This mini-review discusses the challenges and opportunities in processing NanoString nCounter data. NanoString nCounter is a medium-throughput technology used for mRNA and miRNA differential expression studies. It offers advantages such as no amplification step and the ability to analyze low-quality samples. However, its popularity in experimental research has stabilized in 2022 and 2023, possibly due to a lack of standardized analytical pipelines or optimal processing methods. The authors divided the nCounter data analysis workflow into five steps: data pre-processing, quality control, background correction, normalization, and differential expression analysis. They evaluated eleven R packages for nCounter data processing to identify functionalities belonging to these steps and provide comments on their applications in mRNA and miRNA studies. The nCounter workflow involves hybridization of reporter and capture probes with the target, purification and immobilization of the sample, and detection using a Digital Analyzer. The Digital Analyzer produces a Reporter Code Count (RCC) file and a Reporter Library File (RLF) for further analysis. The nCounter platform has shown good performance in benchmark studies, often outperforming other solutions for mRNA and miRNA analysis. However, it is less sensitive than other methods for miRNA profiling. The nCounter is suitable for quantifying RNA species, particularly mRNAs, but has limitations in detecting low-abundance RNA species. It is less stringent on sample quality, making it suitable for low-grade clinical trial specimens. The nCounter is also less sensitive to minor changes in gene expression compared to other methods. The nCounter is predominantly used in assays to quantify RNA species, particularly mRNAs, but has limitations in detecting low-abundance RNA species. The nCounter data processing involves several steps, including background correction, normalization, and differential expression analysis. The nCounter data processing workflow includes quality control, background correction, normalization, and differential expression analysis. The authors evaluated eleven R packages for nCounter data processing and categorized their functionalities into these steps. They also discussed the advantages and limitations of the nCounter platform, including its suitability for absolute quantification of biomarkers and its sensitivity to variability in gene expression counts. The authors concluded that the nCounter platform is a well-balanced approach between amplification-dependent methods and high-multiplex approaches. They recommended using NanoTube for mRNA data analysis and nSolver for miRNA data processing.This mini-review discusses the challenges and opportunities in processing NanoString nCounter data. NanoString nCounter is a medium-throughput technology used for mRNA and miRNA differential expression studies. It offers advantages such as no amplification step and the ability to analyze low-quality samples. However, its popularity in experimental research has stabilized in 2022 and 2023, possibly due to a lack of standardized analytical pipelines or optimal processing methods. The authors divided the nCounter data analysis workflow into five steps: data pre-processing, quality control, background correction, normalization, and differential expression analysis. They evaluated eleven R packages for nCounter data processing to identify functionalities belonging to these steps and provide comments on their applications in mRNA and miRNA studies. The nCounter workflow involves hybridization of reporter and capture probes with the target, purification and immobilization of the sample, and detection using a Digital Analyzer. The Digital Analyzer produces a Reporter Code Count (RCC) file and a Reporter Library File (RLF) for further analysis. The nCounter platform has shown good performance in benchmark studies, often outperforming other solutions for mRNA and miRNA analysis. However, it is less sensitive than other methods for miRNA profiling. The nCounter is suitable for quantifying RNA species, particularly mRNAs, but has limitations in detecting low-abundance RNA species. It is less stringent on sample quality, making it suitable for low-grade clinical trial specimens. The nCounter is also less sensitive to minor changes in gene expression compared to other methods. The nCounter is predominantly used in assays to quantify RNA species, particularly mRNAs, but has limitations in detecting low-abundance RNA species. The nCounter data processing involves several steps, including background correction, normalization, and differential expression analysis. The nCounter data processing workflow includes quality control, background correction, normalization, and differential expression analysis. The authors evaluated eleven R packages for nCounter data processing and categorized their functionalities into these steps. They also discussed the advantages and limitations of the nCounter platform, including its suitability for absolute quantification of biomarkers and its sensitivity to variability in gene expression counts. The authors concluded that the nCounter platform is a well-balanced approach between amplification-dependent methods and high-multiplex approaches. They recommended using NanoTube for mRNA data analysis and nSolver for miRNA data processing.