from NHBW and NHWW donors (IO360)

| PRJNA1401792 | ENA

Tumour Infiltrating Lymphocytes and Immune-Related Genes as Predictors of Outcome in Pancreatic Adenocarcinoma

Project description:In this study, we analyzed a total of 12 patients affected by pancreatic tumour split into two different groups based on their outcomes (6 good vs 6 worse prognoses). After the IHC of different populations within cancer, we randomly selected 3 patients for each group and performed a gene expression analysis with Nanostring human PanCancer immune module.

2019-06-18 | E-MTAB-8079 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data