sp_pcoa: Generating pcoa plot

In Tong-Chen/ImageGP: For ImageGP cloud platform (https://www.bic.ac.cn/BIC)

Generating pcoa plot

Description

Generating pcoa plot

Usage

sp_pcoa(
  data,
  metadata,
  input_type = "normalized_OTUtable",
  dissimilarity_index = "bray",
  k = 3,
  top_n = 1,
  statistical_value_type = mad,
  binary_dissimilarity_index = F,
  data_transform = "auto",
  group_variable = NULL,
  color_variable = NULL,
  color_variable_order = NULL,
  shape_variable = NULL,
  shape_variable_order = NULL,
  size_variable = NULL,
  size_variable_order = NULL,
  label_variable = NULL,
  label_variable_order = NULL,
  legend.position = "right",
  draw_ellipse = "auto",
  manual_color_vector = NULL,
  title = NULL,
  label_font_size = NULL,
  debug = FALSE,
  type = "t",
  level = 0.95,
  filename = NULL,
  extra_ggplot2_cmd = NULL,
  check_significance = T,
  check_paired_significance = T,
  coord_fixed = T,
  ...
)

Arguments

data	Data file (or data.frame) With the first row as the header line and the first column as the row-name. Columns are separated by one tab. Each row represents one variable (normally genes, OTU). Each column represents one sample. The numbers represent gene expression abundance or OTU abundance or other abundances, and should be normalized.
metadata	Metadata file (or data.frame) with sample attributes like group information. The first column is the same as the first row of value given to parameter data. These attributes would be used as color, size, shape variables in the plot. If not supplied, each sample will be treated as one group.
input_type	The input data is OTU table (normalized_OTUtable) or a distance matrix (distance_matrix).
dissimilarity_index	Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "clark", "bray" (default, meaning Bray–Curtis), "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao", "mahalanobis", "chisq" or "chord". Gower, Bray–Curtis, Jaccard and Kulczynski indices are good in detecting underlying ecological gradients (Faith et al. 1987). Morisita, Horn–Morisita, Binomial, Cao and Chao indices should be able to handle different sample sizes (Wolda 1981, Krebs 1999, Anderson & Millar 2004), and Mountford (1962) and Raup-Crick indices for presence–absence data should be able to handle unknown (and variable) sample sizes.
k	the maximum dimension of the space which the data are to be represented in; must be in \{1, 2, \ldots, n-1\}.
top_n	An integer larger than 1 will be used to get top x genes (like top 5000). A float number less than 1 will be used to get top x fraction genes (like top 0.7 of all genes).
statistical_value_type	Specify the way for statistical computation. Default mad, accept mean, var, sum, median.
binary_dissimilarity_index	Perform presence/absence standardization before computing dissimilarity_index. Default FALSE, accept TRUE.
data_transform	Methods for transforming data. Default 'auto'. Accept 'None', For auto: If the data values are larger than common abundance class scales (here is 9), the function performs a Wisconsin double standardization (wisconsin). If the values look very large, the function also performs sqrt transformation. For None: No transformation would be performed. For total: Compute relative abundance of OTUs/Genes in each sample. For hellinger: square root of method = "total". For scale: row scale. For sqrt and log2, just as the words.
group_variable	The variable for grouping points to generate normal data confidence ellipses. Optional.
label_variable	The variable for text used to label points. Optional. Specially supplying Row.names would label sample with their names.
legend.position	Position of legend, accept top, bottom, left, right, none or c(0.8,0.8).
draw_ellipse	Default 'auto' means to enclose points in a polygon if one group with less than 4 points. If there are more than 4 points for all groups, confidence ellipses would be draw. Accept ⁠confidence ellipse⁠ to draw confidence ellipses for all conditions even though they would not be draw. Accept no to remove ellipse or other polygens.
manual_color_vector	Manually set colors for each geom. Default NULL, meaning using ggplot2 default. Colors like c('red', 'blue', '#6181BD') (number of colors not matter) or a RColorBrewer color set like "BrBG" "PiYG" "PRGn" "PuOr" "RdBu" "RdGy" "RdYlBu" "RdYlGn" "Spectral" "Accent" "Dark2" "Paired" "Pastel1" "Pastel2" "Set1" "Set2" "Set3" "Blues" "BuGn" "BuPu" "GnBu" "Greens" "Greys" "Oranges" "OrRd" "PuBu" "PuBuGn" "PuRd" "Purples" "RdPu" "Reds" "YlGn" "YlGnBu" "YlOrBr" "YlOrRd" (check http://www.sthda.com/english/wiki/colors-in-r for more).
title	Title of picture.
type	The type of ellipse. The default "t" assumes a multivariate t-distribution, and "norm" assumes a multivariate normal distribution. "euclid" draws a circle with the radius equal to level, representing the euclidean distance from the center. This ellipse probably won't appear circular unless coord_fixed() is applied.
level	The level at which to draw an ellipse, or, if type="euclid", the radius of the circle to be drawn.
filename	Output picture to given file.
extra_ggplot2_cmd	Extra ggplot2 commands (currently unsupported)
check_significance	Check if the centroids and dispersion of the groups as defined by measure space are equivalent for all groups.
check_paired_significance	Paired-check for each two groups.
coord_fixed	When True (the default) ensures that one unit on the x-axis is the same length as one unit on the y-axis.
...	Parameters given to sp_ggplot_layout

Value

A ggplot2 object

Examples

## Not run:
## End(Not run)

Tong-Chen/ImageGP documentation built on April 14, 2025, 12:54 p.m.