Cancer Genetics and Epigenetics 2015, Vol.3, No.11, 1-8
3
influence between the same sample, in order to make
the results more accurate. we use QDMR method
based on information entropy to screen DMS. To
model the effect of experimental variability, we
simulated distribution of entropy from uniformly
methylated regions. We computed the fold change
between replicate-dependent difference from the
average level across replicates and the theoretical
maximum range of methylation. The fold change
follows a normal distribution with mean equal to zero
and some unknown, but 'small', standard deviation
(SD) [25].because it is data preprocessing step, we
chose a more relaxed threshold (SD = 0.15).Secondly,
we removed DMS and intersected the remaining
sites. Thirdly, we use QDMR method to screen DMS
between mutation samples and normal samples
(SD=0.07).The purpose is to find DMS between
disease and normal samples. Then we union all CpG
sites and deal with DNA methylation profile as
follows (X is the original methylation profile which
the number of rows is m, Y is the mean matrix.
n
1
represents the number of
DNMT3A
mutation samples,
n
2
represents the number of
IDH
mutation samples,
n3
represents the number of
IDH_3AD
samples,
n
4
represents the number of normal samples, m is the
number of DMS after taking union):
(1)
(2)
(3)
(4)
(5)
We screen DMS for the mean matrix Y by using
SAMR package. SAM is a statistical tool to find
significant genes of a set of microarray data, DNA
methylation sites which and
1
qvalue
as DMS. DMS on the gene were mapped
to obtain DNA methylation profile.
1.3 Clustering analysis
JHU-USC HumanMethylation450K data downloaded
from TCGA (
.
In order to prove the occurrence of acute myeloid
leukemia is not only to the genome mutations, but also
to the epigenetic changes (DNA methylation),we use
differentially methylated profile to do clustering
analysis by MeV v4.9
.
MeV v4.9 software used to analyze the expression
profile data standardization and filtered. It uses a
variety of complex algorithms to achieve clustering,
visualization, classification, statistical analysis and
other functions. Then, using a hierarchical clustering
method to connect the average distance matrix and the
Pearson correlation coefficient matrix to obtain the
clustering heat map. Finally, doing T test statistic analysis
for differentially methylated profile by MeV v4.9
software. T test is designed to test the significance
difference between cancer samples and normal samples
( ).
1.4 Relationship between DNA methylation and
gene expression of different genome regions
In order to analyze the relationship between DNA
methylation and gene expression in the different
genomic region, we map differentially methylated
genes on the two areas: (1) on the gene body. (2) 2kb
upstream of the transcription start site. In order to
analyze the relationship between DNA methylation
and gene expression in the two regions, firstly, for the
gene body, we calculate the mean value of DNA
methylation and mean value of gene expression of a
plurality of samples and make two variables
correlation analysis by using SPSS 19.0 version
.
Then, we separate the DMS in the gene body from
disease group and normal group and draw correlation
analysis diagram using R. Finally, for the gene
promoter region, we calculate the number of
hypermethylated genes with low expression and the
number of hypermethylated genes with high
expression using SAMR package. We choose genes
with fold change> 2 or fold change <0.5 as
differentially expressed genes. The purpose is to
analyze the relationship between DNA methylation
and gene expression in the promoter region.
1.5 Functional enrichment analysis
GO functional analysis is to analyze the main biological
function using screened differentially methylated genes.
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