Data used in Taback, N. (2022). Design and Analysis of Experiments and Observational Studies using R. Chapman & Hall/CRC.

Offers 'markdown' output formats designed with various styles, allowing users to generate HTML reports tailored for scientific or machine learning showcase. The output has a contemporary appearance with vibrant visuals, providing numerous styles for effective highlighting. Created using the 'tufte' <https://rstudio.github.io/tufte/> package code as a starting point.

It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.

Users may specify what fundamental qualities of a new study have 
             or have not changed in an attempt to reproduce or replicate an 
             original study. A comparison of the differences is visualized. 
             Visualization approach follows 'Patil', 'Peng', and 
             'Leek' (2016) <doi:10.1101/066803>.

Omics data (e.g. transcriptomics, proteomics,
    metagenomics...)  offer a detailed and multi-dimensional perspective
    on the molecular components and interactions within complex biological
    (eco)systems.  Analyzing these data requires adapted procedures, which
    are implemented as steps according to the 'recipes' package.

An automatic cell type detection and assignment algorithm for single cell RNA-Seq and Cytof/FACS data. 'SCINA' is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of markers, such as genes and protein symbols that are highly or lowly expressed in each category. See Zhang Z, et al (2019) <doi:10.3390/genes10070531> for more details.

We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) <doi:10.1186/s13059-022-02649-3>.