Description Usage Arguments Value Note See Also Examples

`sDistance`

is supposed to compute and return the distance matrix
between the rows of a data matrix using a specified distance metric

1 2 3 |

`data` |
a data frame or matrix of input data |

`metric` |
distance metric used to calculate a symmetric distance matrix. See 'Note' below for options available |

`dist`

: a symmetric distance matrix of nRow x nRow, where nRow is the number of rows of input data matrix

The distance metrics are supported:

"pearson": Pearson correlation. Note that two curves that have identical shape, but different magnitude will still have a correlation of 1

"spearman": Spearman rank correlation. As a nonparametric version of the pearson correlation, it calculates the correlation between the ranks of the data values in the two vectors (more robust against outliers)

"kendall": Kendall tau rank correlation. Compared to spearman rank correlation, it goes a step further by using only the relative ordering to calculate the correlation. For all pairs of data points

*(x_i, y_i)*and*(x_j, y_j)*, it calls a pair of points either as concordant (*Nc*in total) if*(x_i - x_j)*(y_i - y_j)>0*, or as discordant (*Nd*in total) if*(x_i - x_j)*(y_i - y_j)<0*. Finally, it calculates gamma coefficient*(Nc-Nd)/(Nc+Nd)*as a measure of association which is highly resistant to tied data"euclidean": Euclidean distance. Unlike the correlation-based distance measures, it takes the magnitude into account (input data should be suitably normalized

"manhattan": Cityblock distance. The distance between two vectors is the sum of absolute value of their differences along any coordinate dimension

"cos": Cosine similarity. As an uncentered version of pearson correlation, it is a measure of similarity between two vectors of an inner product space, i.e., measuring the cosine of the angle between them (using a dot product and magnitude)

"mi": Mutual information (MI).

*MI*provides a general measure of dependencies between variables, in particular, positive, negative and nonlinear correlations. The caclulation of*MI*is implemented via applying adaptive partitioning method for deriving equal-probability bins (i.e., each bin contains approximately the same number of data points). The number of bins is heuristically determined (the lower bound):*1+log2(n)*, where n is the length of the vector. Because*MI*increases with entropy, we normalize it to allow comparison of different pairwise clone similarities:*2*MI/[H(x)+H(y)]*, where*H(x)*and*H(y)*stand for the entropy for the vector*x*and*y*, respectively

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# 1) generate an iid normal random matrix of 100x10
data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10)
# 2) calculate distance matrix using different metric
sMap <- sPipeline(data=data)
# 2a) using "pearson" metric
dist <- sDistance(data=data, metric="pearson")
# 2b) using "cos" metric
# dist <- sDistance(data=data, metric="cos")
# 2c) using "spearman" metric
# dist <- sDistance(data=data, metric="spearman")
# 2d) using "kendall" metric
# dist <- sDistance(data=data, metric="kendall")
# 2e) using "euclidean" metric
# dist <- sDistance(data=data, metric="euclidean")
# 2f) using "manhattan" metric
# dist <- sDistance(data=data, metric="manhattan")
# 2g) using "mi" metric
# dist <- sDistance(data=data, metric="mi")
``` |

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