inst/helpfiles/tabPanel_MA_missingValue_TRUE.md

In tnaake/MatrixQCvis: Shiny-based interactive data-quality exploration for omics data

MA plot and Hoeffding's D statistic

In the first part of the panel the A vs. M plots per sample are depicted.

The values are defined as follows - $A = 1/2 \cdot (I_i + I_j)$ and - $M = I_i- I_j$, where $I_i$ and $I_j$ are transformed values. In the case of raw or normalized the values are log2-transformed prior to calculating A and M. In case of transformed, batch corrected, or imputed the values are taken as they are (N.B. when the transformation method is set to none the values are not log2-transformed).

The values for $I_i$ are taken from the sample $i$. For $I_j$, the feature-wise means are calculated from the values of the group $j$ of samples specified by the drop-down menu group. The sample for calculating $I_i$ is excluded from the group $j$. The group can be set to "all" (i.e. all samples except sample $i$ are used to calculate $I_j$) or any other column in colData(se). For any group except "all" the group is taken to which the sample $i$ belongs to and the sample $i$ is excluded from the feature-wise calculation.

The MA values for all samples are by default displayed facet-wise. The MA plot can be set to specific samples by changing the selected value in the input menu plot.

The underlying data set can be selected by the drop-down menu (Data set for the MA plot).

In the second part of the tab, the Hoeffding's D statistic values are visualized for the different data sets raw, normalized, transformed, batch corrected, and imputed.

D is a measure of the distance between F(A, M) and G(A)H(M), where F(A, M) is the joint cumulative distribution function (CDF) of A and M, and G and H are marginal CDFs. The higher the value of D, the more dependent are A and M.

tnaake/MatrixQCvis documentation built on June 20, 2024, 7:22 a.m.