exp | |

tr | do matrix transpose |

dims | get summary information about the HTS matrix dimensions |

as.expr_list | convert the matrix into row gene list |

setTag | set a new tag string to the matrix |

setZero | set the expression value to zero if the expression value is less than a given threshold |

setSamples | set new sample id list to the matrix columns |

setFeatures | set new gene id list to the matrix rows |

filterZeroSamples | filter out all samples columns which its expression vector is ZERO! |

filterZeroGenes | removes the rows which all gene expression result is ZERO |

filterNaNMissing | set the NaN missing value to default value |

load.expr | load an expressin matrix data |

load.expr0 | read the binary matrix data file |

load.matrixView | Load the HTS matrix into a lazy matrix viewer |

matrix_info | get matrix summary information |

write.expr_matrix | write the gene expression data matrix file |

filter | Filter the geneID rows |

as.generic | cast the HTS matrix object to the general dataset |

average | calculate average value of the gene expression for each sample group. this method can be apply for reduce data size when create some plot for visualize the gene expression patterns across the sample groups. |

z_score | Z-score normalized of the expression data matrix To avoid the influence of expression level to the clustering analysis, z-score transformation can be applied to covert the expression values to z-scores by performing the following formula:
x is value to be converted (e.g., a expression value
of a genomic feature in one condition), µ is the
population mean (e.g., average expression value Of
a genomic feature In different conditions), σ Is the
standard deviation (e.g., standard deviation of
expression of a genomic feature in different conditions). |

pca | do PCA on a gene expressin matrix |

totalSumNorm | normalize data by sample column |

relative | normalize data by feature rows |

expression.cmeans_pattern | This function performs clustering analysis of time course data. Calculate gene expression pattern by cmeans algorithm. |

expression.cmeans3D | run cmeans clustering in 3 patterns |

savePattern | save the cmeans expression pattern result to local file |

readPattern | read the cmeans expression pattern result from file |

cmeans_matrix | get cluster membership matrix |

pattern_representatives | get the top n representatives genes in each expression pattern |

split.cmeans_clusters | ## split the cmeans cluster outputsplit the cmeans cluster output into multiple parts based on the cluster tags |

peakCMeans | ## clustering analysis of time course dataThis function performs clustering analysis of time course data |

expr_ranking | |

deg.t.test | do t-test across specific analysis comparision |

log | log scale of the HTS raw matrix |

geneId | get gene Id list |

as.deg | create gene expression DEG model |

deg.class | |

joinSample | do matrix join by samples |

aggregate | merge row or column where the tag is identical |

add_gauss | add random gauss noise to the matrix |