Description Usage Arguments Value Author(s)

The algorithm alternates between 1) computing latent loadings u and latent variable v and 2) estimating noise standard deviation for each of the N genes.

1 | ```
AlternateSVD(x, r, pred = NULL, max.iter = 10, TOL = 1e-04)
``` |

`x` |
an N by n data matrix |

`r` |
a numeric, number of latent factors to estimate |

`pred` |
an n by s matrix, each column is a vector of known covariates for n samples, s covariates are considered, default to |

`max.iter` |
a numeric, maximum number of iteration allowed, default to 10 |

`TOL` |
a numeric, tolerance level for the algorithm to converge, default to 1e-04 |

`sigma` |
a vector of length N, noise standard deviations for N genes |

`coef` |
an N by s matrix, estimated coefficients for known covariates |

`uest` |
an N by r matrix, estimated latent loadings |

`vest` |
an n by r matrix, estiamted latent factors |

Yunting Sun yunting.sun@gmail.com, Nancy R.Zhang nzhang@stanford.edu, Art B.Owen owen@stanford.edu

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