1.16.0版本的更改修正了当使用correct时mnnCorrect()输出的行名。set.row= all=TRUE。1.8.0版更改:迁移findMutualNN()到BiocNeighbors。o multiBatchPCA()中支持d=NA，以便在调用函数时更方便地禁用PCA。修正了d=NA指定子集的错误。row= in fastMNN()。o增加了applyMultiSCE()函数，以便在多个singlecel实验输入的主/备用实验之间轻松应用函数。o增加了mnnDeltaVariance()函数，从MNN对之间的差异的方差计算诊断。o增加了quickCorrect()函数，快速执行交叉，归一化，特征选择和校正。o从OSCA书中添加了一些基于集群的诊断(clusterabundance evar ()， clusterabundance etest()和compareMergedClusters())。o文件支持的矩阵现在在multiBatchPCA()之前实现到内存中。在提供design=时，允许regressbatch()在没有batch=的情况下操作。 Added d= and related options to conveniently perform a PCA on the ResidualMatrix. o Added correct.all= option to all correction functions for consistency. o Added a deferred=TRUE default to multiBatchPCA and its callers, to encourage use of deferred matrix multiplication for speed. o Switched default PCA algorithm in multiBatchPCA to IrlbaParam. o Added add.single= mode for endomorphic addition of correction results in correctExperiments(). Changes in version 1.4.0 o Support the use of arbitrary design matrices in regressBatches(). o Allow lists of objects to be directly passed into the ... for many functions. o Added the clusterMNN() function for performing MNN on cluster centroids. o Added get.variance= option to fastMNN() to return variance explained from PCA. Support d=NA to skip the PCA step altogether. o Modified correctExperiments() to preserve non-conflicting rowData() fields. Changes in version 1.2.0 o Deprecated rotate.all= in favour of get.all.genes= in multiBatchPCA(). o Switched BSPARAM= to use IrlbaParam(deferred=TRUE) by default in fastMNN(), so that the default behaviour is actually fast. o Deprecated auto.order= in favor of merge.order= and auto.merge= in fastMNN() and mnnCorrect(). Automatic merging now detects potential tree-based merges. Merge trees can also be specified as input. o Added the correctExperiments() function to cbind the original assays alongside the merged values. o Added the subset.row= argument to cosineNorm() for in-place subsetting. o Added batch= and preserve.single= arguments to multiBatchNorm(). Standardized behavior of subset.row= by adding a normalize.all= argument. o Added the regressBatches() function for correction via standard linear regression. o Added the prop.k= argument in all MNN-related functions, to allow the value of k to adapt asymmetrically to the size of each batch. Changes in version 1.0.0 o New package batchelor, for batch correction of single-cell (RNA sequencing) data.