Environmental Factor Analysis

RDA and CCA are sorting methods developed based on correspondence analysis. They combine correspondence analysis with multiple regression analysis. Each step of the calculation is a regression with environmental factors, also known as multiple direct gradient analysis. This analysis is mainly used to reflect the relationship between the flora and the environmental factors. RDA is based on a linear model, and CCA is based on a unimodal model. The analysis can detect the relationship between environmental factors, samples, and flora, or the relationship between two.

CCA and RDA ordination plots for the relationship between AOB (A) and AOA (B) prominent OTUs with the environmental parameters in different ponds Fig.1 CCA and RDA ordination plots for the relationship between AOB (A) and AOA (B) prominent OTUs with the environmental parameters in different ponds. (Dai L, et al. 2018)

The selection principle of RDA and CCA method, under the OUT of 97% similarity, first use species-sample data to do DCA analysis, consider the magnitude of the gradient length of the first axis in the analysis result. If the magnitude is greater than 4.0, CCA should be selected, if it is between 3.0- 4.0, both RDA and CCA can be selected. If it is less than 3.0, the result of RDA is better than CCA

The selection principle of RDA and CCA method – Creative Biogene

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Redundancy Analysis

Conceptually, redundancy analysis (RDA) is the constrained principal component analysis. The purpose of RDA is to find new variables to replace the original ones. The coordinates of the quadrats in the sorting diagram are linear combinations of environmental factors. The advantage of this method is that it considers the impact of environmental factors on the quadrats.

Canonical Correlation Analysis

Canonical correlation analysis (CCA) reduces high-dimensional data to one dimension through dimensionality reduction, and then uses correlation coefficients for correlation analysis. CCA helps to clarify the complex and diverse interrelationships between the two sets of variables at a deeper level, and provided that the two sets of variables are continuous variables. CCA is applied to study the relationship between a group of variables and the biological environment.

Tool Used

Analytical tools used in our environmental factor & microbe service include PC-ORD, CANOCO. Both of the two kinds of software can be used to make the plot.

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  1. Dai L, et al. (2018). "Organic matter regulates ammonia-oxidizing bacterial and archaeal communities in the surface sediments of Ctenopharyngodon idellus aquaculture ponds." Frontiers in microbiology. 9: 2290.
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