It’s case_when(), not ifelse(). In order to create a groupings for the ‘group’ variable with multiple conditional, but the nested ifelse() approach would become unwieldy. So, I implemented the case_w

Transcript factor annotation for target genes. First, I downloaded the data on human transcription factors and their target genes from https://www.grnpedia.org/trrust/downloadnetwork.php, as shown in

Peak in AS-event region. Algorithm: To quantify the number of splicing events directly influenced by RNA-binding proteins, we consider the peak which locating in following region (yellow) as a regulat

Sankey diagram. Sankey diagrams are well-suited to visualize flows of differential expression genes ( anything is also ) and with different categories or types. . input datadivide DEGs into three gr

rmats2sashimiplot for AS-events. rmats2sashimiplot produces a sashimiplot visualization of rMATS output files. and rmats2sashimiplot can also produce plots using an annotation file and genomic coord

SUPPA2 for alternative splicing. SUPPA2 is a fast, accurate, and uncertainty-aware for differential splicing analysis across multiple conditions. SUPPA2 uses transcript quantification to compute inclu

标准差与标准误. 标准差（standard deviation，SD）和标准误（standard error，SE）均可用于对数据的进行估计。 所不同的是，标准差反映的是数据的离散程度，即标准差其实表征的是某一次抽样得到的一个 “样本量为N” 的样本里的所有个体之间的差异程度。而标准误则是单个统计量在多次抽样中呈现的变异性，即在很多次抽样得到的很多个 ““样本量为N” 的样本之间变异程度。 概括性