版本1.11.1的更改------------------------ o修复了EBTest()中可能导致错误的错误，当执行isoform DE测试1个样本vs.多个样本时。版本1.9.3的变化------------------------更正在GetDEResults帮助文件中的拼写错误。o包括一个可供选择的归一化方法。另一种方法类似于中位数比例归一化，但可以处理所有基因/亚型至少有一个零计数的情况(在这种情况下，中位数比例归一化将失败)。这种替代方法是为单细胞RNA-seq分析开发的，其中数据集总是包含大量的零。在版本1.9.2的变化------------------------ o修复了一个错误，可能会导致输入矩阵到sizeFactors参数CHANGES IN VERSION 1.9.1 ------------------------ o增加了问答部分在小插曲，以解决常见问题的变化在版本1.7.1 ------------------------ o在EBSeq 1.7.1中，EBSeq合并了一个新的函数GetDEResults()，可用于在一个双条件实验中获得目标FDR下的转录本列表。通过应用该函数及其默认设置获得的结果将对具有低方差和潜在异常值的转录本更加稳健。通过使用此函数中的默认设置，在任何给定分析中识别的基因数量可能与以前的版本(1.7.0或以上)略有不同。为了获得与EBSeq早期版本(1.7.0或更老版本)的结果相当的结果，用户可以在GetDEResults()函数中设置Method="classic"，或使用原始的GetPPMat()函数。GeneDEResults()函数还允许用户修改阈值，以具有预先指定的后褶变化的目标基因/异构体。 o Also, in EBSeq 1.7.1, the default settings in EBTest() and EBMultiTest() function will only remove transcripts with all 0's (instead of removing transcripts with 75th quantile less than 10 in version 1.3.3-1.7.0). To obtain a list of transcripts comparable to the results generated by EBSeq version 1.3.3-1.7.0, a user may change Qtrm = 0.75 and QtrmCut = 10 when applying EBTest() or EBMultiTest() function. CHANGES IN VERSION 1.5.4 ------------------------ o An extra numerical approximation step is implemented in EBMultiTest() function to avoid underflow. The underflow is likely due to large number of samples. A bug in EBMultiTest() is fixed. The bug will cause error when there is exactly 1 gene/isoform that needs numerical approximation. CHANGES IN VERSION 1.5.3 ------------------------- BUG FIXES o Fixed a bug that may generate NA FC estimates when there are no replicates. CHANGES IN VERSION 1.5.2 ------------------------ NEW FEATURES o An extra numerical approximation step is implemented in EBTest() function to avoid underflow. The underflow is likely due to large number of samples. CHANGES IN VERSION 1.3.3 ------------------------ NEW FEATURES o In EBSeq 1.3.3, the default setting of EBTest function will remove low expressed genes (genes whose 75th quantile of normalized counts is less than 10) before identifying DE genes. These two thresholds can be changed in EBTest function. Because low expressed genes are disproportionately noisy, removing these genes prior to downstream analyses can improve model fitting and increase robustness (e.g. by removing outliers).