bctsne {bcTSNE} R Documentation

## Calculate BC t-SNE by orthogonal gradient descent

### Description

Calculate BC t-SNE by orthogonal gradient descent

### Usage

```bctsne(X, Z, k = 50, outDim = 2, perplexity = 30, maxIter = 1000)
```

### Arguments

 `X` numeric matrix, input matrix `Z` numeric matrix, covariate matrix `k` integer of length 1, reduced dimension (number of eigenvectors) `outDim` integer of length 1, the output dimension `perplexity` numeric of length 1, the t-SNE perplexity `maxIter` integer of length 1, the maximum iterations for the BC t-SNE algorithm

### Details

`X` should be preprocessed (e.g. PCA, centered and scaled). `Z` is the full model matrix, excluding the intercept.

### Value

`list` wth the following items:

`Xred`

numeric matrix, the reduced dimension input to `bctsne`

`Z`

model matrix indicating batch membership

`perplexity`

perpelexity value used in computing t-SNE

`Y`

batch-corrected projection matrix

`maxIter`

maximum iterations used in training

### Examples

```## Create small simulated dataset, A, with embeded batch effects
set.seed(2731)
kRid <- 20
p    <- 100
n    <- 200

W <- matrix(rnorm(p*kRid), kRid)
S <- matrix(rnorm(n*kRid), n)
z <- sample(1:3, rep = TRUE, size = n)
Z <- model.matrix( ~ -1 + as.factor(z))
l <- matrix(rnorm(kRid*NCOL(Z)), kRid)
A <- (S - Z %*% t(l) ) %*% W

## Scale A to give input, X
X <- scale(A)

resUnadj <- Rtsne::Rtsne(X)                 ## Standard t-SNE
resAdj   <- bctsne(X = X, Z = Z, k = 10)    ## Batch-corrected t-SNE

## Plot results, no true effects were included in the simulated data, so
## we expect all batches to overlap with bcTSNE; batch membership indicated
## by color