Team:GDSYZX/Model

MODEL

Using ANOVA+t.test statistical modeling to analyze amino acid/peptide/degradation rate/overall recovery/enzyme activity:

1. amino acid data input

sample_id value promoter time
sample1 4657.313 SCUT-3 24h
sample2 5133.433 SCUT-3 24h
sample3 6764.776 SCUT-Ocdo-Osep39 24h
sample4 6815.522 SCUT-Ocdo-Osep39 24h
sample5 3790.149 SCUT-Ocdo-p1380-Osep39 24h
sample6 4306.567 SCUT-Ocdo-p1380-Osep39 24h
sample7 5555.821 SCUT-p22610-Ocdo-Osep39 24h
sample8 5922.985 SCUT-p22610-Ocdo-Osep39 24h
sample9 6072.239 SCUT-p22610-Ocdo-Osep39 24h
sample10 5887.164 SCUT-p22610-Ocdo-Osep39 24h
sample1 22294.925 SCUT-3 48h
sample2 21471.045 SCUT-3 48h
sample3 22384.478 SCUT-Ocdo-Osep39 48h
sample4 23351.642 SCUT-Ocdo-Osep39 48h
sample5 20688.955 SCUT-Ocdo-p1380-Osep39 48h
sample6 22551.642 SCUT-Ocdo-p1380-Osep39 48h
sample7 22569.552 SCUT-p22610-Ocdo-Osep39 48h
sample8 21560.597 SCUT-p22610-Ocdo-Osep39 48h
sample9 21751.642 SCUT-p22610-Ocdo-Osep39 48h
sample10 24581.493 SCUT-p22610-Ocdo-Osep39 48h

(SCUT-3) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0007835 0.00078 0.00078 *** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0025866 0.0026 0.0026 ** T-test

(SCUT-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0009059 0.00091 0.00091 *** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0188224 0.019 0.019 * T-test

(SCUT-Ocdo-p1380-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0030115 0.003 0.003 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0232938 0.023 0.023 * T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0010796 0.0011 0.0011 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0092383 0.0092 0.0092 ** T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0067378 0.0067 0.0067 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.051381 0.051 0.051 ns T-test

2. peptide data input

sample_id value promoter time
sample1 1105.0769 SCUT-3 6h
sample2 1124.2308 SCUT-3 6h
sample3 854.6923 SCUT-Ocdo-Osep39 6h
sample4 851.8462 SCUT-Ocdo-Osep39 6h
sample5 1087.1538 SCUT-Ocdo-p1380-Osep39 6h
sample6 1055.3846 SCUT-Ocdo-p1380-Osep39 6h
sample7 1127.3846 SCUT-p22610-Ocdo-Osep39 6h
sample8 858.2308 SCUT-p22610-Ocdo-Osep39 6h
sample9 1384.6923 SCUT-p22610-Ocdo-Osep39 6h
sample10 982.6154 SCUT-p22610-Ocdo-Osep39 6h
sample1 4248.8462 SCUT-3 24h
sample2 3602.6923 SCUT-3 24h
sample3 5126.5385 SCUT-Ocdo-Osep39 24h
sample4 4837.6923 SCUT-Ocdo-Osep39 24h
sample5 4126.9231 SCUT-Ocdo-p1380-Osep39 24h
sample6 3359.6154 SCUT-Ocdo-p1380-Osep39 24h
sample7 5515.0000 SCUT-p22610-Ocdo-Osep39 24h
sample8 5074.6154 SCUT-p22610-Ocdo-Osep39 24h
sample9 4440.0000 SCUT-p22610-Ocdo-Osep39 24h
sample10 5235.0000 SCUT-p22610-Ocdo-Osep39 24h
sample1 7247.1429 SCUT-3 30h
sample2 6476.7857 SCUT-3 30h
sample3 8390.3571 SCUT-Ocdo-Osep39 30h
sample4 8304.6429 SCUT-Ocdo-Osep39 30h
sample5 8226.4286 SCUT-Ocdo-p1380-Osep39 30h
sample6 6631.0714 SCUT-Ocdo-p1380-Osep39 30h
sample7 9748.9286 SCUT-p22610-Ocdo-Osep39 30h
sample8 8673.2143 SCUT-p22610-Ocdo-Osep39 30h
sample9 8366.7857 SCUT-p22610-Ocdo-Osep39 30h
sample10 9796.0714 SCUT-p22610-Ocdo-Osep39 30h
sample1 14232.1429 SCUT-3 48h
sample2 12535.7143 SCUT-3 48h
sample3 12985.7143 SCUT-Ocdo-Osep39 48h
sample4 12928.5714 SCUT-Ocdo-Osep39 48h
sample5 13691.0714 SCUT-Ocdo-p1380-Osep39 48h
sample6 13039.2857 SCUT-Ocdo-p1380-Osep39 48h
sample7 14814.2857 SCUT-p22610-Ocdo-Osep39 48h
sample8 12626.7857 SCUT-p22610-Ocdo-Osep39 48h
sample9 15828.5714 SCUT-p22610-Ocdo-Osep39 48h
sample10 12642.8571 SCUT-p22610-Ocdo-Osep39 48h

(SCUT-3) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0002503 0.00025 0.00025 *** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 6h 24h 0.0726248 0.18 0.073 ns T-test
value 6h 30h 0.0424828 0.18 0.042 * T-test
value 6h 48h 0.0439166 0.18 0.044 * T-test
value 24h 30h 0.0300798 0.18 0.030 * T-test
value 24h 48h 0.0338185 0.18 0.034 * T-test
value 30h 48h 0.0469815 0.18 0.047 * T-test

(SCUT-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 2e-07 2e-07 1.7e-07 **** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 6h 24h 0.0222467 0.0350 0.02225 * T-test
value 6h 30h 0.0036029 0.0140 0.00360 ** T-test
value 6h 48h 0.0014615 0.0073 0.00146 ** T-test
value 24h 30h 0.0172796 0.0350 0.01728 * T-test
value 24h 48h 0.0087497 0.0260 0.00875 ** T-test
value 30h 48h 0.0003397 0.0020 0.00034 *** T-test

(SCUT-Ocdo-p1380-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0002005 2e-04 2e-04 *** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 6h 24h 0.0903173 0.240 0.0903 ns T-test
value 6h 30h 0.0793583 0.240 0.0794 ns T-test
value 6h 48h 0.0166184 0.083 0.0166 * T-test
value 24h 30h 0.0904189 0.240 0.0904 ns T-test
value 24h 48h 0.0030665 0.018 0.0031 ** T-test
value 30h 48h 0.0536034 0.210 0.0536 ns T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0005517 0.00055 0.00055 *** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 6h 24h 0.0077009 0.046 0.0077 ** T-test
value 6h 30h 0.0315300 0.160 0.0315 * T-test
value 6h 48h 0.0513645 0.210 0.0514 ns T-test
value 24h 30h 0.0550608 0.210 0.0551 ns T-test
value 24h 48h 0.0723613 0.210 0.0724 ns T-test
value 30h 48h 0.1043014 0.210 0.1043 ns T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0020349 0.002 0.002 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 6h 24h 0.0327874 0.20 0.033 * T-test
value 6h 30h 0.0426962 0.21 0.043 * T-test
value 6h 48h 0.0733354 0.23 0.073 ns T-test
value 24h 30h 0.0572928 0.23 0.057 ns T-test
value 24h 48h 0.0910098 0.23 0.091 ns T-test
value 30h 48h 0.1486648 0.23 0.149 ns T-test

3. degradation rate data input

sample_id rep_id promoter value
sample1 rep1 SCUT-3 0.788
sample2 rep2 SCUT-3 0.752
sample3 rep1 SCUT-Ocdo-Osep39 0.800
sample4 rep2 SCUT-Ocdo-Osep39 0.804
sample5 rep1 SCUT-Ocdo-p1380-Osep39 0.792
sample6 rep2 SCUT-Ocdo-p1380-Osep39 0.736
sample7 rep1 SCUT-p22610-Ocdo-Osep39 0.804
sample8 rep2 SCUT-p22610-Ocdo-Osep39 0.796
sample9 rep1 SCUT-p22610-Ocdo-Osep39 0.828
sample10 rep2 SCUT-p22610-Ocdo-Osep39 0.816
.y. p p.adj p.format p.signif method
value 0.1707243 0.17 0.17 ns Anova
.y. group1 group2 p p.adj p.format p.signif method
value SCUT-3 SCUT-Ocdo-Osep39 0.3233419 1 0.32 ns T-test
value SCUT-3 SCUT-Ocdo-p1380-Osep39 0.8760431 1 0.88 ns T-test
value SCUT-3 SCUT-p22610-Ocdo-Osep39 0.3342052 1 0.33 ns T-test
value SCUT-3 SCUT-p22610-Ocdo-Osep39 0.1850207 1 0.19 ns T-test
value SCUT-Ocdo-Osep39 SCUT-Ocdo-p1380-Osep39 0.4033778 1 0.40 ns T-test
value SCUT-Ocdo-Osep39 SCUT-p22610-Ocdo-Osep39 0.7117228 1 0.71 ns T-test
value SCUT-Ocdo-Osep39 SCUT-p22610-Ocdo-Osep39 0.1577581 1 0.16 ns T-test
value SCUT-Ocdo-p1380-Osep39 SCUT-p22610-Ocdo-Osep39 0.4176720 1 0.42 ns T-test
value SCUT-Ocdo-p1380-Osep39 SCUT-p22610-Ocdo-Osep39 0.2752650 1 0.28 ns T-test
value SCUT-p22610-Ocdo-Osep39 SCUT-p22610-Ocdo-Osep39 0.1095996 1 0.11 ns T-test

4. overall recovery data input

sample_id promoter time value
sample1 SCUT-3 24h 10946.757
sample2 SCUT-3 24h 9874.036
sample3 SCUT-Ocdo-Osep39 24h 12383.106
sample4 SCUT-Ocdo-Osep39 24h 12062.916
sample5 SCUT-Ocdo-p1380-Osep39 24h 11121.401
sample6 SCUT-Ocdo-p1380-Osep39 24h 9980.511
sample7 SCUT-p22610-Ocdo-Osep39 24h 12730.373
sample8 SCUT-p22610-Ocdo-Osep39 24h 12840.884
sample9 SCUT-p22610-Ocdo-Osep39 24h 11617.463
sample10 SCUT-p22610-Ocdo-Osep39 24h 13146.343
sample1 SCUT-3 48h 37179.606
sample2 SCUT-3 48h 34451.535
sample3 SCUT-Ocdo-Osep39 48h 35909.296
sample4 SCUT-Ocdo-Osep39 48h 35526.183
sample5 SCUT-Ocdo-p1380-Osep39 48h 35645.698
sample6 SCUT-Ocdo-p1380-Osep39 48h 32635.704
sample7 SCUT-p22610-Ocdo-Osep39 48h 36414.883
sample8 SCUT-p22610-Ocdo-Osep39 48h 34694.248
sample9 SCUT-p22610-Ocdo-Osep39 48h 43262.601
sample10 SCUT-p22610-Ocdo-Osep39 48h 36713.902

(SCUT-3) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0033119 0.0033 0.0033 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0168858 0.017 0.017 * T-test

(SCUT-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0001129 0.00011 0.00011 *** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0001433 0.00014 0.00014 *** T-test

(SCUT-Ocdo-p1380-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0046228 0.0046 0.0046 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0220143 0.022 0.022 * T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0014305 0.0014 0.0014 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0234883 0.023 0.023 * T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0145126 0.015 0.015 * Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0628446 0.063 0.063 ns T-test

5. enzyme activity data input

sample_id promoter time value
sample1 SCUT-3 24h 5.844828
sample2 SCUT-3 24h 6.827586
sample3 SCUT-Ocdo-Osep39 24h 7.396552
sample4 SCUT-Ocdo-Osep39 24h 7.586207
sample5 SCUT-Ocdo-p1380-Osep39 24h 4.551724
sample6 SCUT-Ocdo-p1380-Osep39 24h 3.155172
sample7 SCUT-p22610-Ocdo-Osep39 24h 6.465517
sample8 SCUT-p22610-Ocdo-Osep39 24h 11.655172
sample9 SCUT-p22610-Ocdo-Osep39 24h 6.931034
sample10 SCUT-p22610-Ocdo-Osep39 24h 11.120690
sample1 SCUT-3 48h 5.482759
sample2 SCUT-3 48h 10.344828
sample3 SCUT-Ocdo-Osep39 48h 18.758621
sample4 SCUT-Ocdo-Osep39 48h 18.034483
sample5 SCUT-Ocdo-p1380-Osep39 48h 15.344828
sample6 SCUT-Ocdo-p1380-Osep39 48h 7.758621
sample7 SCUT-p22610-Ocdo-Osep39 48h 14.896552
sample8 SCUT-p22610-Ocdo-Osep39 48h 20.586207
sample9 SCUT-p22610-Ocdo-Osep39 48h 24.482759
sample10 SCUT-p22610-Ocdo-Osep39 48h 30.689655

(SCUT-3) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.5898088 0.59 0.59 ns Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.6327277 0.63 0.63 ns T-test

(SCUT-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0011759 0.0012 0.0012 ** Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0142342 0.014 0.014 * T-test

(SCUT-Ocdo-p1380-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.1840481 0.18 0.18 ns Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.2831825 0.28 0.28 ns T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.152869 0.15 0.15 ns Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.1539333 0.15 0.15 ns T-test

(SCUT-p22610-Ocdo-Osep39) value~time - one way ANOVA + t.test

.y. p p.adj p.format p.signif method
value 0.0383703 0.038 0.038 * Anova
.y. group1 group2 p p.adj p.format p.signif method
value 24h 48h 0.0496149 0.05 0.05 * T-test

6. WGCNA

Weighted correlation network analysis, also known as weighted gene co-expression network analysis (WGCNA), is a widely used data mining method especially for studying biological networks based on pairwise correlations between variables. While it can be applied to most high-dimensional data sets, it has been most widely used in genomic applications. It allows one to define modules (clusters), intramodular hubs, and network nodes with regard to module membership, to study the relationships between co-expression modules, and to compare the network topology of different networks (differential network analysis). WGCNA can be used as a data reduction technique (related to oblique factor analysis), as a clustering method (fuzzy clustering), as a feature selection method (e.g. as gene screening method), as a framework for integrating complementary (genomic) data (based on weighted correlations between quantitative variables), and as a data exploratory technique.[1] Although WGCNA incorporates traditional data exploratory techniques, its intuitive network language and analysis framework transcend any standard analysis technique. Since it uses network methodology and is well suited for integrating complementary genomic data sets, it can be interpreted as systems biologic or systems genetic data analysis method. By selecting intramodular hubs in consensus modules, WGCNA also gives rise to network based meta analysis techniques.[2]

WGCNA R software (v 1.66) [3] was used here to divide 5360 of Streptomyces sp. SCUT-3 genes into modules that have similar expression patterns. With a combination of signed network type and bicor correlation method, genes were robustly positive-correlated with each other in the same module. In this study, the scale-free topological fitting index of the soft threshold cannot exceed more than 0.8 after reaching the power value 30, but no abnormal samples are found after checking at the clustering results. Therefore, we inferred that this difference is caused by the special biological significance, and selected the empirical power value 6 for carrying out the follow-up WGCNA analysis to construct a more conservative co-expression network (Figure showed below the file description table).

The file description is showed as below:
feather_lb and feather are experimental group for Streptomyces sp. SCUT-3 cultured LB (Luria broth) culture medium (control) or culture medium with feather add-in, chitin_lb and chitin are another group for Streptomyces sp. SCUT-3 cultured LB culture medium (control) or culture medium with chitin add-in.

Sample Sample_Name Sample_Group
chitin-2 chitin-2 chitin
chitin-3 chitin-3 chitin
chitin-lb-1 chitin-lb-1 chitin-lb
chitin-lb-2 chitin-lb-2 chitin-lb
chitin-lb-3 chitin-lb-3 chitin-lb
feather-1 feather-1 feather
feather-2 feather-2 feather
feather-lb-1 feather-lb-1 feather-lb
feather-lb-2 feather-lb-2 feather-lb

It is worth mentioning that we also developed and provided a Qt compiled software to automatically finishe the WGCNA analyses! (see navigation 'Software')

Reference

[1] Horvath S (2011). Weighted Network Analysis: Application in Genomics and Systems Biology. New York, NY: Springer. ISBN 978-1-4419-8818-8.

[2] Langfelder P, Mischel PS, Horvath S, Ravasi T (17 April 2013). "When Is Hub Gene Selection Better than Standard Meta-Analysis?". PLOS ONE. 8 (4): e61505. Bibcode:2013PLoSO...861505L. doi:10.1371/journal.pone.0061505. PMC 3629234. PMID 23613865.

[3] Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. doi: 10.1186/1471-2105-9-559. PubMed PMID: 19114008; PubMed Central PMCID: PMCPMC2631488.