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This may be the reason why in practice, TIC normalization works well for most datasets.
Intra-sample (e.g., intra-array) normalization works within each distinct sample data, while inter-sample (e.g., inter-array) normalization is simultaneously applied to the desired sample set.
Lowess normalization works in much the same way but use a locally fitted regression curve along the full range of A to identify M values to center data at.
Thus, we conclude that the spike-in-based normalization works when given enough sequence spike-in reads; there were 5000 spike-in reads on average in the trial results.
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73 Lowess normalization and quantile normalization worked well in microRNA-seq data normalization.
A large portion of concept normalization work relies, at least partially, on dictionary lookup techniques and various string matching algorithms to account for term variation.
The LIMS used those data to generate an RNA concentration normalization work-list, which was then executed by an automated liquid handler during the reverse transcription reaction assembly procedure.
Therefore, the approach of using orientation normalization still works in our method.
Normalization technique works good on Type 2 artifacts but the success of this technique depends greatly on the estimation of corrected mean curve.
Their analyses show that the quantile normalization method works better than other normalization techniques in removing differences across arrays in miRNA expression data.
We have found that the normalization method works equally well in other regions as well as with other samples (data not shown).
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