0000000000749726
AUTHOR
Kolja Becker
Machine learning-assisted identification of factors contributing to the technical variability between bulk and single-cell RNA-seq experiments
AbstractBackgroundRecent studies in the area of transcriptomics performed on single-cell and population levels reveal noticeable variability in gene expression measurements provided by different RNA sequencing technologies. Due to increased noise and complexity of single-cell RNA-Seq (scRNA-Seq) data over the bulk experiment, there is a substantial number of variably-expressed genes and so-called dropouts, challenging the subsequent computational analysis and potentially leading to false positive discoveries. In order to investigate factors affecting technical variability between RNA sequencing experiments of different technologies, we performed a systematic assessment of single-cell and bu…
Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster
16 páginas, 6 figuras, 1 tabla