# Eqmr-expca

### From Eqtnminer

## Contents |

## Introduction

**eqmr-expca** performs principal component analysis onto a given matrix using either standard full single value decomposition or using an
EM algorithm that can better handle missing data and that can also reduce significantly the computational burden for large matrices when only
the few first axes are needed.

Besides the program can also perform a permutation procedure in order to generate the empirical distribution of the eigenvalues under the null hypothesis that there is no significant covariance structure due to the presence of hidden factors.

Presently, only expression matrices outputted by eqmr-fexp can be used. Future development will allow to start from a custom matrix provided via a standard text file.

## Options

Short | Long | Description |
---|---|---|

-e | --expmat | The expression matrix file as generated by eqmr-fexp (binary format) |

-o | --output | The output file |

-k | --kmax | The maximum number of axes for the PCA (default is min(#row, #col)) |

-p | --permut | The number of permutations to do to compute PC p-values |

--scale | Scale the features by their variance | |

--sample | Use samples as features |

## Examples

## See Also

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Main source file | warning.png"{{{mainsourcefile}}}" cannot be used as a page name in this wiki. |

Program category | Data Analysis + |

Program language | C + |

Program name | eqmr-expca + |

Program title | PCA on the expression dataset |