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These data ultimately support the inclusion of covariates, as captured by data decomposition methods, in downstream analyses suggesting that such methods are either (a) accounting for unknown covariates beyond technical sequencing artifacts or (b) appropriately weighting the effects of the technical artifacts amongst the ISVs/PCs generated.
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Additionally, we note that there are several caveats to the use of data decomposition methods.
These unknown covariates can be approximated through various data decomposition methods.
We utilized four data decomposition methods – independent surrogate variable analysis (ISVA), surrogate variable analysis (SVA), principal component analysis (PCA) and PEER [ 13, 22] – to account for unknown covariates.
Network component analysis is a data decomposition method for reconstructing regulatory signals and control strengths by using partial and qualitative network connectivity information.
In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed.
In parallel rasterization, it is difficult to design an effective data decomposition method.
EMD, a new adaptive and effective data decomposition method aiming at non-linear and non-stationary data, was presented by NE Huang in 1998 [12].
When first-stage variables are mixed-integer and second-stage variables are continuous, these MILPs can be solved efficiently by classical decomposition methods, such as Dantzig/Wolfe decomposition (DWD), Lagrangian decomposition, and Benders decomposition (BD), or a cross decomposition strategy that combines some of the classical decomposition methods.
The metal can be plated either by electroplating or by thermal decomposition methods.
This section summarizes a data-driven decomposition method, that is, EMD, which has been developed by Huang et al. in 1998 [ 15].
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