Abstract
Murine transplantation models are used extensively to research
immunological rejection and tolerance. Here we studied both murine
heart and liver allograft models using microarray technology. We had
difficulty in identifying genes related to acute rejections expressed
in both heart and liver transplantation models using two standard
methodologies: Student's t test and linear models for microarray data
(Limma). Here we describe a new method, standardized fold change (SFC),
for differential analysis of microarray data. We estimated the
performance of SFC, the t test and Limma by generating simulated
microarray data 100 times. SFC performed better than the t test and
showed a higher sensitivity than Limma where there is a larger value
for fold change of expression. SFC gave better reproducibility than
Limma and the t test with real experimental data from the MicroArray
Quality Control platform and expression data from a mouse cardiac
allograft. Eventually, a group of significant overlapping genes was
detected by SFC in the expression data of mouse cardiac and hepatic
allografts and further validated with the quantitative RT‐PCR assay.
The group included genes for important reactions of transplantation
rejection and revealed functional changes of the immune system in both
heart and liver of the mouse model. We suggest that SFC can be utilized
to stably and effectively detect differential gene expression and to
explore microarray data in further studies.
Keywords: gene expression, microarray analysis, murine transplantation
model, standardized fold change
__________________________________________________________________
Abbreviations
FNR
false negative rate
FPR
false positive rate
Limma
linear models for microarray data
MAQC
MicroArray Quality Control
POD
post‐operative day
qRT‐PCR
quantitative RT‐PCR
SFC
standardized fold change
At the stage of organ failure, organ transplantation is a life‐saving
medical procedure, though it still has some problems, e.g. transplant
rejection and the requirement for life‐long immunosuppressive
drugs. Transplantation models without immunosuppression are important
and the mechanisms of rejection and tolerance in these models need to
be revealed.
Microarray technology is well established and widely used, providing a
picture of gene expression or RNA profiling in different tissues [36]1.
To identify differential expression, Student's t test and linear models
for microarray data (Limma) are two popular choices [37]2, [38]3,
[39]4. The t test utilizes information for all the samples (or standard
deviations) in one microarray probe and is conducted independently
among different probes [40]4, while Limma uses the empirical Bayesian
approach of shrinkage of the estimated sample variances towards a
pooled estimate. The information (means and standard deviations) from
all the probes in a replicate set of experiments is combined and used
at the level of one probe to detect differential expression in Limma
[41]2.
In the present study, we established murine heart and liver allograft
models and used microarray technology to reveal the significant genes
that related to transplant rejection. By using the t test and Limma, no
significant intersecting genes were obtained in these models.
Therefore, we developed a new method, named standardized fold change
(SFC), to detect differential expression by taking information from the
neighbors of one probe with an adjustable bin size. To compare SFC with
the t test and Limma, we generated a simulated data set to estimate the
performance and used the real experimental datasets from the MicroArray
Quality Control (MAQC) platform and the transplantation model to
estimate the reproducibility. We concluded that SFC can be applied as a
new and effective approach to detect differential expression and
contribute more reliable results in microarray studies. Then, SFC
reported a set of significant genes from expression data from the
murine heart and liver allograft, and we further validated them by
qRT‐PCR. Gene expression changes revealed functional reactions and
pathway activities in the early stage of allograft in both heart and
liver.
Materials and methods
Animals
Male B10.BR (BR, H‐2k), B10.D2 (D2, H‐2d), C57BL/10 (B10, H‐2b) and CBA
(H‐2k) mice (weighing 25–30 g) were purchased from the Shizuoka
Laboratory Animal Center (Shizuoka, Japan) and housed and cared for in
agreement with the guidelines of the Institutional Animal Care and Use
Committee and the National Research Institute (Japan) for Child Health
and Development guidelines on laboratory animal welfare. The Committee
on the Care and Use of Laboratory Animals at the National Research
Institute accepted the experimental protocol for Child Health and
Development (Permission no.: 2002‐003). All surgical procedures were
conducted under anesthesia with isoflurane/oxygen, and all attempts
were made to minimize suffering.
Transplantation and RNA extraction
Cardiac transplantation was performed from a sex‐matched B10 donor to a
CBA recipient by microsurgical techniques. Intra‐abdominal vascularized
heterotopic mouse cardiac transplantation was performed [42]5. The
cardiac graft survival was determined using daily palpation of the
recipient's abdomen. Three case samples on the fifth day were obtained.
BR mice were used as donors and D2 mice were used as recipients in the
orthotopic hepatic transplantation. We performed transplantation
surgery on the mice [43]6 in which for orthotopic liver
transplantation, BR mice were used as donors and D2 mice were used as
recipients. We subsequently transplanted the liver into the recipient
mice using the cuff technique [44]6. Grafts were harvested at
post‐operative day 5 (POD5) or at POD8 after transplantation and were
submerged in RNAlater® stabilization solution (Life Technologies,
Carlsbad, CA, USA) for freezing. Total RNA was extracted from frozen
tissue samples using ISOGEN (NipponGene, Tokyo, Japan). We also
designed control groups of three normal cardiac tissues and three
hepatic tissues.
Standardized fold change method
The probe signals from microarray data were firstly natural
log‐transformed and then manipulated with quantitative normalization.
To assess the differential expressions among cases and controls, the
statistic SFC is defined as:
[MATH:
SFCi=<
mi>T−CSTDEV(T−C)=
Ti−CiVar(T−C)=Median(t1,t2…ti
)−Median(c1,c2,…ci)Median
(Ti−b<
/mi>/2−Ci−b/2)2,…(Ti−Ci)2,…(Ti+b<
/mi>/2−Ci+b/2)2/0.455<
mo>. :MATH]
(1)
For the variance of each probe, we ranked all probes by the mean values
of signals from all samples and then took the median value of its b
nearest neighbors as the variance, where the default bin size of b here
is 1000. The SFC software now implements this algorithm in the Linux
system and can be found at [45]https://github.com/WeichenZhou/SFC.
Simulation data study
We generated the simulated data from simple formulas with the Gaussian
noise (mean = 0, variance = 1) as a default distribution for gene
expression data [46]7. The control and case samples in the null
hypothesis are shown as follows:
[MATH: H0
control:y0=x0+N(0,1)(kx0)+1 :MATH]
[MATH: H0
case:y0′
=(1+θ0)x0′+N(0,1)(kx0′)+1 :MATH]
(2)
where θ represented the differential expression underlying cases versus
controls and we defined θ[0] as 0% and k is 1. The control and case
samples in the alternative hypothesis are shown as follows:
[MATH: H1
control:y1=x1+N(0,1)(kx1)+1 :MATH]
[MATH: H1
case:y1′
=(1+θ1)x1′+N(0,1)(kx1′)+1. :MATH]
(3)
We defined θ[1] as 10%, 25% and 50%, respectively. The size of real
positive calls consists of 1%, 5% and 10% of the whole simulated data,
respectively. Following these, a 100‐time simulation was conducted to
assess the false positive rate (FPR) and the false negative rate (FNR).
MAQC data and the reproducibility analysis
The MAQC project was developed by the US Food and Drug Administration
(FDA) to provide standards and quality control metrics and involved six
centers [Applied Biosystems (Thermo Fisher Scientific, Waltham, MA,
USA), Affymetrix (Santa Clara, CA, USA), Agilent Technologies (Santa
Clara, CA, USA), GE Healthcare (Chicago, IL, USA), Illumina (San Diego,
CA, USA) and Eppendorf (Hamburg, Germany)] that are major providers of
microarray platforms and RNA samples [47]1, [48]8. The reproducibility
of the top 100 and 1000 significant genes was estimated inter‐ and
intra‐platform by the three statistical methods, and heatmaps were
drawn with the matrix of each batch. For the expression data from the
mouse transplant model, we picked up two out of three cases and
controls to build one batch and made a 9 × 9 matrix heatmap to estimate
the reproducibility. The significance level of mouse microarray data
was 0.05.
Application on mouse transplantation data
We detected differential expression of genes between cases and controls
in three phases: POD5 of cardiac transplantation, POD5 of hepatic
transplantation and POD8 of hepatic transplantation. All P‐values from
expression data were adjusted by the Bonferroni correction. After
getting all significant probes from SFC, we converted the probe level
significance to gene level using an annotation file. Venn diagrams
showed the significant genes with differential expression. Pathway and
gene ontology (GO) enrichment analyses were performed by using the
Database for Annotation, Visualization and Integrated Discovery (DAVID;
[49]http://david.abcc.ncifcrf.gov/) with the Bonferroni
correction‐adjusted P‐values < 0.05 [50]9. Mouse transplantation data
have been deposited in NCBI's Gene Expression Omnibus [51]10 and are
accessible through GEO Series accession no. [52]GSE89340. All data were
conducted by quantile normalization before processing by different
methods. Limma can be found as the R package limma [53]2, [54]3 and the
heatmaps were created by gplots. All R packages can be downloaded from
Bioconductor ([55]www.bioconductor.org).
Quantitative RT‐PCR (qRT‐PCR)
The RNA was reverse‐transcribed to cDNA using a PrimeScript® RT Reagent
Kit (Takara Bio, Shiga, Japan) as described previously [56]11. The
sequences used in our study are shown in Table [57]S3. Quantitative
RT‐PCR (qRT‐PCR) was performed using a SYBR Green system on the Applied
Biosystems PRISM7700 instrument (Thermo Fisher Scientific), and
experiments were conducted using 0.4 μm of each primer in a final
reaction volume of 20 μL of KAPA SYBR® FAST qPCR kit (Kapa Biosystems,
Cape Town, South Africa). The PCR cycling conditions were as follows:
95 °C for 30 s, and 50 cycles of 95 °C for 5 s, 60 °C for 1 min. The
normalized threshold cycle (C [t]) value of each gene was obtained by
subtracting the C [t] value obtained for 18S rRNA. The cardiac mRNA
levels were analyzed on POD5. Figure 4 indicates the number of copies
of each of the three representative mRNAs measured in the syngeneic
grafts or allografts obtained from three individuals. The relative
amount of each mRNA was normalized to that of 18S rRNA. All experiments
were analyzed in three mice per time point and expressed as the
mean ± SEM. The significance level was set as P < 0.05 compared with
syngeneic grafts on day 5.
Results
The SFC method
We observed that the distribution of the mean value and variance of one
probe signal is non‐linear (Fig. [58]S1). The information from
neighboring probes can usually be borrowed to improve the statistical
power [59]2. SFC was introduced to estimate variance for each probe,
rather than obtaining this from all samples; it takes information from
the neighbors of that probe with an adjustable bin size b. As we set up
the default value of b as 1000, the variance of cases and controls in
one probe can be obtained by calculating the median for those probes
separately. Eventually, by following Eqn [60](1), we can obtain the
statistic SFC for every probe, and the P‐value can be further estimated
based from these.
SFC had a better sensitivity and specificity based on simulation data
We investigated the FPR and the FNR of the three methods under the null
hypothesis and alternative hypothesis. As indicated in Eqn [61](2),
signals of the null hypothesis were generated by a simple formula,
y = x, with the Gaussian noise added. The basic formulas are adjustable
with the parameters k. The signals of the alternative hypothesis were
described by Eqn [62](3), with variable values of θ and the portion of
real positive calls. We calculated the FPR and FNR for every different
θ and portion of real positive calls with a 0.05 significance threshold
and 100‐times simulation (Table [63]1).
Table 1.
Evaluation of the three methods with P < 0.05
t test Limma SFC θ
H0
FPR (%) 5.043 5.222 5.694
FNR (%) 0.000 0.000 0.000
Calls in total (%) 5.043 5.222 5.694
H1: simulated real positive calls = 1%
FPR (%) 6.043 5.455 5.350 10%
8.763 6.306 5.038 25%
14.255 8.600 3.990 50%
FNR (%) 6.825 15.367 6.958 10%
0.783 1.933 0.058 25%
0.808 0.025 0.000 50%
Calls in total (%) 6.908 6.240 6.220 10%
9.661 7.217 5.980 25%
15.098 9.507 4.943 50%
H1: simulated real positive calls = 5%
FPR (%) 13.306 7.987 3.616 10%
32.978 17.856 1.818 25%
52.026 34.301 1.057 50%
FNR (%) 6.942 15.283 8.224 10%
0.492 2.108 0.075 25%
0.699 0.020 0.000 50%
Calls in total (%) 17.290 11.820 8.020 10%
36.301 21.854 6.714 25%
54.388 37.5817 5.999 50%
H1: simulated real positive calls = 10%
FPR (%) 27.850 13.782 1.615 10%
56.305 35.345 0.626 25%
73.170 57.081 0.266 50%
FNR (%) 7.282 15.334 9.830 10%
0.551 2.042 0.277 25%
0.652 0.019 0.000 50%
Calls in total (%) 34.336 20.870 10.470 10%
60.619 41.606 10.535 25%
75.787 61.371 10.238 50%
[64]Open in a new tab
Under the null hypothesis, the rates of the three methods are all near
the significance threshold between 5% and 6% (Fig. [65]1A). Under the
alternative hypothesis, SFC had a better performance for FPR than the
other two methods generally (Fig. [66]1B). With an increasing θ and
portion of real positive calls, the FPR of SFC showed a decreasing
bias, whereas Limma and the t test showed a positive bias with these
parameters (Table [67]1). For the FNR, as the θ and portion of real
positive calls increased, Limma showed a faster decline than the t
test, while SFC had a lower FNR than Limma and performed better with
larger θ and portion of real positive calls. Interestingly, SFC shows a
relatively small number of calls (from 4.9% to 10.5%, Table [68]1),
while Limma and the t test calls a larger set in this situation. In
sum, comparing with Limma and the t test at the significance threshold
of 0.05, SFC had a better sensitivity and specificity, especially with
a larger value of differential expression fold change (θ = 50%).
Figure 1.
Figure 1
[69]Open in a new tab
Bar graphs of FPR and FNR from the three methods under the null
hypothesis (H0) and the alternative hypothesis (H1). (A) FPR under the
null hypothesis (FN = 0). (B) FPR and FNR under different alternative
hypotheses, in which θ is equal to 10%, 25% and 50% and the simulated
real positive calls are 1%, 5% and 10% of the whole simulated data,
respectively. The significance threshold is 0.05.
Reproducibility of SFC is better than Limma and the t test based on MAQC and
mouse transplantation data
Reproducibility is an indispensable estimator for the experiments and
algorithms [70]12, [71]13. We chose both the MAQC dataset and the mouse
cardiac transplantation data to assess the reproducibility of SFC,
Limma and the t test.
We calculated the reproducibility of the top 100 and top 1000 genes for
MAQC by using the three methods. For the interplatform comparison, the
heatmap shows that SFC performed a better reproducibility than Limma
and the t test among six platforms when detecting both the top 100 and
the top 1000, while for intra‐platform reproducibility, all three
methods did not perform well in detecting either the top 100 or the top
1000 significant genes (Fig. [72]2A,B). The same operations were
conducted in the mouse cardiac transplantation data, where SFC also
showed a better performance than the others (Fig. [73]2C). Therefore,
according to better performances of reproducibility for both the MAQC
data and the mouse transplantation data, SFC is more stable than Limma
or the t test.
Figure 2.
Figure 2
[74]Open in a new tab
Heatmaps of reproducibility analysis. (A) Reproducibility of top 100
significant genes by t test, Limma and SFC based on MAQC data. (B)
Reproducibility of top 1000 significant genes by the three methods
based on MAQC data. (C) Reproducibility of significant genes by the
three methods based on pairwise analysis of data from the mouse cardiac
graft model.
Intersected significances from mouse transplantation data were found by SFC
and validated by qRT‐PCR
We further utilized the three methods to analyze the mouse organ
transplantation data and validated the results. After the experimental
process generating CEL files from mouse tissues, we conducted these
methods on the expression data of POD5 of cardiac transplantation and
POD5 and POD8 of hepatic transplantation.
According to SFC, 178 significant genes were differentially expressed
in the cardiac allografts compared with isografts, including 158
overexpressed genes and 20 underexpressed genes (Fig. [75]3). There
were also 362 genes (263 overexpression and 99 underexpression) having
significantly different expression in the hepatic POD5 allografts
compared with isografts, and 389 genes (258 overexpression and 131
underexpression) having significantly different expression in the
hepatic POD8 allografts compared with isografts. Based on these, an
intersection of these three groups was obtained that included 52
important genes, in which they are all overexpressed for cardiac
transplantation and 51 overexpressed and one underexpressed for hepatic
transplantation (Fig. [76]3). At the same time, the calling sets of
significant genes underlying Limma and the t test (Fig. [77]S4A,B)
showed no intersecting ones.
Figure 3.
Figure 3
[78]Open in a new tab
Venn diagram of significant genes analyzed by SFC with the level of
significance set at P < 0.05 after the Bonferroni correction. The
overall numbers of significant genes in three phases are shown outside,
which are followed by numbers in parentheses showing the counts of
overexpressed genes versus underexpressed ones. The circle at the top
represents POD5 for heart; the circle at the bottom left represents
POD5 for liver and the circle at the bottom right represents POD8 for
liver.
We further performed qRT‐PCR for the calls derived from SFC to validate
the fold change of the mRNA expression. Nineteen mRNAs, which were
upregulated in both the cardiac and the hepatic allografts compared
with isografts, were randomly selected (Tables [79]2 and [80]S3). Being
consistent with the results of microarray, a significantly higher
amount of mRNA expression was detected in allografts versus isografts
in cardiac (Fig. [81]4A) and hepatic (Fig. [82]4B) allografts.
Table 2.
List of validated genes
Accession no. Gene Gene name Fold‐heart Fold‐liver‐D5 Fold‐liver‐D8
[83]NM_008337 Ifng Interferon gamma 1593.863 54.675 72.591
[84]NM_010259 Gbp2b Guanylate binding protein 2b 1263.049 12.951 18.460
[85]NM_013542 Gzmb Granzyme B 185.351 147.035 114.736
[86]NM_008324 Ido1 Indoleamine‐2,3‐dioxygenase 1 103.729 38.474 47.050
[87]NM_011073 Prf1 Perforin 1 (pore forming protein) 99.539 38.016
37.767
[88]NM_008510 Xcl1 Chemokine (C motif) ligand 1 82.096 27.777 26.918
[89]NM_011579 Tgtp1 T cell specific GTPase 1 76.367 33.074 59.197
[90]NM_021396 Pdcd1lg2 Programmed cell death 1 ligand 2 74.231 14.479
41.463
[91]NM_001081110 Cd8a CD8 antigen, alpha chain 60.400 33.458 32.012
[92]NM_024253 Nkg7 Natural killer cell group 7 sequence 47.828 38.247
30.322
[93]NM_019465 Crtam Cytotoxic and regulatory T cell molecule 46.089
26.296 15.863
[94]NM_001033126 Cd27 CD27 antigen 33.240 39.830 41.565
[95]NM_008798 Pdcd1 Programmed cell death 1 29.391 74.356 69.542
[96]NM_033078 Klrk1 Killer cell lectin‐like receptor subfamily K,
member 1 28.611 18.487 16.631
[97]NM_008530 Ly6f Lymphocyte antigen 6 complex, locus F 27.006 56.930
29.637
[98]NM_011612 Tnfrsf9 Tumor necrosis factor receptor superfamily,
member 9 26.947 30.625 29.872
[99]NM_009977 Cst7 Cystatin F (leukocystatin) 25.625 26.383 30.931
[100]NM_011337 Ccl3 Chemokine (C‐C motif) ligand 3 21.102 47.883 82.279
[101]NM_013652 Ccl4 Chemokine (C‐C motif) ligand 4 19.907 35.686 56.794
[102]Open in a new tab
Figure 4.
Figure 4
[103]Open in a new tab
Validation of the microarray data using a qRT‐PCR assay in the mouse
cardiac graft model and hepatic graft model. (A) Cardiac mRNA levels
analyzed on POD5, indicating the values of mRNAs measured in the
syngeneic grafts (CONT) or allografts (D5) obtained from three
individuals. (B) Hepatic mRNA levels analyzed on POD5 and POD8,
indicating the value of mRNAs measured in the syngeneic grafts (CONT)
or allografts (D5 or D8) obtained from three individuals. A two‐tailed
unpaired t test was used to calculate P‐values comparing syngeneic
grafts with allografts.
Discussion
Microarray is widely used and accepted as a stable, well established
and less costly technology to investigate gene expression data [104]1,
[105]8, [106]14, [107]15. In this study based on microarray data, we
established a novel method, SFC, to detect differential expression and
compared it with the t test and Limma. According to Eqn [108](1), the
parameter b can be adjusted to control the nearby number of probes,
which contribute the variance of the central probe. We set 1000 as
default, and users are able to customize this value based on a
different number size of microarray probe. For the simulation data, the
parameter configurations (θ and k) of the null hypothesis and
alternative hypothesis also can be adjusted (Eqns [109](2) and
[110](3)) [111]7. Moreover, we calculated the FPR and FNR based on
different significance levels (P = 0.01 and 0.001) for different values
of θ and k. With a more stringent significance level (0.05, 0.001), the
FPRs were decreased while the FNRs were increased, which was observed
by all three methods (Figs [112]1, [113]S2 and [114]S3, Tables [115]1,
[116]S1 and [117]S2). Notably, when P = 0.001, the t test gave a high
FPR (48%, θ = 50%, and the true positive gene percentage was 10%) and
Limma performed with a high FNR (sometimes more than 90%). This
suggests the t test will give more positive hits with a high FPR, while
Limma will report fewer hits to reduce the FPR but miss some true
positive ones. Importantly, SFC can give a good balance of FPR and FNR,
and perform well for both FPR and FNR with a stringent significance
level.
Statistical correction (e.g. the Bonferroni correction) is often
introduced for multiple comparisons to adjust the P‐value and control
the false discovery rate [118]16. We also analyzed the mouse
transplantation data by the other two methods (Limma and the t test)
with different significance levels (P = 0.05, 0.001 and 0.05 with the
Bonferroni correction). Limma and the t test had a large number of
positive hits when the P‐value was 0.05 in three phases (Figs [119]S5
and [120]S6). When the level of significance was P < 0.001, the
positive hits by Limma and the t test decreased a lot while by SFC the
number stayed relatively stable. When the P‐value was stringent at 0.05
with the Bonferroni correction (Figs [121]3 and [122]S4), SFC still
reported 52 significances overlapping with three phases, but Limma and
the t test showed no overlapping significance. The results of the t
test showed no shared significance with SFC. Intriguingly, in 67
significances for cardiac POD5 reported by Limma (Fig. [123]S4), 30
genes showed in the cardiac POD5 result for SFC, and 16 showed in the
52 significances. Besides, for hepatic POD5 and POD8 by Limma, 4 out of
7 (POD5) and 19 out of 36 (POD8) significant genes were observed in the
corresponding results of SFC, and 2 out of 5 (overlapping in POD5 and
POD8) significant genes appear in the 52 genes from SFC. As 19 of 52
genes from SFC were randomly selected and all passed the validation of
qRT‐PCR, these results indicated that SFC gave a more stable result
than the t test and Limma.
We therefore investigated the functions of these 52 genes
(Table [124]S4), revealing the most significant pathways were
graft‐versus‐host disease (mmu05332) and allograft rejection
(mmu05330). Moreover, immune system response (e.g. mmu04612, mmu04660,
GO: 0006955) and positive regulation (e.g. GO: 0050863, GO: 0051249,
GO: 0050870) were also activated. All these enrichment analyses
indicated a reaction of transplantation rejection in vivo and
functional changes of the immune system both at the cardiac and at the
hepatic level after 5 days of allografts [125]6, [126]17, [127]18.
In conclusion, based on the quality control experimental data and
simulated data, SFC performed better than Limma and much better than
the t test by using the nearby information of one probe in pooled
probes. We utilized SFC for the real data of mouse transplantation
models, and it reported a more stable and convincing set with 52
significant genes, revealing insights into pathway and gene expression
changes after both cardiac and hepatic allografts. Nineteen genes were
further randomly picked up and validated by qRT‐PCR. We suggest SFC is
a new and effective approach that can detect differential expression
and help to obtain more reliable information in microarray studies.
Author contributions
WZ, YW, XL and JW designed the project. LS supported the MAQC data, and
MF and XL supported the mouse model and validations. WZ carried out the
analysis and simulations. WZ, XL and JW wrote the manuscript. LJ, XL
and JW contributed to the final revision the paper. All authors read
and approved the final manuscript.
Supporting information
Fig. S1. Distribution of mean and variance of sample microarray signals
in each probe derived from the MAQC data.
[128]Click here for additional data file.^ (1.2MB, tiff)
Fig. S2. Bar graphs of FPR and FNR from the three methods under the
null hypothesis (H0) and the alternative hypothesis (H1) with the level
of significance set at P < 0.01.
[129]Click here for additional data file.^ (184.1KB, tiff)
Fig. S3. Bar graphs of FPR and FNR from the three methods under the
null hypothesis (H0) and the alternative hypothesis (H1) with the level
of significance set at P < 0.001.
[130]Click here for additional data file.^ (194.6KB, tiff)
Fig. S4. Venn diagrams of significant gene numbers analyzed by the t
test and Limma with the level of significance set at P < 0.05 after the
Bonferroni correction.
[131]Click here for additional data file.^ (131.7KB, tiff)
Fig. S5. Venn diagrams of significant gene numbers analyzed by the t
test, Limma and SFC with the level of significance set at P < 0.05.
[132]Click here for additional data file.^ (189.4KB, tiff)
Fig. S6. Venn diagrams of significant gene numbers analyzed by the t
test, Limma and SFC with the level of significance set at P < 0.001.
[133]Click here for additional data file.^ (183.8KB, tiff)
Table S1. Evaluation of three methods with the level of significance
set at P < 0.01.
Table S2. Evaluation of three methods with the level of significance
set at P < 0.001.
Table S3. Primer sequences for qRT‐PCR.
[134]Click here for additional data file.^ (24.8KB, docx)
Table S4. GO term and pathway enrichment analysis based on the 52
significant genes.
[135]Click here for additional data file.^ (13.9KB, xlsx)
Acknowledgements