Computational Molecular Biology 2016, Vol.6, No.2, 1-9
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pre-process raw data that helps to identify differential gene expression. Using different mean and median
calculations of BioConductor algorithms to predict pre-process the data to evaluate quality values that helps to
normalize the data. RMA and MAS5 algorithms help to predict the pre-processing and quality prediction. These
tests help to identify differentially expressed genes present in transcriptional response that significantly correlated
with AD markers.
Table 1 Alzheimer's disease samples used for analysis of differential gene expression analysis
Control, n = 9
Incipient, n = 7
Moderate, n = 8
Severe, n = 7
Age
85.3 ±2.7
90 ±2.1
83.4 ±1.1
84 ±4.0
NFT
2.7 ±1.0
9.4 ±1.8
25.6 ±3.5
32.7 ±7.2
Braak
2.1 ±0.4
5 ±0.4
5.6 ±0.2
5.9 ±0.1
MMSE
27.7 ±0.5
24.3 ±1.1
16.5 ±0.6
6 ±1.4
PMI
2.6 ±0.2
3.3 ±0.6
3.2 ±0.2
3 ±0.1
Note: N- number of subjects in each group; Age-age at death; NFT-neurofibrillary tangle count; Braak-Braak stage; MMSE-adjusted
Minimental Status Exam; PMI- postmortem interval. Values are mean ±SEM.
3.1 Differential gene expression analysis
Using Biostatistical package such as LIMMA is used to determine the differential gene expressions based on
significance of p-values. We have identified 9921 genes is significantly associated with AD. While multiple
comparisons of control, moderate, incipient and severe datasets is classified based on MMSE and NFT test scores.
The significance analysis of microarray shows 5727 genes is significantly associated with GO terms. The
fundamental gene expression data of identified genes whose patterns of expression differ according to phenotype
and genotype of experimental conditions. The transcriptional control of class discriminate genes that differentially
expressed in 4 groups such as control, Incipient, moderate and severe levels. There are different statistical
calculations such as t-test of probable values of gene-by-gene expression in each array is analyzed with FDR
values. We calculate the FDR of the false positives of expected to multiple comparisons divided by the false
positive results found with worst case probability of genes identified (P<0.05) by correlation is significant because
of the error from multiple testing (Table 2). The two ways ANOVA of significant calculation shows 1361 genes is
either up-regulated or down regulated by more than 2 fold change between the 4 groups. ANOVA calculation of
one or two groups is tested with different groups is tested based on linear models that are frequently used for
assessing differential gene expression. Due to lack of information on coregulation of genes that computed with
genes separately. The overall results shows 12 genes CA11, PTN, TBC1D2B, FAR2, LHCGR, EHD1, KCNA5,
GPR22, WDFY3 and ITGBL1 is transcriptional regulation of cerebral cortex in AD and is potential targets for
biomarkers to identify AD for diagnostics (Table 3a; Table 3b; Table 3c; Table 3d). The LHCGR is the clinical
phenotype of AD is the best potential biomarkers against AD diagnostics. We sorted the more significantly
expressed in all four conditions that is predicted in bar graphs The overall results of significant genes shows PTN,
TBC1D2B, FAR2, LHCGR, EHD1, KCNA5 and GPR22, is more potentially expressed in control-moderate,
control-severe, incipient-severe and moderate and severe.
Table 2 Differentially expressed genes that significantly expressed in AD
Incipient-
Moderate
Incipient-
Severe
Incipient-
Normal
Moderate-
Severe
Moderate-
Normal
Severe-
Normal
Up
Down
Up
Down
Up
Down
Up
Down
Up
Down
Up
Down
0
5527
8
216
0
5527
65
816
13
652
213
512
Table 3a Control-moderate of unregulated differential expressed 13 genes in AD
ID
logFC
AveExpr
t
P.Value
adj.P.Val
B
220615_s_at
-0.6596570
6.9067969
-5.533305
5.77E-06
0.0886242
2.892579
221288_at
-0.9151203
6.4597562
-5.291681
1.13E-05
0.08862422
2.412113
220355_s_at
0.4636934
7.2931713
5.2713867
1.19E-05
0.08862422
2.371481
