Biomarker Discovery: a machine learning workflow applied to Tuberculosis diagnosis.

In our previous work, while working in tuberculosis diagnostics research, we developed some workflows to detect possible biomarkers using Omics data from large cohort studies. Discovered in 19th century, Tuberculosis (TB) is still a serious public health problem and it is estimated that one third of the World’s population is infected with Mycobacterium Tuberculosis (mTB). A... Continue Reading →

Advertisements

Large Effect Sizes: Missing information produce misleading results.

Recently I came across the problem with suspiciously large difference in the averages of two groups while analysing some Omics data. An article dealing with similar issues can be seen here. The data distribution is shown below in Figure 1 (FYI: the fold change was around 6 - which is very large for this kind... Continue Reading →

High Dimensional Data & Hierarchical Regression

In a high-throughput experiment one performs measurements on thousands of variables (e.g. genes or proteins) across two or more experimental conditions. In bioinformatics, we come across such data generated using technologies like Microarrays, Next generation sequencing, Mass spec etc. Data from these technologies have their own pre-processing, normalising and quality checks (see here and here... Continue Reading →

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 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 →

Compare Transformations & Batch Effects in Omics Data

While analysing high dimensional data, e.g. from Omics (Genomics, Transcriptomics, Proteomics etc.) - we are essentially measuring multiple response variables (i.e. genes, proteins, metabolites etc.) in multiple samples, resulting in a $latex rXn$ matrix X with r variables and n samples. The data capture can lead to multiple batches or groups in the data -... Continue Reading →

Powered by WordPress.com.

Up ↑