if (!require("BiocManager",quietly =TRUE)) {install.packages("BiocManager") }if (!require("pvca",quietly =TRUE)) { BiocManager::install("pvca") }

install.packages("omicsTools")#> Installing package into 'C:/Users/bach/AppData/Local/Temp/RtmpqQj0Pu/temp_libpath245079296a59'#> (as 'lib' is unspecified)#> package 'omicsTools' successfully unpacked and MD5 sums checked#>#> The downloaded binary packages are in#>  C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\downloaded_packages

if (!require("devtools",quietly =TRUE))install.packages("devtools")#>#> Attaching package: 'devtools'#> The following object is masked from 'package:BiocManager':#>#>     install

devtools::install_github("omicsTools")#> Using GitHub PAT from the git credential store.#> Downloading GitHub repo omicsTools@HEAD#> rlang  (1.1.3  -> 1.1.4 ) [CRAN]#> cli    (3.6.2  -> 3.6.3 ) [CRAN]#> digest (0.6.35 -> 0.6.36) [CRAN]#> fresh  (0.2.0  -> 0.2.1 ) [CRAN]#> dbscan (1.1-12 -> 1.2-0 ) [CRAN]#> Installing 5 packages: rlang, cli, digest, fresh, dbscan#> Installing packages into 'C:/Users/bach/AppData/Local/Temp/RtmpqQj0Pu/temp_libpath245079296a59'#> (as 'lib' is unspecified)#> package 'rlang' successfully unpacked and MD5 sums checked#> package 'cli' successfully unpacked and MD5 sums checked#> package 'digest' successfully unpacked and MD5 sums checked#> package 'fresh' successfully unpacked and MD5 sums checked#> package 'dbscan' successfully unpacked and MD5 sums checked#>#> The downloaded binary packages are in#>  C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\downloaded_packages#> ── R CMD build ─────────────────────────────────────────────────────────────────#>          checking for file 'C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\remotes53285a4b5044\omicsTools-baa6316/DESCRIPTION' ...  ✔  checking for file 'C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\remotes53285a4b5044\omicsTools-baa6316/DESCRIPTION'#>       ─  preparing 'omicsTools': (337ms)#>    checking DESCRIPTION meta-information ...     checking DESCRIPTION meta-information ...   ✔  checking DESCRIPTION meta-information#>       ─  checking for LF line-endings in source and make files and shell scripts#>       ─  checking for empty or unneeded directories#>      Omitted 'LazyData' from DESCRIPTION#>       ─  building 'omicsTools_1.1.3.tar.gz'#>#>#> Installing package into 'C:/Users/bach/AppData/Local/Temp/RtmpqQj0Pu/temp_libpath245079296a59'#> (as 'lib' is unspecified)

# Load the CSV datadata_file<-system.file("extdata","example1.csv",package ="omicsTools")data<- readr::read_csv(data_file)#> Rows: 85 Columns: 482#> ── Column specification ────────────────────────────────────────────────────────#> Delimiter: ","#> chr   (1): Sample#> dbl (414): Urea_pos, Lipoamide_pos, AcetylAmino Sugars_pos, Glycerophosphoch...#> lgl  (67): DBQ_pos.IS, Aminolevulinic Acid_pos, Leucine_pos, Homocystine_pos...#>#> ℹ Use `spec()` to retrieve the full column specification for this data.#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Apply the impute functionimputed_data<- omicsTools::handle_missing_values(data)#> Registered S3 method overwritten by 'GGally':#>   method from#>   +.gg   ggplot2#> ℹ Starting missing value handling... 🚀#> ℹ Calculating missing value percentages... ⏳#> ✔ 446 features removed due to missing values exceeding the threshold. ✅#> ℹ Imputing missing values using method: mean 🔧#> ✔ Missing value handling completed! 🎉

# Write the imputed data to a new CSV filereadr::write_csv(imputed_data,paste0(tempdir(),"/imputed_data.csv"))

# Load the CSV datadata_file<-system.file("extdata","example2.csv",package ="omicsTools")data<- readr::read_csv(data_file)#> Rows: 63 Columns: 202#> ── Column specification ────────────────────────────────────────────────────────#> Delimiter: ","#> chr   (1): Sample#> dbl (201): Urea_pos, Lipoamide_pos, Glycerophosphocholine_pos, Allanoate_pos...#>#> ℹ Use `spec()` to retrieve the full column specification for this data.#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Apply the normalize functionnormalized_data<- omicsTools::qc_normalize(data)# Write the normalized data to a new CSV filereadr::write_csv(normalized_data,paste0(tempdir(),"/normalized_data.csv"))

# Using generated datalibrary(omicsTools)#>#> This is omicsTools version 1.1.3.#> omicsTools is free software and comes with ABSOLUTELY NO WARRANTY.#> Please use at your own risk.

omics_data<-createOmicsData()#>#> ── Generating High-Dimensional Data with Anomalies ─────────────────────────────#> ℹ Introducing anomalies in specific features#> ✔ Data generation complete

# Define custom thresholdscustom_thresholds<-define_thresholds(skewness =3,kurtosis =8)# Update the OmicsData object with custom thresholdsomics_data@thresholds<- custom_thresholds# Calculate measures for each featureomics_data<-calculate_measures(omics_data)#>#> ── Calculating Measures for Each Feature ───────────────────────────────────────#> Calculating [===>------------------------------------------------]   8% in  0sCalculating [===>------------------------------------------------]   9% in  0sCalculating [====>-----------------------------------------------]   9% in  0sCalculating [====>-----------------------------------------------]  10% in  0sCalculating [====>-----------------------------------------------]  11% in  0sCalculating [=====>----------------------------------------------]  11% in  0sCalculating [=====>----------------------------------------------]  12% in  0sCalculating [======>---------------------------------------------]  13% in  0sCalculating 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[=================================================>--]  96% in  3sCalculating [=================================================>--]  97% in  3sCalculating [==================================================>-]  97% in  3sCalculating [==================================================>-]  98% in  3sCalculating [==================================================>-]  99% in  3sCalculating [===================================================>]  99% in  3sCalculating [===================================================>] 100% in  3sCalculating [====================================================] 100% in  3s#> ✔ Measures calculation complete

# Flag anomaliesomics_data<-flag_anomalies(omics_data)#>#> ── Flagging Anomalies ──────────────────────────────────────────────────────────#> ✔ Anomaly flagging complete

# Plotting distribution measuresplot_distribution_measures(omics_data)#>#> ── Plotting Distribution Measures ──────────────────────────────────────────────#> ✔ Distribution measures plot complete

# Plotting sample measuresplot_sample_measures(omics_data)#>#> ── Plotting Sample Measures ────────────────────────────────────────────────────#> ✔ Sample measures plot complete

# Prepare data for UpSet plotupset_data<-prepare_upset_data(omics_data)#>#> ── Preparing Data for UpSet Plot ───────────────────────────────────────────────#> ✔ Data preparation for UpSet plot complete

# Convert to binary matrix for UpSetRupset_matrix<-convert_to_binary_matrix(upset_data)#>#> ── Converting Data to Binary Matrix for UpSetR ─────────────────────────────────#> ✔ Data conversion to binary matrix complete

# Ensure there are at least two columns for the UpSet plotensure_enough_sets_for_upset(upset_matrix)#>#> ── Checking Data for UpSet Plot ────────────────────────────────────────────────#> ✔ Enough sets for UpSet plot

library(omicsTools)# Assuming user_data is a data frame where rows are samples and columns are featuresuser_data<-generate_data_with_anomalies(n_samples =100,n_features =200)# Replace with actual data loading if available#>#> ── Generating High-Dimensional Data with Anomalies ─────────────────────────────#> ℹ Introducing anomalies in specific features#> ✔ Data generation complete

omics_data_user<-createOmicsData(data = user_data)# Define custom thresholdscustom_thresholds_user<-define_thresholds(skewness =4,kurtosis =6,shapiro_p =1e-5,cooks_distance =0.5,lof =10)# Update the OmicsData object with custom thresholdsomics_data_user@thresholds<- custom_thresholds_user# Calculate measures for each featureomics_data_user<-calculate_measures(omics_data_user)#>#> ── Calculating Measures for Each Feature ───────────────────────────────────────#> Calculating [=====================================>--------------]  74% in  0sCalculating [======================================>-------------]  74% in  0sCalculating [======================================>-------------]  75% in  0sCalculating [======================================>-------------]  76% in  0sCalculating [=======================================>------------]  76% in  0sCalculating [=======================================>------------]  77% in  0sCalculating [=======================================>------------]  78% in  0sCalculating [========================================>-----------]  78% in  0sCalculating [========================================>-----------]  79% in  0sCalculating [========================================>-----------]  80% in  0sCalculating [=========================================>----------]  80% in  0sCalculating [=========================================>----------]  81% in  0sCalculating [=========================================>----------]  82% in  0sCalculating [==========================================>---------]  82% in  0sCalculating [==========================================>---------]  83% in  0sCalculating [==========================================>---------]  84% in  0sCalculating [===========================================>--------]  84% in  0sCalculating [===========================================>--------]  85% in  0sCalculating [===========================================>--------]  86% in  0sCalculating [============================================>-------]  86% in  0sCalculating [============================================>-------]  87% in  0sCalculating [=============================================>------]  88% in  0sCalculating [=============================================>------]  89% in  0sCalculating [==============================================>-----]  90% in  0sCalculating [==============================================>-----]  91% in  0sCalculating [===============================================>----]  92% in  0sCalculating [===============================================>----]  93% in  0sCalculating [================================================>---]  94% in  0sCalculating [================================================>---]  95% in  0sCalculating [=================================================>--]  96% in  0sCalculating [=================================================>--]  97% in  0sCalculating [==================================================>-]  98% in  0sCalculating [==================================================>-]  99% in  0sCalculating [===================================================>] 100% in  0sCalculating [====================================================] 100% in  0s#> ✔ Measures calculation complete

# Flag anomaliesomics_data_user<-flag_anomalies(omics_data_user)#>#> ── Flagging Anomalies ──────────────────────────────────────────────────────────#> ✔ Anomaly flagging complete

# Plotting distribution measuresplot_distribution_measures(omics_data_user)#>#> ── Plotting Distribution Measures ──────────────────────────────────────────────#> ✔ Distribution measures plot complete

# Plotting sample measuresplot_sample_measures(omics_data_user)#>#> ── Plotting Sample Measures ────────────────────────────────────────────────────#> ✔ Sample measures plot complete

# Prepare data for UpSet plotupset_data_user<-prepare_upset_data(omics_data_user)#>#> ── Preparing Data for UpSet Plot ───────────────────────────────────────────────#> ✔ Data preparation for UpSet plot complete

# Convert to binary matrix for UpSetRupset_matrix_user<-convert_to_binary_matrix(upset_data_user)#>#> ── Converting Data to Binary Matrix for UpSetR ─────────────────────────────────#> ✔ Data conversion to binary matrix complete

# Ensure there are at least two columns for the UpSet plotensure_enough_sets_for_upset(upset_matrix_user)#>#> ── Checking Data for UpSet Plot ────────────────────────────────────────────────#> ✔ Enough sets for UpSet plot