assessNormalization | R Documentation |
Data-driven miRNA sequencing Normalization Assessment
assessNormalization(raw, normalized, negControls, posControls, clusters)
raw |
Raw read count matrix (rows = genes, cols = samples).
The rows and columns of the count matrix must be named,
where |
normalized |
Named list of normalized count matrices.
Each matrix holds the normalized read count matrix corresponding to a
normalization method under study.
Each list member must be named (e.g. after the used normalization).
Each matrix in |
negControls |
Vector of negative control markers as generated by
the function |
posControls |
Vector of positive control markers as generated by
the function |
clusters |
Named Vector of clusters. Associates each miRNA
in |
DANA Assessment metrics for the provided normalized counts (for each normalized count matrix). DANA computes two assessment metrics:
cc
measures the preservation of biological signals
before versus after normalization.
A high value indicates a high preservation of biological signals
(cc
<= 1).
In particular, cc
is the concordance correlation coefficient of the
within-cluster partial correlation among positive controls before and
after normalization.
mscr
measures the relative reduction of handling before
versus after normalization. A high mscr
indicates higher
removal of handling effects.
In particular, mscr
is the mean-squared correlation reduction in
negative controls before and after normalization.
When selecting a normalization method for the raw
data, one should
aim for the best possible trade-off of hight cc and high mscr.
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