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

Gene ID Conversion. When comparing multiple gene sets from different sources, we often encounter missing data because the same gene may have multiple aliases. To facilitate downstream analysis, we fir

LASSO 回归模型. 一. LASSO 回归模型的原理LASSO 回归（least absolute shrinkage and seletion operator），即最小绝对值收敛和选择算子算法，与最小二乘法在曲线拟合中对参数估计的作用类似，LASSO算法也常被用于广义线性模型中的变量筛选。具体地讲，就是LASSO通过生成一个惩罚函数，对回归模型中的变量系数进行压缩（把不重要的系数压缩到零）

2025年夏天 东湖骑行 [1]. . . 夏天凌波门外的湖面。 此时，周邦彦的 “梅雨霁，暑风和，高柳乱禅多”，大概是应景的。 . . 不远处有一簇美人蕉，再远处是灯火灿烂的居民楼，夜晚的白马道很安静，这是我骑行最多的一段绿道。 . . 一团小月在树梢。 骑行到此处，已经是深夜了，道上人少。 看到月亮正在一株柳树上。这个世界其实是很美好的，日子不该过成现在这样。 . . 种在宿舍窗台上的西