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DMH Data Normalization Methods

Epigenetics. Changes to a gene's expression without a changing the genes themselvesDNA methylationHistone modificationwhen chromatin is methylated, it is in a closed configuration, inhibiting regulatory proteins necessary for transcriptionwhen histones are acylated, the chromatin remains openBo

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DMH Data Normalization Methods

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    1. DMH Data Normalization Methods Daniel Tse Lab of Dr. Tim Huang; Mentor: Dr. Dustin Potter 8.23.06

    2. Epigenetics Changes to a gene’s expression without a changing the genes themselves DNA methylation Histone modification when chromatin is methylated, it is in a closed configuration, inhibiting regulatory proteins necessary for transcription when histones are acylated, the chromatin remains open Both work together to control gene expression

    3. Epigenetic Research Looking at CpG islands dense regions of cytosine and guanine near transcription sites where there is the highest amount of methylation Identification of hyper/hypo methylated genes in tumorigenesis, many genes and aberrantly methylated or demethylated such as genes responsible for tumor suppression, apoptosis, or senescence Demethylating agents, methyltransferases, deacylation inhibitors

    4. DMH

    5. Biases in Two-Color Arrays Slide inconsistencies within the slide slide color biasing intensity readings Scanning aberrations or inconsistencies between slides differences in machines Dye at low intensities, green dye is favored (hypo methylation) at high intensities, red dye is favored (hyper methylation) Impact All can produce non-biological outliers, irregular distributions due to non-biological factors

    7. Normalization Increased bias and confounding variables for high-throughput analysis little work has been done to adapt normalization techniques to DMH Normalization techniques are needed to accurately interpret the results remove aberrations and outliers due to non-biological factors reveal the patterns and outliers of actual biological factors Work done in R open source statistical language based on S+ Two main categories for study intra-slide normalization (within slide) inter-slide normalization (between slide)

    8. Logarithmic Transformation common technique used for two-color arrays log transformations often convert data to a more normal distribution normal distribution often needed for some statistical methods M= log2(Cy5/Cy3) A=log2(Cy5*Cy3)*0.5

    9. Loess

    10. Loess-based Normalizations intensity-dependent non-linear normalization a loess curve is fit to the M vs A data predicted loess value is subtracted from the data to decrease the standard deviation and place the mean log ratio at 0 rank invariant non-linear normalization loess curve is fit to rank invariant signals with respect to Cy5 and Cy3 better for reducing outliers when formulating the loess curve

    11. Loess-based Normalizations Cyclic loess take two arrays a and b calculate M=log2(probe intensities(a)/probe intensities(b)) also: A=0.5*log2(probe intensities(a)*probe intensities(b)) a loess curve is fit to these new M and A values the original M and A values are then adjusted pair-wise combinations are formed to perform this normalization averages are taken of the resulting M and A values between the pair-wise combinations for the final adjustments

    12. Regional Loess

    13. Global Mean

    14. Regression Normalization

    15. Other Between-slide Normalizations Interquantile normalization forces all samples to have very similar distributions the green samples are normalized by a ranked-mean method the red samples are then normalized using the linear regression model in a similar fashion to regression normalization Scaling methods a baseline array is chosen the other arrays in the set of experiments are normalized to the baseline array

    16. Complexity Issues - CPU economy

    17. Global Mean

    18. Loess

    19. Cyclic Loess

    20. Cyclic Loess

    21. Standard Deviation Changes

    22. Outliers

    23. Outliers

    24. Discussion Loess normalization effective in reducing the dye biases observed in raw data reduces the standard deviation effectively reduces old outliers and possibly revealing biological outliers does not induce excessive negative frequencies Rank invariant loess normalization is quickest computational with similar results as other loess normalization schemes

    25. Future Study Use in other data sets Continued analysis with sequential normalization Spike-in data Looking for additional normalizations schemes

    26. Acknowledgements Mentor: Dr. Dustin Potter Supervisors: Dr. Tim Huang, Dr. Pearlly Yan MBI Summer Undergraduate Program

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