Tag Archives: statistical analysis

Linnorm is a novel normalization and transformation method for the analysis of single cell RNA sequencing (scRNA-seq) data. Linnorm is developed to remove technical noises and simultaneously preserve biological variations in scRNA-seq data, such that existing statistical methods can be ...

Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Novel biological technologies, such ...

Data from RNA-seq experiments provide us with many new possibilities to gain insights into biological and disease mechanisms of cellular functioning. However, the reproducibility and robustness of RNA-seq data analysis results is often unclear. This is in part attributed to ...

Various methods had been introduced for normalization and comparison of RNA-seq count data. However, they lacked objectivity because they based on ad hoc assumptions that were never verified their appropriateness. Here, researchers from Akita Prefectural University introduced a method that assumes ...

The Istituto per le Applicazioni del Calcolo (IAC-CNR) seeks applicants for a fully funded 3 year PhD position for the project Statistical analysis of high-dimensional omic data. The position is supported by the HORIZON 2020/Marie Sklodowska Curie Action INCIPIT. The ...

Here the authors walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. They start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of ...

RNA-seq technology has become an important tool for quantifying the gene and transcript expression in transcriptome study. The two major difficulties for the gene and transcript expression quantification are the read mapping ambiguity and the overdispersion of the read distribution ...

Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general framework composed ...

The discrete data structure and large sequencing depth of RNA sequencing (RNA-seq) experiments can often generate outlier read counts in one or more RNA samples within a homogeneous group. Thus, how to identify and manage outlier observations in RNA-seq data ...

Meta-analysis of gene expression data sets is increasingly performed to help identify robust molecular signatures and to gain insights into underlying biological processes. The complicated nature of such analyses requires both advanced statistics and innovative visualization strategies to support efficient ...