Supplementary Materials for
MetaComp: A Comprehensive Analysis Tool for Comparative
Metagenomics
Peng Zhai, Jiangtao Guo, Xiaoqi Wang, Xiao Guo, Fumeng Wang, Fangmin Tian and
Huaiqiu Zhu*
State Key Lab for Turbulence and Complex Systems and Department of Biomedical Engineering, and Center for
Quantitative Biology, Peking University, Beijing 100871, China
*To
whom correspondence should be addressed, Email: hqzhu@pku.edu.cn
S1. Input data
The input data of MetaComp can be represented as an Abundance Profile Matrix (APM). In order to
obtain APM, users first have to align sequences (e.g. DNA, RNA or protein sequence) from
metagenomic samples against a given database (e.g. Pfam, COG or GO). Then, total hit numbers of
all sequences in one sample to each feature (e.g. Pfam ID or COG ID) is counted. The hit number of
sample j to feature i is the value of 𝑐𝑖𝑗 (Table S1). In other words, 𝑐𝑖𝑗 is the total number of feature
i observed in metagenomic sample j .
Table S1. Input data of MetaComp.
Feature
Sample 1
Sample 2
…
Sample n
Feature 1
𝑐11
𝑐21
…
𝑐𝑛1
Feature 2
𝑐12
𝑐22
…
𝑐𝑛2
Feature 3
𝑐13
𝑐23
…
𝑐𝑛3
……
…
…
…
…
Feature m
𝑐1𝑚
𝑐2𝑚
…
𝑐𝑛𝑚
S2. Statistical test
According to the type of input data, we have classified statistical tests into three modes: two samples
test mode, multiple samples test mode and two sample groups test mode.
(1) Two samples test mode
As the sample size of Metagenome is generally large, we choose z-test instead of t-test as our
recommendation for testing. Specifically, N1 and N2 are total counts of features in two samples. Then
z-score for the feature Fi is:
𝑐
𝑐
1
1
𝑧𝑖 = (𝑁𝑖1 + 𝑁𝑖2 )⁄√𝑃(1 − 𝑃)(𝑁 + 𝑁 )
𝑖1
𝑖2
𝑖1
𝑖2
(1)
𝑚
where 𝑁𝑖1 = ∑𝑚
𝑖=1 𝑐𝑖1 , 𝑁𝑖2 = ∑𝑖=1 𝑐𝑖2 , and 𝑃 = (𝑐𝑖1 + 𝑐𝑖2 )⁄(𝑁𝑖1 + 𝑁𝑖2 ). Since z-test is not valid,
the prerequisite of z-test is min⁡(𝑐𝑖1 , 𝑐𝑖2 ) ≤ 𝑧𝑖2 .
(2) Multiple samples test mode
Two samples tests between all conceivable pairs of samples are executed in this mode. For a specific
feature i, the minimum of all conceivable p-values is considered as the p-value of this feature. Thus
we can identify that this feature is significantly different in at least one pair of samples.
(3) Two groups of samples test mode
In MetaComp, we provide four statistical test methods (t-test, Paired t-test, Mann-Whitney U test and
Wilcoxon sign-rank test) to assess whether a specific feature is significantly different between two
groups. Users can choose a proper method themselves. Meanwhile, in order to facilitate the user
without adequate statistic knowledge, MetaComp can automatically select the most suitable test
method according to your input data. The appropriate statistical test method is chosen according to
the contingency table shown in Table S2. Moreover, odds ratio test is implemented for evaluating the
abundance of each feature. As the contingency table shown in Table S3, 𝑐𝑖𝑗 denotes the count of
feature i in sample j. Considering the possibility of unevenness between two groups, an empirical
continuity correction is introduced to improve the accuracy of the test (Sweeting, M.J. et al., 2004).
Consequently, the statistics of the OR statistic for feature i is:
⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡log 2 𝑂𝑅(𝑖) = log 2
𝑅
1
)(𝑀22 +
)
𝑅+1
𝑅+1
1
𝑅
(𝑀12 +
)(𝑀21 +
)
𝑅+1
𝑅+1
(𝑀11 +
(2)
where 𝑅 = 𝑀1 ⁄𝑀2 . According to the computation above, features can be classified as group 1
enriched (when log 2 𝑂𝑅(𝑖) > 1) and group 1 scarcity (when log 2 𝑂𝑅(𝑖) < 1).
S3. Multiple Hypothesis Testing Correction
A typical metagenomic profile consists of several hundreds to thousands of features (e.g. Pfam/Cog
functional profiles). Therefore, direct application of statistical method described above may probably
lead to large numbers of false positive. For example, choosing a threshold of 0.05 will result in 500
false positives in a profile contains 10000 features. Two correction methods are implemented in
MetaComp to solve this problem, including default option false discovery rate (FDR) and an
alternative option Bonferroni correction.
Table S2. Method for comparison of two groups of samples
independent
Correlatedc
Parametrica
Non- parametricb
t-test
Paired t-test
Mann-Whitney U test
Wilcoxon sign-rank test
a. Under the assumption that the variable follow a normal distribution
b. Applicable when sample size is small and normality assumption is violated
c. Consist of a sample of matched pairs of similar units, or one group of units that has been tested twice
Table S3. Contingency table for odd ratio test
Group 1(G1)
Feature i
𝑀11 = ∑
Other Feature
𝑀21 = ∑
Total
𝑗∈𝐺1
𝑗∉𝐺1
Group 2(G2)
𝑐𝑖𝑗
𝑀12 = ∑
𝑐𝑖𝑗
𝑀22 = ∑
𝑀1 = 𝑀11 + 𝑀21
𝑗∈𝐺2
𝑗∉𝐺2
Total
𝑐𝑖𝑗
𝑛1 = 𝑀11 + 𝑀12
𝑐𝑖𝑗
𝑛2 = 𝑀21 + 𝑀22
𝑀2 = 𝑀12 + 𝑀22
S4. Environmental Factor Analysis
This analysis is implemented by regression analysis via the lasso algorithm. MetaComp first
normalizes the input data and environmental factors data before analysing. After that, the ith
environmental factor in jth sample (Xij ) is considered as independent variable, and the jth frequency
of a given feature (Yj ) is considered as dependent variable. The regression function is:
m
n
Y j = ∑i αi Xji + ∑m≠n
m,n βm,n X j × X j
(3)
where 𝑋𝑗𝑚 × 𝑋𝑗𝑛 means the co-effect of environmental factor 𝑋𝑗𝑚 and 𝑋𝑗𝑛 to feature Y𝑗 , 𝛼𝑖 , 𝛽𝑚,𝑛
represent the regression coefficients of the function. Moreover, the reliability of the regression
coefficients is determined by p-value. Only when all p-values (p-values of all regression coefficients
and p-value of the regression function) meet the prescribed standard, we shall accept the result of
regression.
Table S4. Part of the detailed result of the analysis of whale fall, Acid Mine Drainage, Sargasso Sea, and Minnesota soil metagenomic samples
AMD Soil
S.1a
S.2
S.3 W.Boneb W.Mat W.Rib p-value
q-value
Function
PF01036
0
1
344
354
332
0
0
0
1.60e-35
4.67e-34
Bacteriorhodopsin-like protein
PF03814
99
17
3
4
7
0
0
1
5.22e-48
2.92e-46
Ion channel KdpA Potassium-transporting ATPase A subunit
PF02705
0
87
15
30
30
10
0
1
2.27e-56
3.56e-54
APC K trans K+ potassium transporter
PF01077
42
4870
62
51
71
57
45
37
0
0
NIR_SIR Nitrite and sulphite reductase 4Fe-4S domain
a.S=Sargasso Sea
b.W=whale fall
Table S5. Part of the detailed result of relationship between metagenomic samples and environmental factors
Feature
DIPa
COG0378
0
Oxygenb DIP&Oxygen
p-value
Correlation
Annotation
0
3.303E-06
2.974E-02
9.006E-01
Ni2+-binding GTPase involved in regulation of expression and mature
COG1921 1.157E-03
0
0
8.413E-02
8.223E-01
Selenocysteine synthase [seryl-tRNASer selenium transferase]
COG0318 4.841E-03
0
0
8.972E-02
8.152E-01
Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II
a.DIP=dissolved inorganic phosphate
b.Oxygen=oxygen content
References
Sweeting, M.J. et al. (2004) What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of
sparse data, Statistics in medicine, 23(9), 1351-1375