Gene-set enrichment analysis with topGO (part-1)

  Introduction Data analysis performed on high-throughput experiments usually produces lists of significantly perturbed genes (RNASeq) or other entities that can be mapped to genes, like genetic variants (whole genome sequencing) or Transcription factor binding sites (chIPSeq). The long lists of genes (often in the order of hundreds or thousands) produced as the outcome of... Continue Reading →


Methods of handling and working with missing/censored data (part-2)

Description As discussed in my last blog here,┬ámissing data in big data analysis cannot always be ignored and requires a good understanding of the data and user decisions on how to handle this scenario. In biology, this generally occurs when the data is subjected to limits of detection or quantification (censoring or truncation mechanism). These... Continue Reading →

Methods of handling and working with missing data (part 1)

Description In biology, the presence of missing values is a common occurrence for example in proteomics and metabolomics study. This represents a real challenge if one intends to perform an objective statistical analysis avoiding misleading conclusions. The leading causes of incompletely observed data are truncation and censoring which are often wrongly used interchangeably. You can... Continue Reading →

Normalising Nanostring data

This is a quick R guide to learn about Nanostring technology (nCounter) and how to pre-process the data profiled on this platform. Description The nCounter system from Nanostring Technologies is a direct, reliable and highly sensitive multiplexed measurement of nucleic acids (DNA and RNA) based on a novel digital barcode technology. It involves Custom Codeset... Continue Reading →

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