Package 'Canek'

CheckZeroCV

Description

CheckZeroCV

Usage

CheckZeroCV(
  MST = NULL,
  cluMem = NULL,
  corGene = NULL,
  fuzzyPCA = fuzzyPCA,
  memCorrData = NULL,
  zeroCorrection = NULL
)

Arguments

MST	Minimum Spanning Tree
cluMem	Clusters used on MST
corGene	Data to correct
fuzzyPCA	Number of PCs to use in the fuzzy process.
memCorrData	Data to correct
zeroCorrection	Vector indicating which membership has a zero correction vector

---

CorrectBatch

Description

Batch effect correction on two single-cell batches

Usage

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.

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.

Value

A list containing the input batches, the corrected query batch, and the correction data

Examples

x <- SimBatches$batches[[1]]
y <- SimBatches$batches[[2]]
z <- CorrectBatch(x, y)
Corrected <- z$`Corrected Query Batch`

Uncorrected_PCA <- prcomp(t(cbind(x,y)))
plot(Uncorrected_PCA$x[,1:2])
Corrected_PCA <- prcomp(t(cbind(x,z$`Corrected Query Batch`)))
plot(Corrected_PCA$x[,1:2])

---

CorrectBatches

Description

Batch-effect correction over a list of single cell batches

Usage

CorrectBatches(
  lsBatches,
  hierarchical = TRUE,
  queNumCelltypes = NULL,
  maxMem = 5,
  sampling = FALSE,
  numSamples = NULL,
  kNN = 30,
  pcaDim = 50,
  pairsFilter = FALSE,
  perCellMNN = 0.08,
  fuzzy = TRUE,
  fuzzyPCA = 10,
  estMethod = "Median",
  clusterMethod = "louvain",
  doCosNorm = FALSE,
  fracSampling = NULL,
  debug = FALSE,
  verbose = FALSE,
  ...
)

Arguments

lsBatches	List of batches to integrate. Batches should contain the same number of genes as rows.
hierarchical	Use hierarchical integration scheme when correcting more than two batches. If set to FALSE, the input batches are sorted by number of cells and integrated on descending order.
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.
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.
kNN	Number of k-nearest-neighbors used to define the MNNs pairs.
pcaDim	Number of PCA dimensions to use.
pairsFilter	Filter MNNs pairs before estimating the correction vectors. If TRUE, the pairs are filtered from outliers using an interquartile range method.
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.
doCosNorm	Whether to do cosine normalization.
fracSampling	Fraction of cells to sample in the hierarchical selection (default is NULL, no sampling).
debug	Return correction's information
verbose	Print output.
...	Pass down methods from RunCanek().

Details

CorrectBatches is a method to correct batch-effect from two or more 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.

Value

A list containing the integrated datasets as matrix and the correction data .

Examples

Batches <- SimBatches$batches
z <- CorrectBatches(Batches)

Uncorrected_PCA <- prcomp(t(cbind(Batches[[1]], Batches[[2]])))
plot(Uncorrected_PCA$x[,1:2])
Corrected_PCA <- prcomp(t(z))
plot(Corrected_PCA$x[,1:2])

---

Correction vector estimation

Description

Batch effect estimation using an extended Kalman filter

Usage

EkfBE(
  refBatch,
  queBatch,
  pairs,
  sampling = FALSE,
  numSamples = NULL,
  verbose = FALSE
)

Arguments

refBatch	Reference batch.
queBatch	Query batch.
pairs	A numerical matrix containing MNNs pairs cell indexes. First column corresponds to query batch cells.
sampling	Sample MNNs pairs.
numSamples	If sampling, number of MNNs pairs samples to use on the estimation process.
verbose	Print output.

Details

The input batches must have the same number of genes. The model used on the estimation has the form of g_ref = g_que + be, where the batch effect is represented as a value added to the reference gene expression, causing a linear deviation between the reference and the query batches.

Value

A list containing the estimated correction vector and the estimation data. The length of the correction vector is equal to the number of genes.

---

Title Fuzzy

Description

Function to score cell's memberships by fuzzy logic

Usage

Fuzzy(
  cluMem = NULL,
  pcaQue = NULL,
  corCell = NULL,
  fuzzyPCA = 10,
  MST = NULL,
  verbose = FALSE
)

Arguments

cluMem	Memberships' clustering data.
pcaQue	PCA representation of the cells.
corCell	Matrix containing the initial membership assignment. Matrix dimensions are expected as #Cell x #Memberships, with each row sum equal to 1.
fuzzyPCA	Number of PCs to use in the fuzzy process.
MST	Minimum spanning tree
verbose	Print output.

Details

This function perform the fuzzification for the cells' membership. A minimum spanning tree (MST) is created among memberships, and the fuzzification is performed for each of the edges of the MST.#'

---

MeanBE

Description

Batch effect estimation using the MNNs pairs.

Usage

MeanBE(refBatch, queBatch, pairs)

Arguments

refBatch	Reference batch.
queBatch	Query batch.
pairs	A numerical matrix containing MNNs pairs cell indexes. First column corresponds to query batch cells.

Details

The input batches must have the same number of genes. The model used on the estimation has the form of g_ref = g_que + be, where the batch effect is represented as a value added to the reference gene expression. The batch effect is estimated as the median of the gene expression difference among the reference and the query batch, e.g. Median(g_ref - g_que).

Value

A list containing the estimated correction vector and the estimation data. The length of the correction vector is equal to the number of genes.

---

Correction vector estimation

Description

Batch effect estimation using the MNNs pairs.

Usage

MedianBE(refBatch, queBatch, pairs)

Arguments

refBatch	Reference batch.
queBatch	Query batch.
pairs	A numerical matrix containing MNNs pairs cell indexes. First column corresponds to query batch cells.

Details

The input batches must have the same number of genes. The model used on the estimation has the form of g_ref = g_que + be, where the batch effect is represented as a value added to the reference gene expression. The batch effect is estimated as the median of the gene expression difference among the reference and the query batch, e.g. Median(g_ref - g_que).

Value

A list containing the estimated correction vector and the estimation data. The length of the correction vector is equal to the number of genes.

---

Title PairsFiltering

Description

Function to filter MNNs pairs

Usage

PairsFiltering(refBatch, queBatch, pairs, verbose = FALSE)

Arguments

refBatch	Reference batch single-cell data.
queBatch	Query's batch single-cell data.
pairs	A matrix containing MNNs pairs. First column corresponds to query-batch cell indexes.
verbose	Print output.

Details

Filter MNN pairs by quantiles.

Value

A matrix containing the filtered pairs. First column corresponds to query-batch cell indexes.

---

RunCanek

Description

Runs Canek integration.

Usage

RunCanek(x, ...)

## S3 method for class 'Seurat'
RunCanek(
  x,
  batches = NULL,
  slot = "data",
  assay = NULL,
  features = NULL,
  selection.method = "vst",
  nfeatures = 2000,
  fvf.nfeatures = 2000,
  integration.name = "Canek",
  debug = FALSE,
  ...
)

## S3 method for class 'SingleCellExperiment'
RunCanek(
  x,
  batches = NULL,
  assay = "logcounts",
  integration.name = "Canek",
  debug = FALSE,
  ...
)

## S3 method for class 'list'
RunCanek(x, ...)

Arguments

x	object with expression counts or list of matrices.
...	additional arguments passed down to methods.
batches	for S4 objects the column containing batch information.
slot	slot used for Seurat objects (default: data).
assay	assay used for Seurat objects.
features	optional vector of features to use for correction.
selection.method	method used for FindVariableFeatures on Seurat objects when features is NULL.
nfeatures	number of features returned by SelectIntegrationFeatures.
fvf.nfeatures	number of features returned by FindVariableFeatures.
integration.name	name for the integrated assay.
debug	whether to store information about correction vector.

Value

An object of the appropriate type.

---

Dataset with simulated single cell RNA-seq from 2 batches.

Description

Dataset with simulated single cell RNA-seq from 2 batches.

Usage

SimBatches

Format

A list with the following elements:

batches

a list with two matrices representing the two batches

pairs

matrix of pairs between the two batches.

cell_types

a factor with the cell clusters.

...

---