LMSstat

Automation of statistical test with an identical data input aiming to reduce arduous work searching for packages and changing data input.

The package includes

• Simple Statistics :u-test, t-test, post hocs of Anova and Kruskal Wallis with FDR adjusted values

• Bar, Box, Dot, Violin plots with significance (u-test, t-test, post hocs of Anova and Kruskal Wallis)

• Scaling & Transformation

• Normality check (Shapiro Wilk test)

• Scheirer–Ray–Hare Test

• Volcano plot

• Heatmap

• PERMANOVA

• NMDS

• PCA

• PCoA

Instructions

Installation

Download R

https://cran.r-project.org/bin/windows/base/

Download R Studio

https://www.rstudio.com/products/rstudio/download/

Download Rtools

https://cran.r-project.org/bin/windows/Rtools/

Download package in R

install.packages("devtools")

devtools::install_github("daehwankim12/LMSstat")

devtools::install_github("daehwankim12/LMSstat", ref = "develop")

library(LMSstat)

Basic structure of the Data

Used in

• Simple statistics
• Barplot, Boxplot, Dotplot
• Volcano plot
• Scheirer–Ray–Hare Test
• PERMANOVA
• NMDS
• PCA
• Scaling & Transformation
• Normality check (Shapiro Wilk test)
• Heatmap

#Sample Data provided within the package

data("Data")

# Uploading your own Data

setwd("~")

Data <- read.csv("statT.csv", check.names = FALSE)

# When uploading an Excel file
Data <- as.data.frame(readxl::read_excel("statT.xlsx", sheet = 2))

# When your data is large
Data <- as.data.frame(data.table::fread("statT.csv", check.names = FALSE, header = TRUE, integer64 = "double"))

Datafile needs to follow the following format

statT.csv

Used in {id="used-in_1"}

• PERMANOVA

# Sample Data provided within the package
data("Classification")

# Uploading your own Data
Classification <- read.csv("statT_G.csv", header = F)

statT_G.csv

Univariate statistics

# Use `parallel = TRUE` for datasets with more than 1000 metabolites.
Statfile <- All_stats(Data,Adjust_p_value = TRUE, Adjust_method = "BH", parallel = FALSE) 

Statfile <- Allstats(Data,Adjust_p_value = TRUE, Adjust_method = "BH") # Previous version using for-loop

Adjustable parameters {id="adjustable-parameters_1"}

• Adjust_p_value = T # Set True if adjustment is needed

• Adjust_method = F # Adjustment methods frequently used. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none")

head(Statfile[["Result"]]) # includes all statistical results

write.csv(Statfile[["Result"]], "p_value_result.csv")  # Write csv with all the p-value included

install.packages("writexl")

writexl::write_xlsx(list(mysheet = Data), "p_value_result.xlsx")  # Write xlsx with all the p-value included

data.table::fwrite(as.data.frame(Statfile[["Result"]]), "p_value_result.csv", row.names = TRUE)

Plots

# Makes a subdirectory and saves box plots for all the variables
Boxplot(Statfile,asterisk = "u_test") 

# Makes a subdirectory and saves dot plots for all the variables
Dotplot(Statfile,asterisk = "t_test") 

# Makes a subdirectory and saves bar plots for all the variables
Barplot(Statfile,asterisk = "Scheffe")

# Makes a subdirectory and saves violin plots for all the variables
Violinplot(Statfile,asterisk = "Scheffe")

          Boxplot(Statfile)              Dotplot(Statfile)

          Barplot(Statfile)              Violinplot(Statfile)

Adjustable parameters {id="adjustable-parameters_2"}

• asterisk = "t_test" #c("Dunn","Scheffe","u_test","t_test")
• significant_variable_only = F # If set to TRUE, insignificant results will not be plotted
• color = c("#FF3300", "#FF6600", "#FFCC00", "#99CC00", "#0066CC", "#660099") # Colors for the plots
• legend_position = "none" # "none","left","right","bottom","top"
• order = NULL # Order of the groups c("LAC","LUE","WEI","SDF","HGH","ASH")
• tip_length = 0.01 # significance tip length
• label_size = 2.88 # significance label size
• step_increase = 0.05 #significance step increase
• width = 0.3 # box width ; size = 3 # dot size
• fig_width = NA #figure size
• fig_height = NA #figure size
• Y_text = 12 # Y title size
• X_text = 10 # X text size
• Y_lab = 10 #y axis text size
• T_size = 15 # Title size
• sig_int = c(0.1,0.05) # significance interval

Scaling & Transformation

scaled_data <- D_tran(Data, param = "Auto")

           Raw_Data                     Scaled_Data

Adjustable parameters {id="adjustable-parameters_3"}

• param = "None" # "None","Auto","log10","Pareto"

• save = F #Set true if datafile is to be saved

Normality check

#Shapiro Wilk test

Result <- Norm_test(Data)

write.csv(Result, "Normality_test_Result.csv")

Scheirer–Ray–Hare Test - do not use

# csv files including significant variables (Multilevel, Group, interaction) and a Venn diagram are downloaded
SRH(Data)

Adjustable parameters {id="adjustable-parameters_4"}

• Adjust_p_value = T # Set True if adjustment is needed
• Adjust_method = "BH" # Adjustment methods frequently used. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none")

Volcano plot

# Makes a subdirectory and saves Volcano plots for different combination of groups
Test <- All_stats(Data)
Volcano(Test, asterisk = "t-test")

Adjustable parameters {id="adjustable-parameters_5"}

• asterisk = "t-test" #statistics inheriting from Allstats "Scheffe", "t-test", "u-test", "Dunn"
• reverse = T # T, F reverse the direction of fold change
• fig_width = NA #figure size
• fig_height = NA #figure size
• FC_log = 2 # Fold change log transformation value
• pval_log = 10 #p_value log transformation value
• dotsize = 3 #dotsize
• x_limit = c(-2,2) #x axis limt
• y_limit =c(0,6) #y axis limit
• pval_intercept = 0.05 # intercept for identification
• sig_label = T # T,F label significant variables
• color=c("#FF3300","#FF6600","#FFCC00") #colors used for ggplots.
• fixed_limit = F #whether the limit should be fixed or not T, F
• max_overlap = 20 #maximum overlap for labels
• FC_range = c(-1.5,1.5) #significant fold change range

Heatmap

# Makes a subdirectory and saves Heatmap

scaled_data <- D_tran(Data, param = "Auto")

Heatmap(scaled_data) #data inheriting from D_tran

Adjustable parameters {id="adjustable-parameters_6"}

• col =c("green", "white", "red") # colors for heatmap
• col_lim = c(-3, 0, 3) # color boundaries
• reverse = T # T,F Reverse column and rows
• distance = "pearson" # Distance matrix for HCA "pearson", "manhattan","euclidean","spearman","kendall" ,
• rownames = T # T,F
• colnames = T # T,F
• Hsize = (3,6) # Width & Height c(a,b)
• g_legend = "Group" # Annotation legend title
• h_legend = "Color Key" # Heatmap legend title
• Title ="Title" # Title
• T_size = 10 # Title text size
• R_size = 3 # row text size
• C_size = 3 # column text size
• Gcol =c("ASD" = "black","HGH"="red","LAC"="blue","LUE" ="grey","SDF" = "yellow","WEI"="green") # Color for top_annotation bar
• dend_h = 0.5 #dendrite height
• a_h = 0.2 # top annotation hegiht

Multivariate statistics

PERMANOVA

data("Data")

data("Classification") 

Single factor

PERMANOVA done with the Group column

Indiv_Perm(Data) # The group information is treated as a factor

Multiple Factors

Loops PERMANOVA over different classes provided by Classification

Result <- Multi_Perm(Data, Classification) # The group information is treated as factors

Adjustable parameters {id="adjustable-parameters_7"}

• method = Dissimilarity index c("manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao", "mahalanobis", "chisq", "chord")

NMDS

# Makes a subdirectory and saves NMDS plots for all of the distance metrics
NMDS(Data, methods = c("manhattan", "bray", "euclidean"))

NMDS plot with bray distance and p-value from PERMANOVA

Adjustable parameters {id="adjustable-parameters_8"}

• methods = Dissimilarity index c("manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao", "mahalanobis", "chisq", "chord")

• color = c("#FF3300", "#FF6600", "#FFCC00", "#99CC00", "#0066CC", "#660099") # Colors for the plots

• legend_position = "none" # "none","left","right","bottom","top"

• fig_width = NA #figure size

• fig_height = NA #figure size

• names = F # used to indicate sample names

• dotsize = 3 # dotsize

• labsize = 3 # label size

PCA

# Makes a subdirectory and saves PCA plot
PCA(Data, components = c(1, 2), legend_position = "none")

PCA plot with selected components

Adjustable parameters {id="adjustable-parameters_9"}

• color = c("#FF3300", "#FF6600", "#FFCC00", "#99CC00", "#0066CC", "#660099") # Colors for the plots
• legend_position = "none" # "none","left","right","bottom","top"
• fig_width = NA #figure size
• fig_height = NA #figure size
• components = c(1,2) # selected components
• names = F # used to indicate sample names
• dotsize = 3 # dotsize
• labsize = 3 # label size
• ellipse = T # T or F to show ellipse

PCoA

# Makes a subdirectory and saves PCoA plot
PCoA(Data, components = c(1, 2), methods = c("bray", "manhattan"))

PCoA plot with selected components

Adjustable parameters {id="adjustable-parameters_10"}

• color = c("#FF3300", "#FF6600", "#FFCC00", "#99CC00", "#0066CC", "#660099") # Colors for the plots
• legend_position = "none" # "none","left","right","bottom","top"
• fig_width = NA #figure size
• fig_height = NA #figure size
• components = c(1,2) # selected components
• names = F # used to indicate sample names
• dotsize = 3 # dotsize
• labsize = 3 # label size
• ellipse = T # T or F to show ellipse
• methods = Dissimilarity index c("manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao", "mahalanobis", "chisq",chord")