为了避免各位错过最新的推文教程，强烈建议大家将“科研后花园”设置为“星标”！
###安装LorMe包install.packages("LorMe")##加载MicrobiomeStat包library(LorMe) # Lightening One-Code Resolving Microbial Ecology Solutionlibrary(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphicslibrary(dplyr) # A Grammar of Data Manipulation
##加载示例数据集data(testotu) # Load the standard Qiime output feature tablehead(testotu) # First column -ID, last column -taxonomic annotation, others - the feature table.feature_table<- testotu[, -c(1,22)]tax_anno<- testotu[, c(1,22)]#创建分组数据groupinformation <- data.frame(group = c(rep("a", 10), rep("b", 10)),factor1 = rnorm(10),factor2 = rnorm(mean = 100, 10),subject = factor(c(1:10, 1:10)),group2 = c(rep("e", 5), rep("f", 5), rep("e", 5), rep("f", 5)))###对数据进行封装test_object <- tax_summary(groupfile = groupinformation,inputtable = feature_table,reads = TRUE,taxonomytable = tax_anno)###对一些默认设置进行配置my_col=color_scheme("Plan1")my_order=c("b","a")my_facet_order=c("e","f")test_object_plan2 <- object_config(taxobj = test_object,treat_location = 1,rep_location = 4,facet_location = 5,subject_location = NULL,treat_col = my_col,treat_order = my_order,facet_order = my_facet_order)
Taxonomy tidying...Genarating tables...DoneContained elements:[1] "Groupfile" "Base" "Base_percent"[4] "Base_taxonomy" "Phylum" "Phylum_percent"[7] "Phylum_taxonomy" "Genus" "Genus_percent"[10] "Genus_taxonomy" "parameters"Warning message:Expected 7 pieces. Missing pieces filled with `NA` in 261 rows [4,6, 9, 12, 18, 20, 24, 32, 37, 43, 44, 45, 46, 58, 59, 68, 70, 71,72, 76, ...].
1）进行Beta多样性分析：
community_structure<- structure_plot(taxobj = Two_group,taxlevel = "Base",diagram = "stick",ellipse.level = 0.95)
###PERMANOVA has done######PCA has done######PCoA has done###Run 0 stress 0.08827856Run 1 stress 0.09328546Run 2 stress 0.1106891Run 3 stress 0.08984926Run 4 stress 0.09519836Run 5 stress 0.08827854New best solutionProcrustes: rmse 0.0001176953 max resid 0.0002718019Similar to previous bestRun 6 stress 0.08827854New best solutionProcrustes: rmse 5.594382e-05 max resid 0.0001283387Similar to previous bestRun 7 stress 0.1140033Run 8 stress 0.1067692Run 9 stress 0.09216223Run 10 stress 0.08827854New best solutionProcrustes: rmse 3.776737e-06 max resid 6.700617e-06Similar to previous bestRun 11 stress 0.08984929Run 12 stress 0.110387Run 13 stress 0.1091767Run 14 stress 0.08827855Procrustes: rmse 1.143503e-05 max resid 2.70159e-05Similar to previous bestRun 15 stress 0.1091765Run 16 stress 0.09171475Run 17 stress 0.09359075Run 18 stress 0.09459429Run 19 stress 0.09328543Run 20 stress 0.08827854New best solutionProcrustes: rmse 3.025526e-05 max resid 6.993105e-05Similar to previous best*** Best solution repeated 1 times###NMDS has done#####Output list###PERMANOVA#PERMANOVA:named as('PERMANOVA_statistics')##Plot#PCAplot:named as('PCA_Plot')(1/3)PCoAplot:named as('PCoA_Plot')(2/3)NMDSplot:named as('NMDS_Plot')(3/3)#Analysis object#PCA object:named as('PCA_object')PCoA object:named as('PCoA_object')NMDS object:named as ('NMDS_object')#Coordinates dataframe#PCA Coordinates dataframe:named as('PCA_coordinates')PCoA Coordinates dataframe:named as('PCoA_coordinates')NMDS Coordinates dataframe:named as('NMDS_coordinates')##Done##
2）查看PERMANOVA结果：
print(community_structure$PERMANOVA_statistics)Permutation test for adonis under reduced modelTerms added sequentially (first to last)Permutation: freeNumber of permutations: 999adonis2Df SumOfSqs R2 F Pr(>F)Group 1 0.6802 0.2045 3.5991 0.001 ***Residual 14 2.6460 0.7955Total 15 3.3263 1.0000---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
3）数据查看及可视化（LorMe包Beta多样性分析提供PCoA、PCA以及NMDS三种方法供大家选择）：
#PCoA数据PCoA_coordinates图形PCoA_Plot
PCOA1(22.5%) PCOA2(10.7%) SampleID Group Rep cent1 cent21 -0.20083569 1.920869e-02 Sample1 Treatment 1 -0.2023718 0.014930652 -0.32562289 1.252941e-01 Sample2 Treatment 2 -0.2023718 0.014930653 0.21837293 3.294172e-02 Sample3 Control 1 0.2023718 -0.014930654 0.22946699 4.762851e-02 Sample4 Control 2 0.2023718 -0.014930655 0.19816585 -3.728299e-05 Sample5 Control 3 0.2023718 -0.014930656 0.31838249 1.962647e-01 Sample6 Control 4 0.2023718 -0.014930657 0.32406834 2.008773e-01 Sample7 Control 5 0.2023718 -0.014930658 0.13987165 -2.460515e-01 Sample8 Control 6 0.2023718 -0.014930659 0.06210269 -9.246651e-02 Sample9 Control 7 0.2023718 -0.0149306510 0.12854385 -2.586021e-01 Sample10 Control 8 0.2023718 -0.0149306511 -0.18395269 7.715084e-02 Sample11 Treatment 3 -0.2023718 0.0149306512 -0.20509628 -7.482498e-02 Sample12 Treatment 4 -0.2023718 0.0149306513 -0.19444461 -1.052167e-01 Sample13 Treatment 5 -0.2023718 0.0149306514 -0.22643263 7.214771e-02 Sample14 Treatment 6 -0.2023718 0.0149306515 -0.06648091 -2.118060e-01 Sample15 Treatment 7 -0.2023718 0.0149306516 -0.21610909 2.174914e-01 Sample16 Treatment 8 -0.2023718 0.01493065
#PCAPCA_coordinatesPCA_Plot
PC1 PC2 SampleID Group Rep cent1 cent21 -23.580643 -24.365888 Sample1 Treatment 1 -15.56312 -18.496382 -13.599862 -11.109099 Sample2 Treatment 2 -15.56312 -18.496383 4.124383 18.151454 Sample3 Control 1 15.56312 18.496384 2.665482 20.852532 Sample4 Control 2 15.56312 18.496385 3.174064 83.615681 Sample5 Control 3 15.56312 18.496386 123.236966 -29.287876 Sample6 Control 4 15.56312 18.496387 14.644622 18.705873 Sample7 Control 5 15.56312 18.496388 -6.802447 13.787313 Sample8 Control 6 15.56312 18.496389 -8.220349 11.556028 Sample9 Control 7 15.56312 18.4963810 -8.317801 10.590007 Sample10 Control 8 15.56312 18.4963811 -8.653012 -5.414103 Sample11 Treatment 3 -15.56312 -18.4963812 -11.907421 -8.518922 Sample12 Treatment 4 -15.56312 -18.4963813 -27.574772 -69.790647 Sample13 Treatment 5 -15.56312 -18.4963814 -15.355070 -13.360412 Sample14 Treatment 6 -15.56312 -18.4963815 -10.237921 0.994979 Sample15 Treatment 7 -15.56312 -18.4963816 -13.596220 -16.406920 Sample16 Treatment 8 -15.56312 -18.49638
#NMDSNMDS_coordinatesNMDS_Plot
MDS1 MDS2 SampleID Group Rep cent1 cent21 -0.2957033 -0.07279166 Sample1 Treatment 1 -0.3300792 -0.021048522 -0.5727358 -0.10099619 Sample2 Treatment 2 -0.3300792 -0.021048523 0.3567468 -0.13451069 Sample3 Control 1 0.3300792 0.021048524 0.4533141 0.11348936 Sample4 Control 2 0.3300792 0.021048525 0.3040678 0.02462535 Sample5 Control 3 0.3300792 0.021048526 0.5454692 -0.26255489 Sample6 Control 4 0.3300792 0.021048527 0.5723497 -0.13970404 Sample7 Control 5 0.3300792 0.021048528 0.1785089 0.22568280 Sample8 Control 6 0.3300792 0.021048529 0.0744541 0.04720751 Sample9 Control 7 0.3300792 0.0210485210 0.1557234 0.29415275 Sample10 Control 8 0.3300792 0.0210485211 -0.2637770 -0.18606664 Sample11 Treatment 3 -0.3300792 -0.0210485212 -0.2351927 0.05343645 Sample12 Treatment 4 -0.3300792 -0.0210485213 -0.3856578 0.17168116 Sample13 Treatment 5 -0.3300792 -0.0210485214 -0.3575036 -0.02875466 Sample14 Treatment 6 -0.3300792 -0.0210485215 -0.1262922 0.21872544 Sample15 Treatment 7 -0.3300792 -0.0210485216 -0.4037715 -0.22362206 Sample16 Treatment 8 -0.3300792 -0.02104852
4）由于可视化图形比较固定，所以小编这里也是提取计算结果并自定义可视化：
##以PCoA结果为例PCoA_data <- community_structure$PCoA_coordinatesPCoA_data %>%ggplot(aes(`PCOA1(22.5%)`,`PCOA2(10.7%)`))+#质心连线geom_segment(aes(x =cent1, y = cent2,xend =`PCOA1(22.5%)`, yend = `PCOA2(10.7%)`,color = Group),alpha = 0.6,linewidth = 1, show.legend = FALSE)+geom_point(aes(fill = Group), shape =21, color = "black",size =3)+stat_ellipse(aes(fill=Group,color=Group),geom = "polygon",level = 0.9,#置信度95%linetype=1,linewidth=0.6,alpha=0.2)+#主题设置theme_bw()+theme(panel.grid = element_blank(),axis.text = element_text(size = 10),axis.title = element_text(color = "black", size = 12))+scale_color_manual(values = c("#f68d42","#5ec6f2"))+scale_fill_manual(values = c("#f68d42","#5ec6f2"))
##同理绘制NMDS图形NMDS_data <- community_structure$NMDS_coordinatesNMDS_data %>%ggplot(aes(MDS1,MDS2))+#质心连线geom_segment(aes(x =cent1, y = cent2,xend =MDS1, yend = MDS2,color = Group),alpha = 0.6,linewidth = 1, show.legend = FALSE)+geom_point(aes(fill = Group), shape =21, color = "black",size =3)+stat_ellipse(aes(fill=Group,color=Group),geom = "polygon",level = 0.9,#置信度95%linetype=1,linewidth=0.6,alpha=0.2)+#主题设置theme_bw()+theme(panel.grid = element_blank(),axis.text = element_text(size = 10),axis.title = element_text(color = "black", size = 12))+scale_color_manual(values = c("#f68d42","#5ec6f2"))+scale_fill_manual(values = c("#f68d42","#5ec6f2"))
更多内容大家请阅读LorMe包的帮助文档！
参考：LorMe包帮助文档
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PS: 以上内容是小编个人学习代码笔记分享，仅供参考学习，欢迎大家一起交流学习。
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