Relevant bibliographies by topics / NanoString nCounter data

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  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'NanoString nCounter data'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "NanoString nCounter data"

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Brumbaugh, Christopher D., Hyunsung J. Kim, Mario Giovacchini, and Nader Pourmand. "NanoStriDE: normalization and differential expression analysis of NanoString nCounter data." BMC Bioinformatics 12, no. 1 (2011): 479. http://dx.doi.org/10.1186/1471-2105-12-479.

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Molania, Ramyar, Johann A. Gagnon-Bartsch, Alexander Dobrovic, and Terence P. Speed. "A new normalization for Nanostring nCounter gene expression data." Nucleic Acids Research 47, no. 12 (2019): 6073–83. http://dx.doi.org/10.1093/nar/gkz433.

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AbstractThe Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples us
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Ciombor, Kristen Keon, Natasha G. Deane, Keeli B. Lewis, et al. "Colorectal cancer gene expression profiling using nanostring nCounter analysis." Journal of Clinical Oncology 31, no. 15_suppl (2013): 3555. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.3555.

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3555 Background: A more accurate method of identifying stage 2 and 3 colorectal cancer (CRC) patients at highest risk for recurrence after surgical resection is needed. Gene expression signatures utilizing microarray-derived gene expression data from fresh frozen primary CRCs to predict risk of recurrence have been developed by us and others. Advances in technology platforms for gene expression measurements and their applicability to formalin-fixed, paraffin-embedded (FFPE) specimens offer new opportunity to develop clinically useful diagnostics based on molecular profiles. Methods: 58 patient
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Ho, Ben, Anthony Arnoldo, Yvonne Zhong, et al. "ATRT-33. ENABLING RAPID CLASSIFICATION OF ATRT WITH NANOSTRING NCOUNTER PLATFORM." Neuro-Oncology 22, Supplement_3 (2020): iii282. http://dx.doi.org/10.1093/neuonc/noaa222.031.

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Abstract In recent years, using gene expression and methylation array platform, multiple research groups have reported the presence of at least three major Atypical Teratoid Rhabdoid Tumor (ATRT) subtypes that exhibit distinct epigenetic, transcriptomic and clinical features. Yet, utilizing ATRT subtypes in a clinical setting remains challenging due to a lack of suitable biological markers, limited sample quantities and relatively high cost of current assays. To address this gap between research and clinical practice, we have designed an assay that utilizes a custom 35 signature genes panel fo
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Delmonico, Lucas, Said Attiya, Joan W. Chen, John C. Obenauer, Edward C. Goodwin, and Marcia V. Fournier. "Expression Concordance of 325 Novel RNA Biomarkers between Data Generated by NanoString nCounter and Affymetrix GeneChip." Disease Markers 2019 (May 14, 2019): 1–12. http://dx.doi.org/10.1155/2019/1940347.

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Background. With the development of new drug combinations and targeted treatments for multiple types of cancer, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important. A panel of 325 RNA biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization. This “cancer in reverse” approach resulted in a panel of biomarkers relevant for at least 7 cancer types, providing gene expression profiles representing key cellula
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Jia, Gaoxiang, Xinlei Wang, Qiwei Li, et al. "RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data." Annals of Applied Statistics 13, no. 3 (2019): 1617–47. http://dx.doi.org/10.1214/19-aoas1249.

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Morales La Madrid, Andres Eduardo, Thomas Stricker, Peter Pytel, et al. "Genome-based outcome prediction in MYCN nonamplified high-risk neuroblastoma." Journal of Clinical Oncology 30, no. 15_suppl (2012): 9534. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.9534.

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9534 Background: Less than 40% of children with high-risk neuroblastoma achieve long-term survival, and at diagnosis, it is not possible to identify patients who will be cured. Microarray studies have proposed expression signatures associated with outcome within high-risk cohorts. However, integrating this technology as a clinical test has been difficult, in part due to the lack of available frozen tissue and high quality RNA. The nCounter overcomes this obstacle, using formalin-fixed paraffin embedded tissue (FFPE). Our objective is to test the correlation of a previously published “ultra hig
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8

Alì, Greta, Rossella Bruno, Mauro Savino, et al. "Analysis of Fusion Genes by NanoString System: A Role in Lung Cytology?" Archives of Pathology & Laboratory Medicine 142, no. 4 (2018): 480–89. http://dx.doi.org/10.5858/arpa.2017-0135-ra.

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Context.— Patients with non–small cell lung cancer harboring ALK receptor tyrosine kinase (ALK), ROS proto-oncogene 1 (ROS1), and ret proto-oncogene (RET) gene rearrangements can benefit from specific kinase inhibitors. Detection of fusion genes is critical for determining the best treatment. Assessing rearrangements in non–small cell lung cancer remains challenging, particularly for lung cytology. Objective.— To examine the possible application of the multiplex, transcript-based NanoString system (NanoString Technologies, Seattle, Washington) in the evaluation of fusion genes in lung adenocar
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Wan Nor Ruddin, Wan Alif Afiq, Lailatul Jalilah Mohd Ridah, Nurul Yaqin Mohd Nor, Hamizah Ismail, and Norafiza Zainuddin. "Optimization of Cell-free Plasma RNA Extraction for Downstream Application." Science Letters 15, no. 2 (2021): 60–78. http://dx.doi.org/10.24191/sl.v15i2.13828.

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The growing interest in biomedical studies has brought RNA from biofluids including plasma, as promising candidates for genetics profiling. The precision and reliability of an analysis in downstream application such as NanoString nCounter® MAX Analysis System (NanoString Technologies, Seattle, WA) ) depend on the RNA quality, purity and level. In this project, NanoString nCounter® miRNA panel was chosen due to rapid identification and ability to profile approximately 800 miRNAs per run which requires total RNAs from plasma with a minimum concentration of 33.3 ng/μL with 260/280 and 260/230 rat
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Chen, Xi, Natasha G. Deane, Keeli B. Lewis, et al. "Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues." PLOS ONE 11, no. 5 (2016): e0153784. http://dx.doi.org/10.1371/journal.pone.0153784.

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Dissertations / Theses on the topic "NanoString nCounter data"

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Shen, Shu. "Developing An Alternative Way to Analyze NanoString Data." UKnowledge, 2016. http://uknowledge.uky.edu/statistics_etds/20.

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Nanostring technology provides a new method to measure gene expressions. It's more sensitive than microarrays and able to do more gene measurements than RT-PCR with similar sensitivity. This system produces counts for each target gene and tabulates them. Counts can be normalized by using an Excel macro or nSolver before analysis. Both methods rely on data normalization prior to statistical analysis to identify differentially expressed genes. Alternatively, we propose to model gene expressions as a function of positive controls and reference gene measurements. Simulations and examples are used
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Conference papers on the topic "NanoString nCounter data"

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Hoffman, Mariah M., Carrie J. Minette, Shanta M. Messerli, Ratan D. Bhardwaj, and Etienne Z. Gnimpieba. "NanoStringBioNet: Integrated R framework for bioscience knowledge discovery from NanoString nCounter data." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217992.

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