Variance stabilization with DDHFm
نویسندگان
چکیده
The DDHFm package is designed to perform data-driven Haar-Fisz (DDHF) variance stabilization. The basic DDHF method itself is described in [4, 5]. The modifications to DDHF to make it work successfully for microarray (or indeed similar kinds of replicate data) are described in [10]. The basic idea of the Haar-Fisz transform is very simple. First, a Haar wavelet transform is applied to the data. Then a Fisz transformation is applied to the wavelet coefficients. Then a standard inverse Haar transformation is applied to the Fisz-transformed wavelet coefficients to obtain a variance stabilized sequence. Informally, this kind of transformation works because the Fisz variance stabilizes (and moves towards Gaussian) each individual wavelet coefficient and then the multiscale nature of the inverse transform maintains the variance stabilization (since it is an orthogonal transform) by effectively enforcing the stabilization at every scale and location.
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Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach
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