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.
Project description:miRNAs levels were measured in plasma samples of pancreatic cancer cases and controls collected within 5 years prior to diagnosis using the NanoString nCounter Human v3 miRNA expression panel
Project description:EVP miRNAs levels were measured in 54 plasma samples collected approximately 24-28 weeks gestation from individuals in New Hampshire using the NanoString nCounter Human v3 miRNA expression panel
Project description:EVP miRNAs levels were measured in the supernatant fraction of 54 human milk samples collected approximately 6 weeks postpartum from individuals in New Hampshire using the NanoString nCounter Human v3 miRNA expression panel
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
