Package 'sysAgNPs'

Title: Systematic Quantification of AgNPs to Unleash their Potential for Applicability
Description: There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs.
Authors: Xiting Wang [aut, cre] , Longfei Mao [aut, cph] , Jiamin Hu [ctb]
Maintainer: Xiting Wang <[email protected]>
License: GPL (>= 3)
Version: 1.0.0
Built: 2025-03-01 13:32:14 UTC
Source: https://github.com/xitingwang-ida/sysagnps

Help Index

A binary dataframe of datasets used to establish evaluation criteria.

Description

A binary dataframe of datasets used to establish evaluation criteria.

Usage

data(binary_dataset)

Format

A dataframe with 600 rows and 50 variables.

"Plant parts or microbial sites" to "Anti-inflammatory"

Subfeature of nano silver.

Nanosilver data set.

Description

Nanosilver data set.

Usage

data(dataset)

Format

A dataframe with 600 rows and 15 variables.

"Synthesis methods" to "Applications"

Features of nano silver.

Calculate Axis Path. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. Calculates x-y coordinates for a set of radial axes (one per variable being plotted in radar plot)

Description

Calculate Axis Path. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. Calculates x-y coordinates for a set of radial axes (one per variable being plotted in radar plot)

Usage

sys_CalculateAxisPath(var.names, min, max)

Arguments

var.names

list of variables to be plotted on radar plot
min

MININUM value required for the plotted axes (same value will be applied to all axes)
max

MAXIMUM value required for the plotted axes (same value will be applied to all axes)

Value

a dataframe of the calculated axis paths

Calculate Group Path. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. Converts variable values into a set of radial x-y coordinates

Description

Calculate Group Path. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. Converts variable values into a set of radial x-y coordinates

Usage

sys_CalculateGroupPath(df)

Arguments

df

a dataframe with Col 1 is group ('unique' cluster / group ID of entity) and Col 2-n are v1.value to vn.value - values (e.g. group/cluser mean or median) of variables v1 to v.n

Value

a dataframe of the calculated axis paths

Source

Code adapted from a solution posted by Tony M to https://stackoverflow.com/questions/9614433/creating-radar-chart-a-k-a-star-plot-spider-plot-using-ggplot2-in-r/.

Calculate the Combination Entropy

Description

Calculate the average value of the probability of surprising level of the presence and absence of a particular category within the specific category to measure the average uncertainty of feature categories.

Usage

sys_CE(data, dataset)

Arguments

data

A dataframe that contains experimental data.
dataset

The dataset used to to calculate the ratio of the number of reporting a certain feature in the AgNPs dataset to the total number of samples.

Value

A dataframe including: 1. the ratio of the number of reporting a certain feature in the AgNPs dataset to the total number of samples; 2. pc:the probability of the feature combination occurring; 3. Hi:the probability of surprising level of the presence and absence of feature combinations to measure the uncertainty of feature combination.

Examples

data(dataset)
users_data <- dataset
CE <- sys_CE(users_data, dataset)

Calculate the Distribution Entropy

Description

Measure the distribution variability of the presence and absence of feature categories.

Usage

sys_DE(data)

Arguments

data

A dataframe that contains experimental data.

Value

A dataframe including 1. the number of feature in a certain category; 2. the total number of features in the sample; 3. the average value to measure the average uncertainty of feature categories

Examples

data(dataset)
users_data <- dataset
DE = sys_DE(users_data)

Convert categorical variables into discrete variables

Description

Convert categorical variables into discrete variables

Usage

sys_discretize(dataset, vars_to_discretize = NULL)

Arguments

dataset

A dataframe of dataset. Datasets used to establish evaluation criteria.
vars_to_discretize

Variables or columns to be discretized. Default is NULL.

Value

A binary dataframe.

Examples

data(dataset)
dis_data <- sys_discretize(dataset, c("Shape", "pH"))

Build Transition Scores criteria

Description

This function evaluates the criteria for a binary dataset by calculating the transfer probability matrix and iterating to obtain the transfer probability vector.

Usage

sys_eval_cri(binary_dataset, n_iter, vars_to_discretize = NULL)

Arguments

binary_dataset

A binary dataframe of datasets used to establish evaluation criteria.
n_iter

The number of iterations to reach the steady state.
vars_to_discretize

Variables or columns to be discretized. Default is NULL.

Value

A dataframe containing the scores of nanomaterial features.

Examples

data(dataset)
binary_dataset <- dataset
var_dis <- c("Synthesis methods", "pH", "Temperature (℃)",
"Zeta potential (mV)","Size (nm)", "Shape", "Applications")
criteria <- sys_eval_cri(binary_dataset, 6, var_dis)

Generate circle coordinates. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. Generate coordinates to draw a circle.

Description

Generate circle coordinates. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. Generate coordinates to draw a circle.

Usage

sys_funcCircleCoords(center = c(0, 0), r = 1, npoints = 100)

Arguments

center

coordinate for centroid
r

radius
npoints

number of coordinates to generate

Value

a dataframe

Source

Adapted from Joran's response to https://stackoverflow.com/questions/6862742/draw-a-circle-with-ggplot2/.

Generate Dynamic Color Values. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. This function dynamically generates a vector of color values based on the number of groups. It uses RColorBrewer for smaller sets of groups and generates a gradient for larger sets.

Description

Generate Dynamic Color Values. This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/. This function dynamically generates a vector of color values based on the number of groups. It uses RColorBrewer for smaller sets of groups and generates a gradient for larger sets.

Usage

sys_generate_color_values(num_groups)

Arguments

num_groups

The number of groups for which to generate color values.

Value

A character vector of color values.

Examples

sys_generate_color_values(5)
sys_generate_color_values(20)

This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/.

Description

This function is derived from the 'ggradar' package.https://github.com/ricardo-bion/ggradar/.

Usage

sys_ggradar(
  plot.data,
  base.size = 15,
  font.radar = "sans",
  values.radar = c("0%", "50%", "100%"),
  axis.labels = colnames(plot.data)[-1],
  grid.min = 0,
  grid.mid = 0.5,
  grid.max = 1,
  centre.y = grid.min - ((1/9) * (grid.max - grid.min)),
  plot.extent.x.sf = 1,
  plot.extent.y.sf = 1.2,
  x.centre.range = 0.02 * (grid.max - centre.y),
  label.centre.y = FALSE,
  grid.line.width = 0.5,
  gridline.min.linetype = "longdash",
  gridline.mid.linetype = "longdash",
  gridline.max.linetype = "longdash",
  gridline.min.colour = "grey",
  gridline.mid.colour = "#007A87",
  gridline.max.colour = "grey",
  grid.label.size = 6,
  gridline.label.offset = -0.1 * (grid.max - centre.y),
  label.gridline.min = TRUE,
  label.gridline.mid = TRUE,
  label.gridline.max = TRUE,
  axis.label.offset = 1.15,
  axis.label.size = 5,
  axis.line.colour = "grey",
  group.line.width = 1.5,
  group.point.size = 6,
  group.colours = NULL,
  background.circle.colour = "#D7D6D1",
  background.circle.transparency = 0.2,
  plot.legend = if (nrow(plot.data) > 1) TRUE else FALSE,
  legend.title = "",
  plot.title = "",
  legend.text.size = 14,
  legend.position = "left",
  fill = FALSE,
  fill.alpha = 0.5,
  draw.points = TRUE,
  point.alpha = 1,
  line.alpha = 1
)

Arguments

plot.data

dataframe comprising one row per group
base.size

text size
font.radar

text font family
values.radar

values to print at minimum, 'average', and maximum gridlines
axis.labels

names of axis labels if other than column names supplied via plot.data
grid.min

value at which mininum grid line is plotted
grid.mid

value at which 'average' grid line is plotted
grid.max

value at which maximum grid line is plotted
centre.y

value of y at centre of plot
plot.extent.x.sf

controls relative size of plot horizontally
plot.extent.y.sf

controls relative size of plot vertically
x.centre.range

controls axis label alignment
label.centre.y

whether value of y at centre of plot should be labelled
grid.line.width

width of gridline
gridline.min.linetype

line type of minimum gridline
gridline.mid.linetype

line type of 'average' gridline
gridline.max.linetype

line type of maximum gridline
gridline.min.colour

colour of minimum gridline
gridline.mid.colour

colour of 'average' gridline
gridline.max.colour

colour of maximum gridline
grid.label.size

text size of gridline label
gridline.label.offset

displacement to left/right of central vertical axis
label.gridline.min

whether or not to label the mininum gridline
label.gridline.mid

whether or not to label the 'mininum'average' gridline
label.gridline.max

whether or not to label the maximum gridline
axis.label.offset

vertical displacement of axis labels from maximum grid line, measured relative to circle diameter
axis.label.size

text size of axis label
axis.line.colour

colour of axis line
group.line.width

line width of group
group.point.size

point size of group
group.colours

colour of group
background.circle.colour

colour of background circle/radar
background.circle.transparency

transparency of background circle/radar
plot.legend

whether to include a plot legend
legend.title

title of legend
plot.title

title of radar plot
legend.text.size

text size in legend
legend.position

position of legend, valid values are "top", "right", "bottom", "left"
fill

whether to fill polygons
fill.alpha

if filling polygons, transparency values
draw.points

whether to draw points
point.alpha

alpha for points, can be a single value or vector
line.alpha

alpha for lines, can be a single value or vector

Value

a ggplot object

Source

Most of the code is from http://rstudio-pubs-static.s3.amazonaws.com/5795_e6e6411731bb4f1b9cc7eb49499c2082.html.

Obtain the transition probability of each iteration

Description

Loop "n_iter" times to obtain the transition probability of each iteration.

Usage

sys_iter(binary_dataset, n_iter, vars_to_discretize = NULL)

Arguments

binary_dataset

A binary dataframe of datasets used to establish evaluation criteria.
n_iter

The number of iterations to reach the steady state.
vars_to_discretize

Variables or columns to be discretized. Default id NULL.

Value

A dataframe containing the number of iterations and the transition probability of each iteration.

Examples

data(dataset)
iter_prob <- sys_iter(dataset, 6, c("Shape", "pH"))

Line and Radar Plot of sysAgNPs score

Description

Line and Radar Plot of sysAgNPs score

Usage

sys_line_radar(sysAgNPs_score, num_plots)

Arguments

sysAgNPs_score

A dataframe containing four columns of numeric data.
num_plots

The range of the graph to be output and saved can be a vector or a single value.

Value

A ggplot object.

Calculate the Proclivity Entropy

Description

Measure the preference of feature categories.

Usage

sys_PE(data)

Arguments

data

A dataframe that contains experimental data.

Value

A dataframe including 1. the number of feature in a certain category; 2. the total number of features in the sample; 3. the expected value to measure the average description level across different feature categories.

Examples

data(dataset)
users_data <- dataset
PE = sys_PE(users_data)

Iterate to obtain the steady state probability

Description

Change the values of the constraints step by step and record the number of iterations to reach the steady state.

Usage

sys_steady(
  binary_dataset,
  tran_matrix,
  tol_vec = c(0.01, 0.001, 1e-04, 1e-05, 1e-06, 1e-07)
)

Arguments

binary_dataset

A binary dataframe of datasets used to establish evaluation criteria.
tran_matrix

A transfer probability matrix.
tol_vec

A smaller constants used as constraints.

Value

A data frame containing the constraints and the number of iterations to reach the steady state.

Examples

data(binary_dataset)
data(tran_matrix)
tol_iter <- sys_steady(binary_dataset, tran_matrix, 1e-5)

Calculate transition probability matrix

Description

Calculate transition probability matrix

Usage

sys_tran(binary_dataset)

Arguments

binary_dataset

A binary dataframe of datasets used to establish Transition Scores criteria.

Value

A transfer probability matrix.

Examples

data(binary_dataset)
tran_matrix <- sys_tran(binary_dataset)

Calculate the Transition Scores

Description

Calculate the Transition Scores

Usage

sys_TS(data, dataset, n_iter, vars_to_discretize)

Arguments

data

A dataframe that contains experimental data.
dataset

A binary dataframe. Datasets used to establish evaluation criteria.
n_iter

The number of iterations to reach the steady state.
vars_to_discretize

Variables or columns to be discretized. Default is NULL.

Value

A dataframe that contains sysAgNPs scores.

sysAgNPs package application results in four evaluation scores.

Description

sysAgNPs package application results in four evaluation scores.

Usage

data(sysAgNPs_score)

Format

A dataframe with 600 rows and 4 variables.

DE to TS

Four evaluation scores.

A transfer probability matrix.

Description

A transfer probability matrix.

Usage

data(tran_matrix)

Format

A matrix with 50 rows and 50 columns.

V1 to V50

Subfeature of nano silver.