This function checks and preprocesses a merged seurat object.
Usage
CheckDataMerge(
srt_merge,
batch = NULL,
assay = NULL,
do_normalization = NULL,
normalization_method = "LogNormalize",
do_HVF_finding = TRUE,
HVF_source = "separate",
HVF_method = "vst",
nHVF = 2000,
HVF_min_intersection = 1,
HVF = NULL,
vars_to_regress = NULL,
verbose = TRUE,
seed = 11
)Arguments
- srt_merge
A merged `Seurat` object that includes the batch information.
- batch
A character string specifying the batch variable name.
- assay
Which assay to use. If
NULL, the default assay of the Seurat object will be used.- do_normalization
Whether data normalization should be performed. Default is
TRUE.- normalization_method
The normalization method to be used. Possible values are
"LogNormalize","SCT", and"TFIDF". Default is"LogNormalize".- do_HVF_finding
Whether to perform high variable feature finding. If
TRUE, the function will force to find the highly variable features (HVF) using the specified HVF method.- HVF_source
The source of highly variable features. Possible values are
"global"and"separate". Default is"separate".- HVF_method
The method to use for finding highly variable features. Options are
"vst","mvp", or"disp". Default is"vst".- nHVF
The number of highly variable features to select. If NULL, all highly variable features will be used. Default is
2000.- HVF_min_intersection
The feature needs to be present in batches for a minimum number of times in order to be considered as highly variable. Default is
1.- HVF
A vector of feature names to use as highly variable features. If NULL, the function will use the highly variable features identified by the HVF method.
- vars_to_regress
A vector of variable names to include as additional regression variables. Default is
NULL.- verbose
Whether to print the message. Default is
TRUE.- seed
Random seed for reproducibility. Default is
11.