Batch effect correction on two single-cell batches
CorrectBatch(
refBatch,
queBatch,
cnRef = NULL,
cnQue = NULL,
queNumCelltypes = NULL,
maxMem = 5,
pairs = NULL,
kNN = 30,
sampling = FALSE,
numSamples = NULL,
idxQuery = NULL,
idxRef = NULL,
pcaDim = 50,
perCellMNN = 0.08,
fuzzy = TRUE,
fuzzyPCA = 10,
estMethod = "Median",
clusterMethod = "louvain",
pairsFilter = FALSE,
doCosNorm = FALSE,
verbose = FALSE
)Arguments
- refBatch
Reference batch.
- queBatch
Query batch (batch to correct).
- cnRef
Cosine normalization of the reference batch.
- cnQue
Cosine normalization of the query batch.
- queNumCelltypes
Number of cell types in the query batch. By default Canek searches the number of cell types using an heuristic algorithm. Change this parameter if you know the number of cell types in advanced.
- maxMem
Maximum number of memberships from the query batch. This parameter is used on the heuristic algorithm to find the number of cell types.
- pairs
A numerical matrix containing MNNs pairs cell indexes. First column corresponds to query batch cell indexes.
- kNN
Number of k-nearest-neighbors used to define the MNNs pairs.
- sampling
Use MNNs pairs sampling when using a Kalman filter to estimate the correction vector.
- numSamples
If sampling. Number of MNNs pairs samples to use on the estimation process.
- idxQuery
Numerical vector indicating the index of the cells from the query batch to use on the correction vector estimation.
- idxRef
Numerical vector indicating the index of the cells from the reference batch to use on the correction vector estimation.
- pcaDim
Number of PCA dimensions to use.
- perCellMNN
Threshold value to decide if a membership's correction value is calculated. As a rough interpretation, this values can be thought as the proportion of cells from a membership with an associated MNN pair. If the proportion is low, an specific correction vectors is not calculated for this membership.
- fuzzy
Use fuzzy logic to join the local correction vectors.
- fuzzyPCA
Number of PCs to use in the fuzzy process.
- estMethod
Method to use when estimating the correction vectors:
Median. Use the cells median distance.
EKF. Use an extended Kalman filter.
- clusterMethod
Method used to identify memberships.
- pairsFilter
Filter MNNs pairs before estimating the correction vectors. If TRUE, the pairs are filtered from outliers using an interquartile range method.
- doCosNorm
Whether to do cosine normalization.
- verbose
Print output.
Value
A list containing the input batches, the corrected query batch, and the correction data
Details
CorrectBatch is a method to correct batch-effect from two single-cell batches. Batch-effects observations are defined using mutual nearest neighbors (MNNs) pairs and cell groups from the query batch are distinguished using clustering. We estimate a correction vector for each cluster using its MNNs pairs and use these vectors to remove the batch effect from the query batch in two ways:
A linear correction is performed by equally correcting the cells from the same cluster.
A non-linear correction is performed by differently correcting each cell using fuzzy logic.
