A pipeline that can process single or multiple Single Cell RNAseq 
    samples primarily specializes in Clustering and Dimensionality Reduction. 
    Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, 
    Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte,
    Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling 
    them by biological names. Once users named each cluster, they can evaluate the 
    quality of them again and find the de novo marker genes also.

'Last.fm'<https://www.last.fm> is a music platform focussed on building a 
    detailed profile of a users listening habits. It does this by 'scrobbling' (recording) 
    every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc)
    and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a 
    complete record of your entire listening history. 'scrobbler' provides helper functions
    to download and analyse your listening history in R.

Allows the user to animate 'shiny' elements when scrolling to view them.
    The animations are activated using the 'scrollrevealjs' library. See <https://scrollrevealjs.org/> for more information.

Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) <doi:10.1101/2022.04.05.487176>.

Test published summary statistics for consistency
    (Brown and Heathers, 2017, <doi:10.1177/1948550616673876>;
    Allard, 2018, <https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>;
    Heathers and Brown, 2019, <https://osf.io/5vb3u/>).
    The package also provides infrastructure for implementing new
    error detection techniques.