Gge Biplot Full Version
Gildo Santiago
I am trying to create a biplot and keep getting this error. I have run the same command in several versions of qiime2-2019 (.10, .4, and .1) with several different datasets with varying levels of analysis (SEPP and not, debloomed and not, greengenes, silva, mitochondria & chloroplast and not). I have been following this post as a guide for generating the necessary lead up files.
Since it seems like the final eigenvalue is the problem, you might be able to get around this by running the rarefaction? --> beta diversity --> PCoA --> PCoA biplot commands manually (i.e. without using the core-metrics-phylogenetic pipeline). I think this would allow you to run qiime diversity pcoa and explicitly set --p-number-of-dimensions to a value smaller than 41, and I think this should solve this problem.
@fedarko Thank you very much for your help. After following your recommendation and adding --p-number-of-dimensions when I did my pcoa using qiime diversity pcoa all was right. The pcoa biplot run without errors and I could use it in the qiime view.
What would be the easiest way to go about checking the correct --p-n-number-of-dimensions that might be ideal for your dataset? The biplots are a bit hit for the metabolomics community and we are receiving the same error when trying to create biplots with version 2019.10!
A biplot is plot which aims to represent both the observations and variables of a matrix of multivariate data on the same plot. There are many variations on biplots (see the references) and perhaps the most widely used one is implemented by biplot.princomp. The function biplot.default merely provides the underlying code to plot two sets of variables on the same figure.
Generates an interactive ordination biplot where the user can visually integrate sample and feature metadata. Vectors representing the n most important features are then plotted in the emperor visualization (5 largest, by default).
(a) A data set in table form, where the columns are separate variables andthe rows are separate observations (identified by a row ID variable). Inthis arrangment, use the VAR= argument to specify this list of variables and the ID= variable to specify an additional variable whose values are labels for therows.Assume a dataset of reaction times to 4 topics in 3 experimental tasks, ina SAS dataset like this: TASK TOPIC1 TOPIC2 TOPIC3 TOPIC4 Easy 2.43 3.12 3.68 4.04 Medium 3.41 3.91 4.07 5.10 Hard 4.21 4.65 5.87 5.69For this arrangment, the macro would be invoked as follows: %biplot(var=topic1-topic4, id=task);(b) A contingency table in frequency form (e.g., the output from PROCFREQ), or multi-way data in the univariate format used as input to PROCGLM. In this case, there will be two or more factor (class) variables, andone response variable, with one observation per cell. For this form, youmust use the VAR= argument to specify the two (or more) factor (class) variables, and specifythe name of response variable as the RESPONSE= parameter. Do not specify an ID= variable for this form.For contingency table data, the response will be the cell frequency, andyou will usually use the POWER=0 parameter to perform an analysis of the log frequency.The same data in this format would have 12 observations, and look like: TASK TOPIC RT Easy 1 2.43 Easy 2 3.12 Easy 3 3.68 ... Hard 4 5.69For this arrangment, the macro would be invoked as follows: %biplot(var=topic task, response=RT);In this arrangement, the order of the VAR= variables does not matter. The columns of the two-way table are determinedby the variable which varies most rapidly in the input dataset (topic, inthe example).UsageThe BIPLOT macro is defined with keyword parameters. The VAR=parameter must be specified, together with either one ID= variable or one RESPONSE= variable.The arguments may be listed within parentheses in any order, separated bycommas. For example: %biplot(data=soccer, var=_num_, id=home);The plot may be re-drawn or customized using the output OUT=data set of coordinates and the ANNO= Annotate data set.The graphical representation of biplots requires that the axes in the plotare equated, so that equal distances on the ordinate and abscissa representequal data units (to perserve distances and angles in the plot). A '+',whose vertical and horizontal lengths should be equal, is drawn at theorigin to indicate whether this has been achieved.If you do not specifiy the HAXIS= and YAXIS= parameters, the EQUATE macro is called to generate the AXIS statements toequate the axes. In this case the INC=, XEXTRA=, and YEXTRA=, parameters may be used to control the details of the generated AXISstatements.By default, the macro produces and plots a two-dimensional solution.ParametersDATA=Specifies the name of the input data set to be analyzed. [Default: DATA=_LAST_]VAR=Specifies the names of the column variables when the data are in tableform, or the names of the factor variables when the data are in frequencyform or GLM form. [Default: VAR=_NUM_]ID=Observation ID variable when the data are in table form.RESPONSE=Name of the response variable (for GLM form) DIM=Specifies the number of dimensions of the CA/MCA solution. Only twodimensions are plotted by the PPLOT and GPLOT options, however. [Default: DIM=2]FACTYPE=Biplot factor type: GH, SYM, JK or COV [Default: FACTYPE=SYM]VARDEF=Variance def for FACTYPE=COV: DF N [Default: VARDEF=DF]SCALE=Scale factor for variable vectors [Default: SCALE=1]POWER=Power transform of response [Default: POWER=1]OUT=Specifies the name of the output data set of coordinates. [Default: OUT=BIPLOT]ANNO=Specifies the name of the annotate data set of labels produced by themacro. [Default: ANNO=BIANNO]STD=How to standardize columns: NONE MEAN STD [Default: STD=MEAN]COLORS=Colors for OBS and VARS [Default: COLORS=BLUE RED]SYMBOLS=Symbols for OBS and VARS [Default: SYMBOLS=NONE NONE]INTERP=Markers/interpolation for OBS and VARS. [Default: INTERP=NONE VEC]LINES=Lines for OBS and VARS interpolation [Default: LINES=33 20]PPLOT=Produce a printer plot? [Default: PPLOT=NO]VTOH=The vertical to horizontal aspect ratio (height of one character divided bythe width of one character) of the printer device, used to equate axes fora printer plot, when PPLOT=YES. [Default: VTOH=2]GPLOT=Produce a graphics plot? [Default: GPLOT=YES]PLOTREQ=The dimensions to be plotted [Default: PLOTREQ=DIM2*DIM1]HAXIS=AXIS statement for horizontal axis. If both HAXIS= andVAXIS= are omitted, the program calls the EQUATE macro to define suitable axisstatements. This creates the axis statements AXIS98 and AXIS99, whether ornot a graph is produced.VAXIS=The name of an AXIS statement for the vertical axis.INC=The length of X and Y axis tick increments, in data units (for the EQUATEmacro). Ignored if HAXIS= and VAXIS= are specified. [Default: INC=0.5 0.5]XEXTRA=Number of extra X axis tick marks at the left and right. Use to allow extraspace for labels. [Default: XEXTRA=0 0]YEXTRA=Number of extra Y axis tick marks at the bottom and top. [Default: YEXTRA=0 0]M0=Length of origin marker, in data units. [Default: M0=0.5]DIMLAB=Prefix for dimension labels [Default: DIMLAB=Dimension]NAME=Name of the graphics catalog entry [Default: NAME=BIPLOT] DependenciesBIPLOT requires the following macros:
In additive main effects and multiplicative interaction 1 (AMMI 1), the biplot abscissa and ordinate indicated the 1st principal component (PC1) term and the trait's significant influence, respectively. In this study, Fig. 7 (Pattern A, B, C, and D) showing the additive main effects and multiplicative interaction 30 genotype and 4 environments for the trait TNP, FPW, HSW, and yield per hectare, respectively. Based on genotype mean and interaction with the environment a little similarity was found among the genotypes. For hundred seed weight (Fig. 7: Pattern C) environment three (ENV3), ENV2 for total number of pods (Fig. 7: Pattern A), ENV4 for fresh pod weight (Fig. 7: Pattern B), and ENV2 for yield per hectare (Fig. 7: Pattern D) had a PCA1 score or vector closer to zero compared to other environments, indicates lower interaction effect which almost ensures the better performance of all genotypes in that environment. Moreover, these environments are treated as suitable for all genotypes evaluated. For total number of pods the genotypes G24, G10, G7, G9, G5, G26, and G12 (Fig. 7: Pattern A); for fresh pod weight the genotypes G24, G19, G10, G15, G17, G11, G3, G27, and G29 (Fig. 7: Pattern B); for hundred seed weight the genotypes G10, G29, G18, G21, G1(Fig. 7: Pattern C); for yield per hectare the genotypes G18, G14, G7, G3, G1, G5, and G4 (Fig. 7: Pattern D) had approximately zero scores on the first PCA1 axis which indicates that these genotypes were less influenced by the environment. However, some genotypes had their mean below-average performance though, in general, plant breeders are highly attracted to genotypes that are high-yielding and relatively more stable. Genotypes with PC1 scores adjacent to zero lines of biplot indicated that genotypes were suited to all environments, whereas PC1 vectors with the same sign and score but away from zero lines of biplot indicated that genotypes were adapted to a specific environment, is supported by Murphy et al.47. When the PCA1 score for a genotype or environment is near to zero, there is a small interaction impact; contrary, if a genotype and environment achieve the same sign on the PCA axis, there is a positive interaction; otherwise, there is a negative interaction. A report published by Mogale11 is comparable to our findings in Bambara groundnut and Oladosu et al.19 in rice.
The main intention of this current multi-environmental study is to evaluate V. subterranea genotypes based on mean performance under a wide range of environments in order to identify superior genotypes. The multi environmental trail (MET) of Bambara groundnut genotypes may also give information on genotype adaptability and stability to a certain environmental situation. Eventually, a genotype is proposed for commercial cultivation, its susceptibility to genotype by environment interaction (GEI) should be assessed. However, considering the multivariate (GGE and AMMI biplot) statistical result the tested genotypes are categorized into three major groups. Group one genotypes are those that are highly stable and have a high yielding potential. This group comprises genotypes of G1, G3, and G5, which are well suited to a range of environments. The basic criteria for the second category are genotypes with low stability but high yield per hectare. This group contains genotypes of G2 and G4 (perform better in ENV1), as well as G6, G8, and G7 (perform better in ENV2) that are appropriate for a specific environment. The last group worked with genotypes that had a low yield but a high stability. This group comprises genotypes of G10, G13, G11, G14, G17, G19, and G18, which are ideal for breeding schemes intended to improve certain phenotypes. This category of genotypes may have yield component compensation criteria, such as the ability to recover quickly from a wide range of environmental challenges. Genotypes G1, G3, and G5 performed well across all test locations and designated as ideal in terms of mean, stability, high yield, and emerged as the top genotype among those investigated. Grain yield and its contributing characteristics (total number of pods, fresh pod weight, hundred seed weight, and so on) are strongly influenced, either directly or indirectly, by a variety of environmental factors. Our findings suggested that breeding may improve bambara groundnut production efficiency, and that ideally-established genotypes could be recommended for commercial cultivation in Malaysia as well as in tropical region.