一、β多樣性分析內(nèi)容及意義
a) 樣品間距離計算
Euclidean，Bray-Curtis市框，Unweighted_unifrac，weighted_unifrac昧旨，……拾给，計算兩兩樣品間距離。
意義：用于后續(xù)進一步的 β多樣性分析和可視化統(tǒng)計分析兔沃。
b) PCA分析
主成分分析：利用方差分解蒋得，對多維數(shù)據(jù)進行降維，從而提取出數(shù)據(jù)中最主要的元素和結(jié)構(gòu)的方法乒疏。
意義：揭示復(fù)雜數(shù)據(jù)背景下的簡單規(guī)律额衙。
c) PCoA分析
主坐標(biāo)分析，通過一系列的特征值和特征向量排序從多維數(shù)據(jù)中提取出最主要的元素和結(jié)構(gòu)怕吴。
可基于 bray_curtis窍侧、Weighted Unifrac 和 Unweighted Unifrac 等距離分別來進行 PCoA 分析。 當(dāng)基于Euclidean進行PCoA分析時转绷，PCoA=PCA
意義：選取貢獻率最大的主坐標(biāo)組合伟件，進行差異揭示。
d) NMDS分析
非度量多維尺度分析 是一種將多維空間的研究對象（樣品或變量）簡化到低維空間進行定位议经、分析和歸類斧账，同時又保留對象間原始關(guān)系的數(shù)據(jù)分析方法。
適用于：無法獲得研究對象間精確的相似性或相異性數(shù)據(jù)煞肾，僅能得到他們之間等級關(guān)系數(shù)據(jù)的情形咧织。
特點：根據(jù)樣品中包含的物種信息，以點的形式反映在多維空間上籍救，而對不同樣品間的差異程度习绢，則是通過點與點間的距離體現(xiàn)的，最終獲得樣品的空間定位點圖蝙昙。
PCA、PCoA篮幢、NMDS为迈、RDA之間的區(qū)別：
e) 相似度柱狀圖
根據(jù) β多樣性距離矩陣進行層次聚類（Hierarchical cluatering）分析，使用非加權(quán)組平均法 UPGMA（Unweighted pair group method with arithmetic mean）算法構(gòu)建樹狀結(jié)構(gòu)伴郁。
目的：利用樹枝結(jié)構(gòu)描述和比較多個樣品間的相似性和差異關(guān)系耿战。
f) 組合分析
將β多樣性分析內(nèi)容及性能整合/組合，用于說明樣品（組）之間的差異性焊傅。
二剂陡、β多樣性分析在科技論文中的描述
a) PCA、PCoA狐胎、NMDS：
示例1：
a) PCA、PCoA暴浦、NMDS ：
①關(guān)于圖表的描述：
Trajectory of the gut microbiota in T2D patients treated with XX溅话，XX and XX at weeks 0, 4, 8 and 12. (a) Unweighted Unifrac PCoA of gut microbiota based on the OTU data from the pyrosequencing run. (b) Clustering of gut microbiota based on XXX distances calculated with multivariate analysis of variance (MANOVA). Each point represents the mean principal coordinate (PC) score of all patients in a group at one time point, and the error bar represents the s.e.m. Placebo: n =36; HD: n = 36. ***P<0.0001.
②關(guān)于圖表結(jié)果的描述：
UniFrac PCoA and PCA showed that after 4 weeks of treatment, the gut microbiota structure of the XX group had already significantly diverged from that of its base line and of the XX group.
As the treatment progressed, the gut microbiota made no additional changes.
示例2：
a)PCA屑墨、PCoA、NMDS ：
①關(guān)于圖表的描述：
Allergen sensitization of XXX mice is associated with a microbiotic signature. 【A】NMDS based on weighted UniFrac distance between samples of XXX versus XXX mice performed on the XXX taxa the abundance of which was significantly different between groups by using the KW test. 【B】Hierarchical clustering (average linkage) based on weighted UniFrac distance between samples given abundance of XXX taxa with significant abundance differences across at least 1 of the categories. 【C】Nearest shrunken centroid analysis of OTUs that best characterize the difference between the XXX versus XXX groups. The direction of the horizontal bars reflects either overrepresentation or underrepresentation of the indicated OTUs (left- and right-sided bars, respectively). The length of the bar represents the magnitude of the effect.【D】Representation of the abundance of the OTUs identified by the nearest shrunken centroid analysis using the PAM method. Nine mice were used for the XXX group (n = 5 and 4 mice, respectively), and 5 mice were used for the PBS group.
②關(guān)于圖表結(jié)果的描述：
We used 2 ordination methods, nonmetric multidimensional scaling (NMDS) and hierarchical clustering-average-neighbor (HC-AN) analyses, which examine relationships between ecologic communities, such as those of the microbial flora, to determine whether those OTUs identified by using the KW filter discriminate between XXX and XXX mice. Results revealed that the identified taxa successfully partitioned the mice into 2 distinct groups.
b）基于距離的柱狀圖：
示例：
①圖表描述：
Multivariate analysis based on information at phylum, genus and OTUs levels. 【a】 PCA based on phylum distribution, 【b】PCA based on genus distribution, 【c】 PCA based on total OTUs level information, 【d】cluster analysis of OTUs profile according to Bray Curtis distance (the average linkage),【e】unweighted UniFrac PCoA result, and 【f】weighted UniFrac PCoA result. PC1 and PC2 were used to plot all PCA and PCoA results.
②關(guān)于圖表結(jié)果的描述：
Different multiple variation statistical analyses were used to identify the relationships among sediments collected from the four locations. The resolutions of principal component analysis (PCA) between phyla and genus levels were different. Separating locations at the phylum level was difficult (Fig. a), but a much better discrimination was exhibited at the genus level (Fig. b) indicating that these communities shared similar phyla diversity. The best resolution was obtained at the OTU level with all tags being counted (Fig. c). The hierarchical clustering based on OTU information was also generated (Fig. d). As shown by the cluster analysis, sediment samples from the same location grouped tightly, and the sediments from the four locations could be separated into two lineages; one included XX and XX sediment while the other group consisted of sediments collected outside the XX forest. In general, the distances of samples were within 0.22–0.55, indicating that all sediments shared a high similarity rate in their bacterial structure. Similar results were also found in principal coordinate analysis (PCoA) analysis using the weighted and unweighted UniFrac metrics (Fig. e and f). The PCA, PCoA, and cluster results further indicated that XX samples was closer to XX, which was consistent with the above alpha and beta analysis results.
The clustering and PCoA results obviously showed that the bacterial communities in three, replicated rhizosphere sediments had a high similarity, which was probably due to influences (i.e., the rhizosphere effect) other than spatial variations.
溫馨提示：
一般地，β多樣性分析关炼，可以獨立或幾種分析相結(jié)合的方式來說明樣品間的相似性或差異性程腹，而幾種分析圖表相結(jié)合的方式較為常見。?
在分析結(jié)果中儒拂，這些分析結(jié)果往往都會提供寸潦，因此需要根據(jù)情況色鸳，提取信息。
