From aa5cc2bff256ef18a547487c0b50f825ea1cad6e Mon Sep 17 00:00:00 2001 From: danymukesha <45208254+danymukesha@users.noreply.github.com> Date: Tue, 21 May 2024 10:31:20 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20danymuke?= =?UTF-8?q?sha/BioGA@a73bcf22f747b0f3201e68317cea380b1004da8a=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- apple-touch-icon-120x120.png | Bin 8538 -> 8538 bytes apple-touch-icon-152x152.png | Bin 11447 -> 11447 bytes apple-touch-icon-180x180.png | Bin 14195 -> 14195 bytes apple-touch-icon-60x60.png | Bin 3631 -> 3631 bytes apple-touch-icon-76x76.png | Bin 4776 -> 4776 bytes apple-touch-icon.png | Bin 14195 -> 14195 bytes articles/Introduction.html | 2 +- favicon-16x16.png | Bin 1056 -> 1056 bytes favicon-32x32.png | Bin 1922 -> 1922 bytes index.html | 8 ++++---- pkgdown.yml | 2 +- search.json | 2 +- 12 files changed, 7 insertions(+), 7 deletions(-) diff --git a/apple-touch-icon-120x120.png b/apple-touch-icon-120x120.png index 6b39671ac370a13458d729c500776c881ce48546..6c5c1f4f15aedc05a6903acb46f6785c0b2dc016 100644 GIT binary patch delta 57 zcmccRbjxW%4V#?aoF{8SHa2N0@EaRgnV4xC7+4t?e4lc3<>XxoO2}euCu>C~zgEZx E0BODye*gdg delta 57 zcmccRbjxW%4I7(w-qZt48=Ev0_zldgOpLV+46FO3>+)stYlVCO DG!7D( diff --git a/apple-touch-icon-152x152.png b/apple-touch-icon-152x152.png index bf83d485ba53b3d918404a3dba51b56c29f5e27e..27b066ba2c7ac942c5761e71a4c68f5c0f984b6e 100644 GIT binary patch delta 57 zcmdlUxjk}14V#?)>&J5rZER}O<~KI5GBMXSFt9Q(2z-1_cCxsR60(@WzOsdrJ#_K` DZQ&D& delta 57 zcmdlUxjk}14I7)zhmWC(8=Km+`3=mhOiZ*546FB#$Of0kw46F)XHYHa3+D@*5jinV4!D7+4t?9ILr+GMPh230Z8rVtU79OQC!K DI{Okl delta 57 zcmZ3Xxy;G2aDH#Rw$@f#ahnOJHY7+4t?Fy7hfF!{2X60%r9Bk!NdEav$D DWc(9R delta 57 zcmeyI_c?Du4I7)D+Genetic Algorithm Optimizationformat(elapsed_time, units = "secs"), " " ) } -#> Generation: 1 - Elapsed Time: 0.009374619 secs Generation: 2 - Elapsed Time: 0.01133275 secs Generation: 3 - Elapsed Time: 0.01151824 secs Generation: 4 - Elapsed Time: 0.01168966 secs Generation: 5 - Elapsed Time: 0.01185751 secs Generation: 6 - Elapsed Time: 0.01202178 secs Generation: 7 - Elapsed Time: 0.01219082 secs Generation: 8 - Elapsed Time: 0.0123558 secs Generation: 9 - Elapsed Time: 0.01252437 secs Generation: 10 - Elapsed Time: 0.01269555 secs Generation: 11 - Elapsed Time: 0.01290107 secs Generation: 12 - Elapsed Time: 0.01307988 secs Generation: 13 - Elapsed Time: 0.01324534 secs Generation: 14 - Elapsed Time: 0.01341176 secs Generation: 15 - Elapsed Time: 0.01357484 secs Generation: 16 - Elapsed Time: 0.01373816 secs Generation: 17 - Elapsed Time: 0.01389885 secs Generation: 18 - Elapsed Time: 0.01406193 secs Generation: 19 - Elapsed Time: 0.01422501 secs +#> Generation: 1 - Elapsed Time: 0.009090424 secs Generation: 2 - Elapsed Time: 0.01087737 secs Generation: 3 - Elapsed Time: 0.0110631 secs Generation: 4 - Elapsed Time: 0.01123357 secs Generation: 5 - Elapsed Time: 0.0114007 secs Generation: 6 - Elapsed Time: 0.01156545 secs Generation: 7 - Elapsed Time: 0.01175046 secs Generation: 8 - Elapsed Time: 0.01192689 secs Generation: 9 - Elapsed Time: 0.01209474 secs Generation: 10 - Elapsed Time: 0.01225996 secs Generation: 11 - Elapsed Time: 0.01242828 secs Generation: 12 - Elapsed Time: 0.01259375 secs Generation: 13 - Elapsed Time: 0.01275945 secs Generation: 14 - Elapsed Time: 0.01295376 secs Generation: 15 - Elapsed Time: 0.01312971 secs Generation: 16 - Elapsed Time: 0.01329684 secs Generation: 17 - Elapsed Time: 0.01346183 secs Generation: 18 - Elapsed Time: 0.01362658 secs Generation: 19 - Elapsed Time: 0.01378894 secs

Fitness Calculation diff --git a/favicon-16x16.png b/favicon-16x16.png index 04b4d1ba9d8938e283b9b11e52e37a2c7402685e..2b9d26fea6992bce07ea4a058f588b4a47867d95 100644 GIT binary patch delta 58 zcmZ3$v4CSkE)$!asaRv`j>*ML|M-m!tW1ry4GgRd3^LhXO_+KP< delta 57 zcmZqTZ{nX&!^UP@`!nLo#->0vegiWrQ$uY711kfAtkk#vCqH6SLKc&@2z8z;%$^Sb DAgmE> diff --git a/index.html b/index.html index 387eff1..95d13f8 100644 --- a/index.html +++ b/index.html @@ -106,7 +106,7 @@

Installation#> ggplot2 (3.5.0 -> 3.5.1 ) [CRAN] #> Skipping 17 packages ahead of CRAN: BiocGenerics, graph, S4Arrays, IRanges, S4Vectors, MatrixGenerics, GenomeInfoDbData, zlibbioc, XVector, GenomeInfoDb, RBGL, Biobase, DelayedArray, GenomicRanges, BiocStyle, biocViews, SummarizedExperiment #> Installing 16 packages: fs, fastmap, cachem, xfun, tinytex, knitr, htmltools, bslib, rmarkdown, matrixStats, munsell, farver, BiocManager, bookdown, gtable, ggplot2 -#> Installing packages into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath8488528329e2' +#> Installing packages into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath848868d23488' #> (as 'lib' is unspecified) #> Warning: unable to access index for repository https://bioconductor.org/packages/3.17/data/annotation/bin/windows/contrib/4.3: #> cannot open URL 'https://bioconductor.org/packages/3.17/data/annotation/bin/windows/contrib/4.3/PACKAGES' @@ -132,9 +132,9 @@

Installation#> package 'ggplot2' successfully unpacked and MD5 sums checked #> #> The downloaded binary packages are in -#> C:\Users\dany.mukesha\AppData\Local\Temp\RtmpWuKbDY\downloaded_packages +#> C:\Users\dany.mukesha\AppData\Local\Temp\RtmpArcJMj\downloaded_packages #> ── R CMD build ───────────────────────────────────────────────────────────────── -#> ✔ checking for file 'C:\Users\dany.mukesha\AppData\Local\Temp\RtmpWuKbDY\remotes99a020a46bb9\danymukesha-BioGA-9b9a1cc/DESCRIPTION' (776ms) +#> checking for file 'C:\Users\dany.mukesha\AppData\Local\Temp\RtmpArcJMj\remotes15e82c92423\danymukesha-BioGA-23ecb91/DESCRIPTION' ... ✔ checking for file 'C:\Users\dany.mukesha\AppData\Local\Temp\RtmpArcJMj\remotes15e82c92423\danymukesha-BioGA-23ecb91/DESCRIPTION' (343ms) #> ─ preparing 'BioGA': #> checking DESCRIPTION meta-information ... checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information #> ─ cleaning src @@ -144,7 +144,7 @@

Installation#> ─ building 'BioGA_0.99.5.tar.gz' #> #> -#> Installing package into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath8488528329e2' +#> Installing package into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath848868d23488' #> (as 'lib' is unspecified)

diff --git a/pkgdown.yml b/pkgdown.yml index 7f1d977..d3ed302 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.0.9 pkgdown_sha: ~ articles: Introduction: Introduction.html -last_built: 2024-05-21T10:06Z +last_built: 2024-05-21T10:30Z urls: reference: https://danymukesha.github.io/BioGA/reference article: https://danymukesha.github.io/BioGA/articles diff --git a/search.json b/search.json index 1406eb6..f738f84 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://danymukesha.github.io/BioGA/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 Dany Mukesha Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"installation","dir":"Articles","previous_headings":"Getting Started","what":"Installation","title":"Introduction","text":"install package, start R (version “4.4”) enter: can also install package directly GitHub using devtools package: vignette, illustrate usage BioGA genetic algorithm optimization context high throughput genomic data analysis. showcase interoperability Bioconductor classes, demonstrating genetic algorithm optimization can seamlessly integrated existing genomics pipelines improved analysis interpretation. BioGA package provides set functions genetic algorithm optimization tailored analyzing high throughput genomic data. vignette demonstrates usage BioGA context selecting best combination genes predicting certain trait, disease susceptibility.","code":"if (!require(\"BiocManager\", quietly = TRUE)) install.packages(\"BiocManager\") BiocManager::install(\"BioGA\") devtools::install_github(\"danymukesha/BioGA\")"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"overview","dir":"Articles","previous_headings":"Getting Started","what":"Overview","title":"Introduction","text":"Genomic data refers genetic information stored organism’s DNA. includes sequence nucleotides (adenine, thymine, cytosine, guanine) make DNA molecules. Genomic data can provide valuable insights various biological processes, gene expression, genetic variation, evolutionary relationships. Genomic data context consist gene expression profiles measured across different individuals (e.g., patients). row genomic_data matrix represents gene, column represents patient sample. values matrix represent expression levels gene patient sample. ’s example genomic data: example, row represents gene (genomic feature), column represents sample. values matrix represent measurement gene expression, mRNA levels protein abundance, sample. instance, value 0.1 Sample 1 Gene1 indicates expression level Gene1 Sample 1. Similarly, value 2.2 Sample 2 Gene3 indicates expression level Gene3 Sample 2. Genomic data can used various analyses, including genetic association studies, gene expression analysis, comparative genomics. context evaluate_fitness_cpp function, genomic data used calculate fitness scores individuals population, typically context genetic algorithm optimization. population represents set candidate combinations genes predictive trait. individual population represented binary vector indicating presence absence gene. example, individual population might represented [1, 0, 1], indicating presence Gene1 Gene3 absence Gene2. population undergoes genetic algorithm operations selection, crossover, mutation, replacement evolve towards individuals higher predictive power trait.","code":"Sample 1 Sample 2 Sample 3 Sample 4 Gene1 0.1 0.2 0.3 0.4 Gene2 1.2 1.3 1.4 1.5 Gene3 2.3 2.2 2.1 2.0"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"example-scenario","dir":"Articles","previous_headings":"Getting Started","what":"Example Scenario","title":"Introduction","text":"Consider example scenario using genetic algorithm optimization select best combination genes predicting certain trait, disease susceptibility. example, counts matrix representing counts gene expression levels across different samples. row corresponds gene, column corresponds sample. use SummarizedExperiment class store data, common Bioconductor class representing rectangular feature x sample data, RNAseq count matrices microarray data.","code":"# Load necessary packages library(BioGA) library(SummarizedExperiment) #> Loading required package: MatrixGenerics #> Loading required package: matrixStats #> #> Attaching package: 'MatrixGenerics' #> The following objects are masked from 'package:matrixStats': #> #> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, #> colCounts, colCummaxs, colCummins, colCumprods, colCumsums, #> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, #> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, #> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, #> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, #> colWeightedMeans, colWeightedMedians, colWeightedSds, #> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, #> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, #> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, #> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, #> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, #> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, #> rowWeightedMads, rowWeightedMeans, rowWeightedMedians, #> rowWeightedSds, rowWeightedVars #> Loading required package: GenomicRanges #> Loading required package: stats4 #> Loading required package: BiocGenerics #> #> Attaching package: 'BiocGenerics' #> The following objects are masked from 'package:stats': #> #> IQR, mad, sd, var, xtabs #> The following objects are masked from 'package:base': #> #> anyDuplicated, aperm, append, as.data.frame, basename, cbind, #> colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, #> get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, #> match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, #> Position, rank, rbind, Reduce, rownames, sapply, setdiff, table, #> tapply, union, unique, unsplit, which.max, which.min #> Loading required package: S4Vectors #> #> Attaching package: 'S4Vectors' #> The following object is masked from 'package:utils': #> #> findMatches #> The following objects are masked from 'package:base': #> #> expand.grid, I, unname #> Loading required package: IRanges #> Loading required package: GenomeInfoDb #> Loading required package: Biobase #> Welcome to Bioconductor #> #> Vignettes contain introductory material; view with #> 'browseVignettes()'. To cite Bioconductor, see #> 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'. #> #> Attaching package: 'Biobase' #> The following object is masked from 'package:MatrixGenerics': #> #> rowMedians #> The following objects are masked from 'package:matrixStats': #> #> anyMissing, rowMedians # Define parameters num_genes <- 1000 num_samples <- 10 # Define parameters for genetic algorithm population_size <- 100 generations <- 20 mutation_rate <- 0.1 # Generate example genomic data using SummarizedExperiment counts <- matrix(rpois(num_genes * num_samples, lambda = 10), nrow = num_genes ) rownames(counts) <- paste0(\"Gene\", 1:num_genes) colnames(counts) <- paste0(\"Sample\", 1:num_samples) # Create SummarizedExperiment object se <- SummarizedExperiment::SummarizedExperiment(assays = list(counts = counts)) # Convert SummarizedExperiment to matrix for compatibility with BioGA package genomic_data <- assay(se) head(genomic_data) #> Sample1 Sample2 Sample3 Sample4 Sample5 Sample6 Sample7 Sample8 Sample9 #> Gene1 5 11 6 14 9 14 4 12 8 #> Gene2 6 10 3 10 11 6 6 8 10 #> Gene3 9 12 9 9 12 10 12 7 10 #> Gene4 11 11 7 13 13 6 11 16 6 #> Gene5 13 12 8 7 9 9 14 10 9 #> Gene6 4 10 11 7 9 13 7 16 11 #> Sample10 #> Gene1 12 #> Gene2 10 #> Gene3 10 #> Gene4 11 #> Gene5 12 #> Gene6 8"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"initialization","dir":"Articles","previous_headings":"Getting Started","what":"Initialization","title":"Introduction","text":"population represents set candidate combinations genes predictive trait. individual population represented binary vector indicating presence absence gene. example, individual population might represented [1, 0, 1], indicating presence Gene1 Gene3 absence Gene2. population undergoes genetic algorithm operations selection, crossover, mutation, replacement evolve towards individuals higher predictive power trait.","code":"# Initialize population (select the number of canditate you wish `population`) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5 )"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"genetic-algorithm-optimization","dir":"Articles","previous_headings":"Getting Started","what":"Genetic Algorithm Optimization","title":"Introduction","text":"","code":"# Initialize fitness history fitness_history <- list() # Initialize time progress start_time <- Sys.time() # Run genetic algorithm optimization generation <- 0 while (TRUE) { generation <- generation + 1 # Evaluate fitness fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) fitness_history[[generation]] <- fitness # Check termination condition if (generation == generations) { # defined number of generations break } # Selection selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2 ) # Crossover and Mutation offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2) # (no mutation in this example) mutated_offspring <- BioGA::mutation_cpp(offspring, mutation_rate = 0) # Replacement population <- BioGA::replacement_cpp(population, mutated_offspring, num_to_replace = 1 ) # Calculate time progress elapsed_time <- difftime(Sys.time(), start_time, units = \"secs\") # Print time progress cat( \"\\rGeneration:\", generation, \"- Elapsed Time:\", format(elapsed_time, units = \"secs\"), \" \" ) } #> Generation: 1 - Elapsed Time: 0.009374619 secs Generation: 2 - Elapsed Time: 0.01133275 secs Generation: 3 - Elapsed Time: 0.01151824 secs Generation: 4 - Elapsed Time: 0.01168966 secs Generation: 5 - Elapsed Time: 0.01185751 secs Generation: 6 - Elapsed Time: 0.01202178 secs Generation: 7 - Elapsed Time: 0.01219082 secs Generation: 8 - Elapsed Time: 0.0123558 secs Generation: 9 - Elapsed Time: 0.01252437 secs Generation: 10 - Elapsed Time: 0.01269555 secs Generation: 11 - Elapsed Time: 0.01290107 secs Generation: 12 - Elapsed Time: 0.01307988 secs Generation: 13 - Elapsed Time: 0.01324534 secs Generation: 14 - Elapsed Time: 0.01341176 secs Generation: 15 - Elapsed Time: 0.01357484 secs Generation: 16 - Elapsed Time: 0.01373816 secs Generation: 17 - Elapsed Time: 0.01389885 secs Generation: 18 - Elapsed Time: 0.01406193 secs Generation: 19 - Elapsed Time: 0.01422501 secs"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"fitness-calculation","dir":"Articles","previous_headings":"Getting Started","what":"Fitness Calculation","title":"Introduction","text":"fitness calculation described provided code calculates measure dissimilarity gene expression profiles individuals population genomic data. measure dissimilarity, “fitness”, quantifies well gene expression profile individual matches genomic data. Mathematically, fitness calculation can represented follows: Let: \\(g_{ijk}\\) gene expression level gene \\(j\\) individual \\(\\) sample \\(k\\) genomic data. \\(p_{ij}\\) gene expression level gene \\(j\\) individual \\(\\) population. \\(N\\) number individuals population. \\(G\\) number genes. \\(S\\) number samples. , fitness \\(F_i\\) individual \\(\\) population can calculated sum squared differences gene expression levels individual \\(\\) corresponding gene expression levels genomic data, across genes samples: \\[ F_i = \\sum_{j=1}^{G} \\sum_{k=1}^{S} (g_{ijk} - p_{ij})^2 \\] fitness calculation aims minimize overall dissimilarity gene expression profiles individuals population genomic data. Individuals lower fitness scores considered gene expression profiles similar genomic data therefore likely selected optimization genetic algorithm. showcases integration genetic algorithms genomic data analysis highlights potential genetic algorithms feature selection genomics. ’s BioGA work context high throughput genomic data analysis: Problem Definition: BioGA starts clear definition problem solved. include tasks identifying genetic markers associated particular disease, optimizing gene expression patterns, clustering genomic data identify patterns groupings. Representation: Genomic data need appropriately represented use within genetic algorithm framework. might involve encoding data suitable format, binary strings representing genes chromosomes. Fitness Evaluation: BioGA define fitness function evaluates well particular solution performs respect problem addressed. context genomic data analysis, involve measures classification accuracy, correlation clinical outcomes, fitness particular model. Initialization: algorithm initialize population candidate solutions, typically randomly using heuristic method. solution population represents potential solution problem hand. Genetic Operations: BioGA apply genetic operators selection, crossover, mutation evolve population successive generations. Selection identifies individuals higher fitness serve parents next generation. Crossover combines genetic material two parent solutions produce offspring. Mutation introduces random changes offspring maintain genetic diversity. Termination Criteria: algorithm continue iterating generations termination criterion met. maximum number generations, reaching satisfactory solution, convergence population. Result Analysis: algorithm terminates, BioGA analyze final population identify best solution(s) found. involve validation interpretation results context original problem. applications BioGA genomic data analysis include genome-wide association studies (GWAS), gene expression analysis, pathway analysis, predictive modeling personalized medicine, among others. leveraging genetic algorithms, BioGA offers powerful approach exploring complex genomic datasets identifying meaningful patterns associations.","code":"# Plot fitness change over generations BioGA::plot_fitness_history(fitness_history) sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.4.0 (2024-04-24) #> os Ubuntu 22.04.4 LTS #> system x86_64, linux-gnu #> ui X11 #> language en #> collate C.UTF-8 #> ctype C.UTF-8 #> tz UTC #> date 2024-05-21 #> pandoc 3.1.11 @ /opt/hostedtoolcache/pandoc/3.1.11/x64/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] RSPM #> animation 2.7 2021-10-07 [1] RSPM #> Biobase * 2.64.0 2024-04-30 [1] Bioconduc~ #> BiocGenerics * 0.50.0 2024-04-30 [1] Bioconduc~ #> BiocManager 1.30.23 2024-05-04 [1] RSPM #> BiocStyle * 2.32.0 2024-04-30 [1] Bioconduc~ #> biocViews 1.72.0 2024-04-30 [1] Bioconduc~ #> BioGA * 0.99.5 2024-05-21 [1] local #> bitops 1.0-7 2021-04-24 [1] RSPM #> bookdown 0.39 2024-04-15 [1] RSPM #> bslib 0.7.0 2024-03-29 [1] RSPM #> cachem 1.1.0 2024-05-16 [1] RSPM #> cli 3.6.2 2023-12-11 [1] RSPM #> colorspace 2.1-0 2023-01-23 [1] RSPM #> crayon 1.5.2 2022-09-29 [1] RSPM #> DelayedArray 0.30.1 2024-05-07 [1] Bioconduc~ #> desc 1.4.3 2023-12-10 [1] RSPM #> digest 0.6.35 2024-03-11 [1] RSPM #> evaluate 0.23 2023-11-01 [1] RSPM #> fansi 1.0.6 2023-12-08 [1] RSPM #> farver 2.1.2 2024-05-13 [1] RSPM #> fastmap 1.2.0 2024-05-15 [1] RSPM #> fs 1.6.4 2024-04-25 [1] RSPM #> GenomeInfoDb * 1.40.0 2024-04-30 [1] Bioconduc~ #> GenomeInfoDbData 1.2.12 2024-05-20 [1] Bioconductor #> GenomicRanges * 1.56.0 2024-05-01 [1] Bioconduc~ #> ggplot2 3.5.1 2024-04-23 [1] RSPM #> glue 1.7.0 2024-01-09 [1] RSPM #> graph 1.82.0 2024-04-30 [1] Bioconduc~ #> gtable 0.3.5 2024-04-22 [1] RSPM #> highr 0.10 2022-12-22 [1] RSPM #> htmltools 0.5.8.1 2024-04-04 [1] RSPM #> httr 1.4.7 2023-08-15 [1] RSPM #> IRanges * 2.38.0 2024-04-30 [1] Bioconduc~ #> jquerylib 0.1.4 2021-04-26 [1] RSPM #> jsonlite 1.8.8 2023-12-04 [1] RSPM #> knitr 1.46 2024-04-06 [1] RSPM #> labeling 0.4.3 2023-08-29 [1] RSPM #> lattice 0.22-6 2024-03-20 [3] CRAN (R 4.4.0) #> lifecycle 1.0.4 2023-11-07 [1] RSPM #> magrittr 2.0.3 2022-03-30 [1] RSPM #> Matrix 1.7-0 2024-03-22 [3] CRAN (R 4.4.0) #> MatrixGenerics * 1.16.0 2024-04-30 [1] Bioconduc~ #> matrixStats * 1.3.0 2024-04-11 [1] RSPM #> memoise 2.0.1 2021-11-26 [1] RSPM #> munsell 0.5.1 2024-04-01 [1] RSPM #> pillar 1.9.0 2023-03-22 [1] RSPM #> pkgconfig 2.0.3 2019-09-22 [1] RSPM #> pkgdown 2.0.9 2024-04-18 [1] any (@2.0.9) #> purrr 1.0.2 2023-08-10 [1] RSPM #> R6 2.5.1 2021-08-19 [1] RSPM #> ragg 1.3.2 2024-05-15 [1] RSPM #> RBGL 1.80.0 2024-04-30 [1] Bioconduc~ #> Rcpp 1.0.12 2024-01-09 [1] RSPM #> RCurl 1.98-1.14 2024-01-09 [1] RSPM #> rlang 1.1.3 2024-01-10 [1] RSPM #> rmarkdown 2.27 2024-05-17 [1] RSPM #> RUnit 0.4.33 2024-02-22 [1] RSPM #> S4Arrays 1.4.0 2024-04-30 [1] Bioconduc~ #> S4Vectors * 0.42.0 2024-04-30 [1] Bioconduc~ #> sass 0.4.9 2024-03-15 [1] RSPM #> scales 1.3.0 2023-11-28 [1] RSPM #> sessioninfo 1.2.2 2021-12-06 [1] RSPM #> SparseArray 1.4.3 2024-05-07 [1] Bioconduc~ #> SummarizedExperiment * 1.34.0 2024-05-01 [1] Bioconduc~ #> systemfonts 1.1.0 2024-05-15 [1] RSPM #> textshaping 0.3.7 2023-10-09 [1] RSPM #> tibble 3.2.1 2023-03-20 [1] RSPM #> UCSC.utils 1.0.0 2024-04-30 [1] Bioconduc~ #> utf8 1.2.4 2023-10-22 [1] RSPM #> vctrs 0.6.5 2023-12-01 [1] RSPM #> withr 3.0.0 2024-01-16 [1] RSPM #> xfun 0.44 2024-05-15 [1] RSPM #> XML 3.99-0.16.1 2024-01-22 [1] RSPM #> XVector 0.44.0 2024-04-30 [1] Bioconduc~ #> yaml 2.3.8 2023-12-11 [1] RSPM #> zlibbioc 1.50.0 2024-04-30 [1] Bioconduc~ #> #> [1] /home/runner/work/_temp/Library #> [2] /opt/R/4.4.0/lib/R/site-library #> [3] /opt/R/4.4.0/lib/R/library #> #> ──────────────────────────────────────────────────────────────────────────────"},{"path":"https://danymukesha.github.io/BioGA/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Dany Mukesha. Author, maintainer.","code":""},{"path":"https://danymukesha.github.io/BioGA/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Mukesha D (2024). BioGA: Bioinformatics Genetic Algorithm (BioGA). R package version 0.99.5, https://danymukesha.github.io/BioGA/.","code":"@Manual{, title = {BioGA: Bioinformatics Genetic Algorithm (BioGA)}, author = {Dany Mukesha}, year = {2024}, note = {R package version 0.99.5}, url = {https://danymukesha.github.io/BioGA/}, }"},{"path":"https://danymukesha.github.io/BioGA/index.html","id":"bioga-","dir":"","previous_headings":"","what":"Bioinformatics Genetic Algorithm (BioGA)","title":"Bioinformatics Genetic Algorithm (BioGA)","text":"BioGA package provides set functions genetic algorithm optimization tailored analyzing high throughput genomic data. functions implemented C++ improved speed efficiency, easy--use interface use within R.","code":""},{"path":"https://danymukesha.github.io/BioGA/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bioinformatics Genetic Algorithm (BioGA)","text":"can install package directly GitHub using devtools package:","code":"devtools::install_github(\"danymukesha/BioGA\") #> Downloading GitHub repo danymukesha/BioGA@HEAD #> fs (1.6.3 -> 1.6.4 ) [CRAN] #> fastmap (1.1.1 -> 1.2.0 ) [CRAN] #> cachem (1.0.8 -> 1.1.0 ) [CRAN] #> xfun (0.42 -> 0.44 ) [CRAN] #> tinytex (0.50 -> 0.51 ) [CRAN] #> knitr (1.45 -> 1.46 ) [CRAN] #> htmltools (0.5.7 -> 0.5.8.1) [CRAN] #> bslib (0.6.1 -> 0.7.0 ) [CRAN] #> rmarkdown (2.26 -> 2.27 ) [CRAN] #> matrixStats (1.2.0 -> 1.3.0 ) [CRAN] #> munsell (0.5.0 -> 0.5.1 ) [CRAN] #> farver (2.1.1 -> 2.1.2 ) [CRAN] #> BiocManager (1.30.22 -> 1.30.23) [CRAN] #> bookdown (0.38 -> 0.39 ) [CRAN] #> gtable (0.3.4 -> 0.3.5 ) [CRAN] #> ggplot2 (3.5.0 -> 3.5.1 ) [CRAN] #> Skipping 17 packages ahead of CRAN: BiocGenerics, graph, S4Arrays, IRanges, S4Vectors, MatrixGenerics, GenomeInfoDbData, zlibbioc, XVector, GenomeInfoDb, RBGL, Biobase, DelayedArray, GenomicRanges, BiocStyle, biocViews, SummarizedExperiment #> Installing 16 packages: fs, fastmap, cachem, xfun, tinytex, knitr, htmltools, bslib, rmarkdown, matrixStats, munsell, farver, BiocManager, bookdown, gtable, ggplot2 #> Installing packages into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath8488528329e2' #> (as 'lib' is unspecified) #> Warning: unable to access index for repository https://bioconductor.org/packages/3.17/data/annotation/bin/windows/contrib/4.3: #> cannot open URL 'https://bioconductor.org/packages/3.17/data/annotation/bin/windows/contrib/4.3/PACKAGES' #> Warning: unable to access index for repository https://bioconductor.org/packages/3.17/data/experiment/bin/windows/contrib/4.3: #> cannot open URL 'https://bioconductor.org/packages/3.17/data/experiment/bin/windows/contrib/4.3/PACKAGES' #> Warning: unable to access index for repository https://bioconductor.org/packages/3.17/workflows/bin/windows/contrib/4.3: #> cannot open URL 'https://bioconductor.org/packages/3.17/workflows/bin/windows/contrib/4.3/PACKAGES' #> package 'fs' successfully unpacked and MD5 sums checked #> package 'fastmap' successfully unpacked and MD5 sums checked #> package 'cachem' successfully unpacked and MD5 sums checked #> package 'xfun' successfully unpacked and MD5 sums checked #> package 'tinytex' successfully unpacked and MD5 sums checked #> package 'knitr' successfully unpacked and MD5 sums checked #> package 'htmltools' successfully unpacked and MD5 sums checked #> package 'bslib' successfully unpacked and MD5 sums checked #> package 'rmarkdown' successfully unpacked and MD5 sums checked #> package 'matrixStats' successfully unpacked and MD5 sums checked #> package 'munsell' successfully unpacked and MD5 sums checked #> package 'farver' successfully unpacked and MD5 sums checked #> package 'BiocManager' successfully unpacked and MD5 sums checked #> package 'bookdown' successfully unpacked and MD5 sums checked #> package 'gtable' successfully unpacked and MD5 sums checked #> package 'ggplot2' successfully unpacked and MD5 sums checked #> #> The downloaded binary packages are in #> C:\\Users\\dany.mukesha\\AppData\\Local\\Temp\\RtmpWuKbDY\\downloaded_packages #> ── R CMD build ───────────────────────────────────────────────────────────────── #> ✔ checking for file 'C:\\Users\\dany.mukesha\\AppData\\Local\\Temp\\RtmpWuKbDY\\remotes99a020a46bb9\\danymukesha-BioGA-9b9a1cc/DESCRIPTION' (776ms) #> ─ preparing 'BioGA': #> checking DESCRIPTION meta-information ... checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information #> ─ cleaning src #> ─ checking for LF line-endings in source and make files and shell scripts #> ─ checking for empty or unneeded directories #> Omitted 'LazyData' from DESCRIPTION #> ─ building 'BioGA_0.99.5.tar.gz' #> #> #> Installing package into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath8488528329e2' #> (as 'lib' is unspecified)"},{"path":"https://danymukesha.github.io/BioGA/reference/BioGA-package.html","id":null,"dir":"Reference","previous_headings":"","what":"BioGA: Bioinformatics Genetic Algorithm (BioGA) — BioGA-package","title":"BioGA: Bioinformatics Genetic Algorithm (BioGA) — BioGA-package","text":"Genetic algorithm class optimization algorithms inspired process natural selection genetics. package allows users analyze optimize high throughput genomic data using genetic algorithms. functions provided implemented C++ improved speed efficiency, easy--use interface use within R.","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/reference/BioGA-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BioGA: Bioinformatics Genetic Algorithm (BioGA) — BioGA-package","text":"Maintainer: Dany Mukesha danymukesha@gmail.com (ORCID)","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to perform crossover between selected individuals — crossover_cpp","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"Function perform crossover selected individuals","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"","code":"crossover_cpp(selected_parents, offspring_size)"},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"selected_parents Numeric matrix representing selected individuals. offspring_size Number offspring generate.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"Numeric matrix representing offspring.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2) BioGA::crossover_cpp(selected_parents, offspring_size = 2) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -1.400044 0.2553171 -2.437264 -0.005571287 0.6215527 1.148412 -1.821818 #> [2,] -1.400044 0.2553171 -2.437264 -0.005571287 0.6215527 1.148412 -1.821818 #> [,8] [,9] [,10] #> [1,] -0.2473253 -0.2441996 -0.2827054 #> [2,] -0.2473253 -0.2441996 -0.2827054"},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"Function evaluate fitness using genomic data","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"","code":"evaluate_fitness_cpp(genomic_data, population)"},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"genomic_data Numeric matrix genomic data rows represent genes/features columns represent samples. population Numeric matrix representing population individuals.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"Numeric vector fitness scores individual.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) BioGA::evaluate_fitness_cpp(genomic_data, population) #> [1] 139.2861 139.2861 139.2861 139.2861 139.2861"},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to initialize the population from genomic data — initialize_population_cpp","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"Function initialize population genomic data","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"","code":"initialize_population_cpp(genomic_data, population_size)"},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"genomic_data Numeric matrix genomic data rows represent genes/features columns represent samples. population_size Number individuals population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"Numeric matrix representing initialized population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) BioGA::initialize_population_cpp(genomic_data, population_size = 5) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [2,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [3,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [4,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [5,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [,8] [,9] [,10] #> [1,] 1.46011 0.1496794 -1.433321 #> [2,] 1.46011 0.1496794 -1.433321 #> [3,] 1.46011 0.1496794 -1.433321 #> [4,] 1.46011 0.1496794 -1.433321 #> [5,] 1.46011 0.1496794 -1.433321"},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to mutate the offspring — mutation_cpp","title":"Function to mutate the offspring — mutation_cpp","text":"Function mutate offspring","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to mutate the offspring — mutation_cpp","text":"","code":"mutation_cpp(offspring, mutation_rate)"},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to mutate the offspring — mutation_cpp","text":"offspring Numeric matrix representing offspring. mutation_rate Probability mutation individual.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to mutate the offspring — mutation_cpp","text":"Numeric matrix representing mutated offspring.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to mutate the offspring — mutation_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2) offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2) BioGA::mutation_cpp(offspring, mutation_rate = 0) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -0.4709225 -0.133251 1.226682 0.332944 -0.3470885 -0.09855069 0.03476606 #> [2,] -0.4709225 -0.133251 1.226682 0.332944 -0.3470885 -0.09855069 0.03476606 #> [,8] [,9] [,10] #> [1,] 0.386127 0.02083123 0.007586777 #> [2,] 0.386127 0.02083123 0.007586777"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Fitness Values — plot_fitness","title":"Plot Fitness Values — plot_fitness","text":"Plot fitness values population generations.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Fitness Values — plot_fitness","text":"","code":"plot_fitness(fitness_values)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Fitness Values — plot_fitness","text":"fitness_values numeric vector containing fitness values.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Fitness Values — plot_fitness","text":"Plot fitness","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Fitness Values — plot_fitness","text":"","code":"# example of usage fitness_values <- c(10, 8, 6, 4, 2) plot_fitness(fitness_values)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Fitness Change Over Generations — plot_fitness_history","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"Plot change fitness values generations.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"","code":"plot_fitness_history(fitness_history)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"fitness_history list containing fitness values generation.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"Plot fitness history","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"","code":"# example of usage fitness_history <- list(c(10, 8, 6, 4, 2), c(9, 7, 5, 3, 1)) plot_fitness_history(fitness_history)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Population Distribution — plot_population","title":"Plot Population Distribution — plot_population","text":"Plot distribution individuals population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Population Distribution — plot_population","text":"","code":"plot_population(population)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Population Distribution — plot_population","text":"population numeric matrix containing population data.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Population Distribution — plot_population","text":"Plot population","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Population Distribution — plot_population","text":"","code":"# example of usage population <- matrix(runif(100), nrow = 10, ncol = 10) plot_population(population)"},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to replace non-selected individuals in the population — replacement_cpp","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"Replace non-selected individuals population","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"","code":"replacement_cpp(population, offspring, num_to_replace)"},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"population Numeric matrix representing population individuals. offspring Numeric matrix representing offspring. num_to_replace Number individuals replace.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"Numeric matrix representing updated population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2) offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2) mutated_offspring <- BioGA::mutation_cpp(offspring, mutation_rate = 0) BioGA::replacement_cpp(population, mutated_offspring, num_to_replace = 1) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [2,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [3,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [4,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [5,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [,8] [,9] [,10] #> [1,] -1.354928 -0.8175683 -0.6344 #> [2,] -1.354928 -0.8175683 -0.6344 #> [3,] -1.354928 -0.8175683 -0.6344 #> [4,] -1.354928 -0.8175683 -0.6344 #> [5,] -1.354928 -0.8175683 -0.6344"},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to select individuals based on fitness scores — selection_cpp","title":"Function to select individuals based on fitness scores — selection_cpp","text":"Function select individuals based fitness scores","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to select individuals based on fitness scores — selection_cpp","text":"","code":"selection_cpp(population, fitness, num_parents)"},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to select individuals based on fitness scores — selection_cpp","text":"population Numeric matrix representing population individuals. fitness Numeric vector fitness scores individual. num_parents Number individuals select.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to select individuals based on fitness scores — selection_cpp","text":"Numeric matrix representing selected individuals.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to select individuals based on fitness scores — selection_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) BioGA::selection_cpp(population, fitness, num_parents = 2) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.2445431 2.617297 -0.7378764 0.5842284 -1.238873 -0.8900997 -1.510025 #> [2,] 0.2445431 2.617297 -0.7378764 0.5842284 -1.238873 -0.8900997 -1.510025 #> [,8] [,9] [,10] #> [1,] -0.5851824 -1.553676 -0.5760951 #> [2,] -0.5851824 -1.553676 -0.5760951"},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/news/index.html","id":"bioga-0994","dir":"Changelog","previous_headings":"","what":"BioGA 0.99.4","title":"BioGA 0.99.4","text":"Fixed added updates requested Bioconductor Peer review","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/news/index.html","id":"bioga-0992","dir":"Changelog","previous_headings":"","what":"BioGA 0.99.2","title":"BioGA 0.99.2","text":"Fixed ERROR: System files ‘BioGA.Rproj’ found Git tracked.","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/news/index.html","id":"bioga-0990","dir":"Changelog","previous_headings":"","what":"BioGA 0.99.0","title":"BioGA 0.99.0","text":"Added NEWS.md file track changes package.","code":""}] +[{"path":"https://danymukesha.github.io/BioGA/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 Dany Mukesha Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"installation","dir":"Articles","previous_headings":"Getting Started","what":"Installation","title":"Introduction","text":"install package, start R (version “4.4”) enter: can also install package directly GitHub using devtools package: vignette, illustrate usage BioGA genetic algorithm optimization context high throughput genomic data analysis. showcase interoperability Bioconductor classes, demonstrating genetic algorithm optimization can seamlessly integrated existing genomics pipelines improved analysis interpretation. BioGA package provides set functions genetic algorithm optimization tailored analyzing high throughput genomic data. vignette demonstrates usage BioGA context selecting best combination genes predicting certain trait, disease susceptibility.","code":"if (!require(\"BiocManager\", quietly = TRUE)) install.packages(\"BiocManager\") BiocManager::install(\"BioGA\") devtools::install_github(\"danymukesha/BioGA\")"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"overview","dir":"Articles","previous_headings":"Getting Started","what":"Overview","title":"Introduction","text":"Genomic data refers genetic information stored organism’s DNA. includes sequence nucleotides (adenine, thymine, cytosine, guanine) make DNA molecules. Genomic data can provide valuable insights various biological processes, gene expression, genetic variation, evolutionary relationships. Genomic data context consist gene expression profiles measured across different individuals (e.g., patients). row genomic_data matrix represents gene, column represents patient sample. values matrix represent expression levels gene patient sample. ’s example genomic data: example, row represents gene (genomic feature), column represents sample. values matrix represent measurement gene expression, mRNA levels protein abundance, sample. instance, value 0.1 Sample 1 Gene1 indicates expression level Gene1 Sample 1. Similarly, value 2.2 Sample 2 Gene3 indicates expression level Gene3 Sample 2. Genomic data can used various analyses, including genetic association studies, gene expression analysis, comparative genomics. context evaluate_fitness_cpp function, genomic data used calculate fitness scores individuals population, typically context genetic algorithm optimization. population represents set candidate combinations genes predictive trait. individual population represented binary vector indicating presence absence gene. example, individual population might represented [1, 0, 1], indicating presence Gene1 Gene3 absence Gene2. population undergoes genetic algorithm operations selection, crossover, mutation, replacement evolve towards individuals higher predictive power trait.","code":"Sample 1 Sample 2 Sample 3 Sample 4 Gene1 0.1 0.2 0.3 0.4 Gene2 1.2 1.3 1.4 1.5 Gene3 2.3 2.2 2.1 2.0"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"example-scenario","dir":"Articles","previous_headings":"Getting Started","what":"Example Scenario","title":"Introduction","text":"Consider example scenario using genetic algorithm optimization select best combination genes predicting certain trait, disease susceptibility. example, counts matrix representing counts gene expression levels across different samples. row corresponds gene, column corresponds sample. use SummarizedExperiment class store data, common Bioconductor class representing rectangular feature x sample data, RNAseq count matrices microarray data.","code":"# Load necessary packages library(BioGA) library(SummarizedExperiment) #> Loading required package: MatrixGenerics #> Loading required package: matrixStats #> #> Attaching package: 'MatrixGenerics' #> The following objects are masked from 'package:matrixStats': #> #> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, #> colCounts, colCummaxs, colCummins, colCumprods, colCumsums, #> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, #> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, #> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, #> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, #> colWeightedMeans, colWeightedMedians, colWeightedSds, #> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, #> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, #> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, #> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, #> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, #> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, #> rowWeightedMads, rowWeightedMeans, rowWeightedMedians, #> rowWeightedSds, rowWeightedVars #> Loading required package: GenomicRanges #> Loading required package: stats4 #> Loading required package: BiocGenerics #> #> Attaching package: 'BiocGenerics' #> The following objects are masked from 'package:stats': #> #> IQR, mad, sd, var, xtabs #> The following objects are masked from 'package:base': #> #> anyDuplicated, aperm, append, as.data.frame, basename, cbind, #> colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, #> get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, #> match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, #> Position, rank, rbind, Reduce, rownames, sapply, setdiff, table, #> tapply, union, unique, unsplit, which.max, which.min #> Loading required package: S4Vectors #> #> Attaching package: 'S4Vectors' #> The following object is masked from 'package:utils': #> #> findMatches #> The following objects are masked from 'package:base': #> #> expand.grid, I, unname #> Loading required package: IRanges #> Loading required package: GenomeInfoDb #> Loading required package: Biobase #> Welcome to Bioconductor #> #> Vignettes contain introductory material; view with #> 'browseVignettes()'. To cite Bioconductor, see #> 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'. #> #> Attaching package: 'Biobase' #> The following object is masked from 'package:MatrixGenerics': #> #> rowMedians #> The following objects are masked from 'package:matrixStats': #> #> anyMissing, rowMedians # Define parameters num_genes <- 1000 num_samples <- 10 # Define parameters for genetic algorithm population_size <- 100 generations <- 20 mutation_rate <- 0.1 # Generate example genomic data using SummarizedExperiment counts <- matrix(rpois(num_genes * num_samples, lambda = 10), nrow = num_genes ) rownames(counts) <- paste0(\"Gene\", 1:num_genes) colnames(counts) <- paste0(\"Sample\", 1:num_samples) # Create SummarizedExperiment object se <- SummarizedExperiment::SummarizedExperiment(assays = list(counts = counts)) # Convert SummarizedExperiment to matrix for compatibility with BioGA package genomic_data <- assay(se) head(genomic_data) #> Sample1 Sample2 Sample3 Sample4 Sample5 Sample6 Sample7 Sample8 Sample9 #> Gene1 5 11 6 14 9 14 4 12 8 #> Gene2 6 10 3 10 11 6 6 8 10 #> Gene3 9 12 9 9 12 10 12 7 10 #> Gene4 11 11 7 13 13 6 11 16 6 #> Gene5 13 12 8 7 9 9 14 10 9 #> Gene6 4 10 11 7 9 13 7 16 11 #> Sample10 #> Gene1 12 #> Gene2 10 #> Gene3 10 #> Gene4 11 #> Gene5 12 #> Gene6 8"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"initialization","dir":"Articles","previous_headings":"Getting Started","what":"Initialization","title":"Introduction","text":"population represents set candidate combinations genes predictive trait. individual population represented binary vector indicating presence absence gene. example, individual population might represented [1, 0, 1], indicating presence Gene1 Gene3 absence Gene2. population undergoes genetic algorithm operations selection, crossover, mutation, replacement evolve towards individuals higher predictive power trait.","code":"# Initialize population (select the number of canditate you wish `population`) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5 )"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"genetic-algorithm-optimization","dir":"Articles","previous_headings":"Getting Started","what":"Genetic Algorithm Optimization","title":"Introduction","text":"","code":"# Initialize fitness history fitness_history <- list() # Initialize time progress start_time <- Sys.time() # Run genetic algorithm optimization generation <- 0 while (TRUE) { generation <- generation + 1 # Evaluate fitness fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) fitness_history[[generation]] <- fitness # Check termination condition if (generation == generations) { # defined number of generations break } # Selection selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2 ) # Crossover and Mutation offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2) # (no mutation in this example) mutated_offspring <- BioGA::mutation_cpp(offspring, mutation_rate = 0) # Replacement population <- BioGA::replacement_cpp(population, mutated_offspring, num_to_replace = 1 ) # Calculate time progress elapsed_time <- difftime(Sys.time(), start_time, units = \"secs\") # Print time progress cat( \"\\rGeneration:\", generation, \"- Elapsed Time:\", format(elapsed_time, units = \"secs\"), \" \" ) } #> Generation: 1 - Elapsed Time: 0.009090424 secs Generation: 2 - Elapsed Time: 0.01087737 secs Generation: 3 - Elapsed Time: 0.0110631 secs Generation: 4 - Elapsed Time: 0.01123357 secs Generation: 5 - Elapsed Time: 0.0114007 secs Generation: 6 - Elapsed Time: 0.01156545 secs Generation: 7 - Elapsed Time: 0.01175046 secs Generation: 8 - Elapsed Time: 0.01192689 secs Generation: 9 - Elapsed Time: 0.01209474 secs Generation: 10 - Elapsed Time: 0.01225996 secs Generation: 11 - Elapsed Time: 0.01242828 secs Generation: 12 - Elapsed Time: 0.01259375 secs Generation: 13 - Elapsed Time: 0.01275945 secs Generation: 14 - Elapsed Time: 0.01295376 secs Generation: 15 - Elapsed Time: 0.01312971 secs Generation: 16 - Elapsed Time: 0.01329684 secs Generation: 17 - Elapsed Time: 0.01346183 secs Generation: 18 - Elapsed Time: 0.01362658 secs Generation: 19 - Elapsed Time: 0.01378894 secs"},{"path":"https://danymukesha.github.io/BioGA/articles/Introduction.html","id":"fitness-calculation","dir":"Articles","previous_headings":"Getting Started","what":"Fitness Calculation","title":"Introduction","text":"fitness calculation described provided code calculates measure dissimilarity gene expression profiles individuals population genomic data. measure dissimilarity, “fitness”, quantifies well gene expression profile individual matches genomic data. Mathematically, fitness calculation can represented follows: Let: \\(g_{ijk}\\) gene expression level gene \\(j\\) individual \\(\\) sample \\(k\\) genomic data. \\(p_{ij}\\) gene expression level gene \\(j\\) individual \\(\\) population. \\(N\\) number individuals population. \\(G\\) number genes. \\(S\\) number samples. , fitness \\(F_i\\) individual \\(\\) population can calculated sum squared differences gene expression levels individual \\(\\) corresponding gene expression levels genomic data, across genes samples: \\[ F_i = \\sum_{j=1}^{G} \\sum_{k=1}^{S} (g_{ijk} - p_{ij})^2 \\] fitness calculation aims minimize overall dissimilarity gene expression profiles individuals population genomic data. Individuals lower fitness scores considered gene expression profiles similar genomic data therefore likely selected optimization genetic algorithm. showcases integration genetic algorithms genomic data analysis highlights potential genetic algorithms feature selection genomics. ’s BioGA work context high throughput genomic data analysis: Problem Definition: BioGA starts clear definition problem solved. include tasks identifying genetic markers associated particular disease, optimizing gene expression patterns, clustering genomic data identify patterns groupings. Representation: Genomic data need appropriately represented use within genetic algorithm framework. might involve encoding data suitable format, binary strings representing genes chromosomes. Fitness Evaluation: BioGA define fitness function evaluates well particular solution performs respect problem addressed. context genomic data analysis, involve measures classification accuracy, correlation clinical outcomes, fitness particular model. Initialization: algorithm initialize population candidate solutions, typically randomly using heuristic method. solution population represents potential solution problem hand. Genetic Operations: BioGA apply genetic operators selection, crossover, mutation evolve population successive generations. Selection identifies individuals higher fitness serve parents next generation. Crossover combines genetic material two parent solutions produce offspring. Mutation introduces random changes offspring maintain genetic diversity. Termination Criteria: algorithm continue iterating generations termination criterion met. maximum number generations, reaching satisfactory solution, convergence population. Result Analysis: algorithm terminates, BioGA analyze final population identify best solution(s) found. involve validation interpretation results context original problem. applications BioGA genomic data analysis include genome-wide association studies (GWAS), gene expression analysis, pathway analysis, predictive modeling personalized medicine, among others. leveraging genetic algorithms, BioGA offers powerful approach exploring complex genomic datasets identifying meaningful patterns associations.","code":"# Plot fitness change over generations BioGA::plot_fitness_history(fitness_history) sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.4.0 (2024-04-24) #> os Ubuntu 22.04.4 LTS #> system x86_64, linux-gnu #> ui X11 #> language en #> collate C.UTF-8 #> ctype C.UTF-8 #> tz UTC #> date 2024-05-21 #> pandoc 3.1.11 @ /opt/hostedtoolcache/pandoc/3.1.11/x64/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] RSPM #> animation 2.7 2021-10-07 [1] RSPM #> Biobase * 2.64.0 2024-04-30 [1] Bioconduc~ #> BiocGenerics * 0.50.0 2024-04-30 [1] Bioconduc~ #> BiocManager 1.30.23 2024-05-04 [1] RSPM #> BiocStyle * 2.32.0 2024-04-30 [1] Bioconduc~ #> biocViews 1.72.0 2024-04-30 [1] Bioconduc~ #> BioGA * 0.99.5 2024-05-21 [1] local #> bitops 1.0-7 2021-04-24 [1] RSPM #> bookdown 0.39 2024-04-15 [1] RSPM #> bslib 0.7.0 2024-03-29 [1] RSPM #> cachem 1.1.0 2024-05-16 [1] RSPM #> cli 3.6.2 2023-12-11 [1] RSPM #> colorspace 2.1-0 2023-01-23 [1] RSPM #> crayon 1.5.2 2022-09-29 [1] RSPM #> DelayedArray 0.30.1 2024-05-07 [1] Bioconduc~ #> desc 1.4.3 2023-12-10 [1] RSPM #> digest 0.6.35 2024-03-11 [1] RSPM #> evaluate 0.23 2023-11-01 [1] RSPM #> fansi 1.0.6 2023-12-08 [1] RSPM #> farver 2.1.2 2024-05-13 [1] RSPM #> fastmap 1.2.0 2024-05-15 [1] RSPM #> fs 1.6.4 2024-04-25 [1] RSPM #> GenomeInfoDb * 1.40.0 2024-04-30 [1] Bioconduc~ #> GenomeInfoDbData 1.2.12 2024-05-20 [1] Bioconductor #> GenomicRanges * 1.56.0 2024-05-01 [1] Bioconduc~ #> ggplot2 3.5.1 2024-04-23 [1] RSPM #> glue 1.7.0 2024-01-09 [1] RSPM #> graph 1.82.0 2024-04-30 [1] Bioconduc~ #> gtable 0.3.5 2024-04-22 [1] RSPM #> highr 0.10 2022-12-22 [1] RSPM #> htmltools 0.5.8.1 2024-04-04 [1] RSPM #> httr 1.4.7 2023-08-15 [1] RSPM #> IRanges * 2.38.0 2024-04-30 [1] Bioconduc~ #> jquerylib 0.1.4 2021-04-26 [1] RSPM #> jsonlite 1.8.8 2023-12-04 [1] RSPM #> knitr 1.46 2024-04-06 [1] RSPM #> labeling 0.4.3 2023-08-29 [1] RSPM #> lattice 0.22-6 2024-03-20 [3] CRAN (R 4.4.0) #> lifecycle 1.0.4 2023-11-07 [1] RSPM #> magrittr 2.0.3 2022-03-30 [1] RSPM #> Matrix 1.7-0 2024-03-22 [3] CRAN (R 4.4.0) #> MatrixGenerics * 1.16.0 2024-04-30 [1] Bioconduc~ #> matrixStats * 1.3.0 2024-04-11 [1] RSPM #> memoise 2.0.1 2021-11-26 [1] RSPM #> munsell 0.5.1 2024-04-01 [1] RSPM #> pillar 1.9.0 2023-03-22 [1] RSPM #> pkgconfig 2.0.3 2019-09-22 [1] RSPM #> pkgdown 2.0.9 2024-04-18 [1] any (@2.0.9) #> purrr 1.0.2 2023-08-10 [1] RSPM #> R6 2.5.1 2021-08-19 [1] RSPM #> ragg 1.3.2 2024-05-15 [1] RSPM #> RBGL 1.80.0 2024-04-30 [1] Bioconduc~ #> Rcpp 1.0.12 2024-01-09 [1] RSPM #> RCurl 1.98-1.14 2024-01-09 [1] RSPM #> rlang 1.1.3 2024-01-10 [1] RSPM #> rmarkdown 2.27 2024-05-17 [1] RSPM #> RUnit 0.4.33 2024-02-22 [1] RSPM #> S4Arrays 1.4.0 2024-04-30 [1] Bioconduc~ #> S4Vectors * 0.42.0 2024-04-30 [1] Bioconduc~ #> sass 0.4.9 2024-03-15 [1] RSPM #> scales 1.3.0 2023-11-28 [1] RSPM #> sessioninfo 1.2.2 2021-12-06 [1] RSPM #> SparseArray 1.4.3 2024-05-07 [1] Bioconduc~ #> SummarizedExperiment * 1.34.0 2024-05-01 [1] Bioconduc~ #> systemfonts 1.1.0 2024-05-15 [1] RSPM #> textshaping 0.3.7 2023-10-09 [1] RSPM #> tibble 3.2.1 2023-03-20 [1] RSPM #> UCSC.utils 1.0.0 2024-04-30 [1] Bioconduc~ #> utf8 1.2.4 2023-10-22 [1] RSPM #> vctrs 0.6.5 2023-12-01 [1] RSPM #> withr 3.0.0 2024-01-16 [1] RSPM #> xfun 0.44 2024-05-15 [1] RSPM #> XML 3.99-0.16.1 2024-01-22 [1] RSPM #> XVector 0.44.0 2024-04-30 [1] Bioconduc~ #> yaml 2.3.8 2023-12-11 [1] RSPM #> zlibbioc 1.50.0 2024-04-30 [1] Bioconduc~ #> #> [1] /home/runner/work/_temp/Library #> [2] /opt/R/4.4.0/lib/R/site-library #> [3] /opt/R/4.4.0/lib/R/library #> #> ──────────────────────────────────────────────────────────────────────────────"},{"path":"https://danymukesha.github.io/BioGA/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Dany Mukesha. Author, maintainer.","code":""},{"path":"https://danymukesha.github.io/BioGA/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Mukesha D (2024). BioGA: Bioinformatics Genetic Algorithm (BioGA). R package version 0.99.5, https://danymukesha.github.io/BioGA/.","code":"@Manual{, title = {BioGA: Bioinformatics Genetic Algorithm (BioGA)}, author = {Dany Mukesha}, year = {2024}, note = {R package version 0.99.5}, url = {https://danymukesha.github.io/BioGA/}, }"},{"path":"https://danymukesha.github.io/BioGA/index.html","id":"bioga-","dir":"","previous_headings":"","what":"Bioinformatics Genetic Algorithm (BioGA)","title":"Bioinformatics Genetic Algorithm (BioGA)","text":"BioGA package provides set functions genetic algorithm optimization tailored analyzing high throughput genomic data. functions implemented C++ improved speed efficiency, easy--use interface use within R.","code":""},{"path":"https://danymukesha.github.io/BioGA/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bioinformatics Genetic Algorithm (BioGA)","text":"can install package directly GitHub using devtools package:","code":"devtools::install_github(\"danymukesha/BioGA\") #> Downloading GitHub repo danymukesha/BioGA@HEAD #> fs (1.6.3 -> 1.6.4 ) [CRAN] #> fastmap (1.1.1 -> 1.2.0 ) [CRAN] #> cachem (1.0.8 -> 1.1.0 ) [CRAN] #> xfun (0.42 -> 0.44 ) [CRAN] #> tinytex (0.50 -> 0.51 ) [CRAN] #> knitr (1.45 -> 1.46 ) [CRAN] #> htmltools (0.5.7 -> 0.5.8.1) [CRAN] #> bslib (0.6.1 -> 0.7.0 ) [CRAN] #> rmarkdown (2.26 -> 2.27 ) [CRAN] #> matrixStats (1.2.0 -> 1.3.0 ) [CRAN] #> munsell (0.5.0 -> 0.5.1 ) [CRAN] #> farver (2.1.1 -> 2.1.2 ) [CRAN] #> BiocManager (1.30.22 -> 1.30.23) [CRAN] #> bookdown (0.38 -> 0.39 ) [CRAN] #> gtable (0.3.4 -> 0.3.5 ) [CRAN] #> ggplot2 (3.5.0 -> 3.5.1 ) [CRAN] #> Skipping 17 packages ahead of CRAN: BiocGenerics, graph, S4Arrays, IRanges, S4Vectors, MatrixGenerics, GenomeInfoDbData, zlibbioc, XVector, GenomeInfoDb, RBGL, Biobase, DelayedArray, GenomicRanges, BiocStyle, biocViews, SummarizedExperiment #> Installing 16 packages: fs, fastmap, cachem, xfun, tinytex, knitr, htmltools, bslib, rmarkdown, matrixStats, munsell, farver, BiocManager, bookdown, gtable, ggplot2 #> Installing packages into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath848868d23488' #> (as 'lib' is unspecified) #> Warning: unable to access index for repository https://bioconductor.org/packages/3.17/data/annotation/bin/windows/contrib/4.3: #> cannot open URL 'https://bioconductor.org/packages/3.17/data/annotation/bin/windows/contrib/4.3/PACKAGES' #> Warning: unable to access index for repository https://bioconductor.org/packages/3.17/data/experiment/bin/windows/contrib/4.3: #> cannot open URL 'https://bioconductor.org/packages/3.17/data/experiment/bin/windows/contrib/4.3/PACKAGES' #> Warning: unable to access index for repository https://bioconductor.org/packages/3.17/workflows/bin/windows/contrib/4.3: #> cannot open URL 'https://bioconductor.org/packages/3.17/workflows/bin/windows/contrib/4.3/PACKAGES' #> package 'fs' successfully unpacked and MD5 sums checked #> package 'fastmap' successfully unpacked and MD5 sums checked #> package 'cachem' successfully unpacked and MD5 sums checked #> package 'xfun' successfully unpacked and MD5 sums checked #> package 'tinytex' successfully unpacked and MD5 sums checked #> package 'knitr' successfully unpacked and MD5 sums checked #> package 'htmltools' successfully unpacked and MD5 sums checked #> package 'bslib' successfully unpacked and MD5 sums checked #> package 'rmarkdown' successfully unpacked and MD5 sums checked #> package 'matrixStats' successfully unpacked and MD5 sums checked #> package 'munsell' successfully unpacked and MD5 sums checked #> package 'farver' successfully unpacked and MD5 sums checked #> package 'BiocManager' successfully unpacked and MD5 sums checked #> package 'bookdown' successfully unpacked and MD5 sums checked #> package 'gtable' successfully unpacked and MD5 sums checked #> package 'ggplot2' successfully unpacked and MD5 sums checked #> #> The downloaded binary packages are in #> C:\\Users\\dany.mukesha\\AppData\\Local\\Temp\\RtmpArcJMj\\downloaded_packages #> ── R CMD build ───────────────────────────────────────────────────────────────── #> checking for file 'C:\\Users\\dany.mukesha\\AppData\\Local\\Temp\\RtmpArcJMj\\remotes15e82c92423\\danymukesha-BioGA-23ecb91/DESCRIPTION' ... ✔ checking for file 'C:\\Users\\dany.mukesha\\AppData\\Local\\Temp\\RtmpArcJMj\\remotes15e82c92423\\danymukesha-BioGA-23ecb91/DESCRIPTION' (343ms) #> ─ preparing 'BioGA': #> checking DESCRIPTION meta-information ... checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information #> ─ cleaning src #> ─ checking for LF line-endings in source and make files and shell scripts #> ─ checking for empty or unneeded directories #> Omitted 'LazyData' from DESCRIPTION #> ─ building 'BioGA_0.99.5.tar.gz' #> #> #> Installing package into 'C:/Users/dany.mukesha/AppData/Local/Temp/Rtmp63bptc/temp_libpath848868d23488' #> (as 'lib' is unspecified)"},{"path":"https://danymukesha.github.io/BioGA/reference/BioGA-package.html","id":null,"dir":"Reference","previous_headings":"","what":"BioGA: Bioinformatics Genetic Algorithm (BioGA) — BioGA-package","title":"BioGA: Bioinformatics Genetic Algorithm (BioGA) — BioGA-package","text":"Genetic algorithm class optimization algorithms inspired process natural selection genetics. package allows users analyze optimize high throughput genomic data using genetic algorithms. functions provided implemented C++ improved speed efficiency, easy--use interface use within R.","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/reference/BioGA-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BioGA: Bioinformatics Genetic Algorithm (BioGA) — BioGA-package","text":"Maintainer: Dany Mukesha danymukesha@gmail.com (ORCID)","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to perform crossover between selected individuals — crossover_cpp","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"Function perform crossover selected individuals","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"","code":"crossover_cpp(selected_parents, offspring_size)"},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"selected_parents Numeric matrix representing selected individuals. offspring_size Number offspring generate.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"Numeric matrix representing offspring.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/crossover_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to perform crossover between selected individuals — crossover_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2) BioGA::crossover_cpp(selected_parents, offspring_size = 2) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -1.400044 0.2553171 -2.437264 -0.005571287 0.6215527 1.148412 -1.821818 #> [2,] -1.400044 0.2553171 -2.437264 -0.005571287 0.6215527 1.148412 -1.821818 #> [,8] [,9] [,10] #> [1,] -0.2473253 -0.2441996 -0.2827054 #> [2,] -0.2473253 -0.2441996 -0.2827054"},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"Function evaluate fitness using genomic data","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"","code":"evaluate_fitness_cpp(genomic_data, population)"},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"genomic_data Numeric matrix genomic data rows represent genes/features columns represent samples. population Numeric matrix representing population individuals.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"Numeric vector fitness scores individual.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/evaluate_fitness_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to evaluate fitness using genomic data — evaluate_fitness_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) BioGA::evaluate_fitness_cpp(genomic_data, population) #> [1] 139.2861 139.2861 139.2861 139.2861 139.2861"},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to initialize the population from genomic data — initialize_population_cpp","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"Function initialize population genomic data","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"","code":"initialize_population_cpp(genomic_data, population_size)"},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"genomic_data Numeric matrix genomic data rows represent genes/features columns represent samples. population_size Number individuals population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"Numeric matrix representing initialized population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/initialize_population_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to initialize the population from genomic data — initialize_population_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) BioGA::initialize_population_cpp(genomic_data, population_size = 5) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [2,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [3,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [4,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [5,] -1.193641 -0.7517233 1.455841 -0.8286035 0.2897745 -0.4800535 -0.6048294 #> [,8] [,9] [,10] #> [1,] 1.46011 0.1496794 -1.433321 #> [2,] 1.46011 0.1496794 -1.433321 #> [3,] 1.46011 0.1496794 -1.433321 #> [4,] 1.46011 0.1496794 -1.433321 #> [5,] 1.46011 0.1496794 -1.433321"},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to mutate the offspring — mutation_cpp","title":"Function to mutate the offspring — mutation_cpp","text":"Function mutate offspring","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to mutate the offspring — mutation_cpp","text":"","code":"mutation_cpp(offspring, mutation_rate)"},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to mutate the offspring — mutation_cpp","text":"offspring Numeric matrix representing offspring. mutation_rate Probability mutation individual.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to mutate the offspring — mutation_cpp","text":"Numeric matrix representing mutated offspring.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/mutation_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to mutate the offspring — mutation_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2) offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2) BioGA::mutation_cpp(offspring, mutation_rate = 0) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -0.4709225 -0.133251 1.226682 0.332944 -0.3470885 -0.09855069 0.03476606 #> [2,] -0.4709225 -0.133251 1.226682 0.332944 -0.3470885 -0.09855069 0.03476606 #> [,8] [,9] [,10] #> [1,] 0.386127 0.02083123 0.007586777 #> [2,] 0.386127 0.02083123 0.007586777"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Fitness Values — plot_fitness","title":"Plot Fitness Values — plot_fitness","text":"Plot fitness values population generations.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Fitness Values — plot_fitness","text":"","code":"plot_fitness(fitness_values)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Fitness Values — plot_fitness","text":"fitness_values numeric vector containing fitness values.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Fitness Values — plot_fitness","text":"Plot fitness","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Fitness Values — plot_fitness","text":"","code":"# example of usage fitness_values <- c(10, 8, 6, 4, 2) plot_fitness(fitness_values)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Fitness Change Over Generations — plot_fitness_history","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"Plot change fitness values generations.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"","code":"plot_fitness_history(fitness_history)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"fitness_history list containing fitness values generation.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"Plot fitness history","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_fitness_history.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Fitness Change Over Generations — plot_fitness_history","text":"","code":"# example of usage fitness_history <- list(c(10, 8, 6, 4, 2), c(9, 7, 5, 3, 1)) plot_fitness_history(fitness_history)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Population Distribution — plot_population","title":"Plot Population Distribution — plot_population","text":"Plot distribution individuals population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Population Distribution — plot_population","text":"","code":"plot_population(population)"},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Population Distribution — plot_population","text":"population numeric matrix containing population data.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Population Distribution — plot_population","text":"Plot population","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/plot_population.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Population Distribution — plot_population","text":"","code":"# example of usage population <- matrix(runif(100), nrow = 10, ncol = 10) plot_population(population)"},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to replace non-selected individuals in the population — replacement_cpp","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"Replace non-selected individuals population","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"","code":"replacement_cpp(population, offspring, num_to_replace)"},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"population Numeric matrix representing population individuals. offspring Numeric matrix representing offspring. num_to_replace Number individuals replace.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"Numeric matrix representing updated population.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/replacement_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to replace non-selected individuals in the population — replacement_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2) offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2) mutated_offspring <- BioGA::mutation_cpp(offspring, mutation_rate = 0) BioGA::replacement_cpp(population, mutated_offspring, num_to_replace = 1) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [2,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [3,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [4,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [5,] -2.645212 -1.032457 -0.7074664 -0.70056 0.5378854 -0.3163322 -0.8396228 #> [,8] [,9] [,10] #> [1,] -1.354928 -0.8175683 -0.6344 #> [2,] -1.354928 -0.8175683 -0.6344 #> [3,] -1.354928 -0.8175683 -0.6344 #> [4,] -1.354928 -0.8175683 -0.6344 #> [5,] -1.354928 -0.8175683 -0.6344"},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to select individuals based on fitness scores — selection_cpp","title":"Function to select individuals based on fitness scores — selection_cpp","text":"Function select individuals based fitness scores","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to select individuals based on fitness scores — selection_cpp","text":"","code":"selection_cpp(population, fitness, num_parents)"},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to select individuals based on fitness scores — selection_cpp","text":"population Numeric matrix representing population individuals. fitness Numeric vector fitness scores individual. num_parents Number individuals select.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to select individuals based on fitness scores — selection_cpp","text":"Numeric matrix representing selected individuals.","code":""},{"path":"https://danymukesha.github.io/BioGA/reference/selection_cpp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to select individuals based on fitness scores — selection_cpp","text":"","code":"# example of usage genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10) population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5) fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population) BioGA::selection_cpp(population, fitness, num_parents = 2) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.2445431 2.617297 -0.7378764 0.5842284 -1.238873 -0.8900997 -1.510025 #> [2,] 0.2445431 2.617297 -0.7378764 0.5842284 -1.238873 -0.8900997 -1.510025 #> [,8] [,9] [,10] #> [1,] -0.5851824 -1.553676 -0.5760951 #> [2,] -0.5851824 -1.553676 -0.5760951"},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/news/index.html","id":"bioga-0994","dir":"Changelog","previous_headings":"","what":"BioGA 0.99.4","title":"BioGA 0.99.4","text":"Fixed added updates requested Bioconductor Peer review","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/news/index.html","id":"bioga-0992","dir":"Changelog","previous_headings":"","what":"BioGA 0.99.2","title":"BioGA 0.99.2","text":"Fixed ERROR: System files ‘BioGA.Rproj’ found Git tracked.","code":""},{"path":[]},{"path":"https://danymukesha.github.io/BioGA/news/index.html","id":"bioga-0990","dir":"Changelog","previous_headings":"","what":"BioGA 0.99.0","title":"BioGA 0.99.0","text":"Added NEWS.md file track changes package.","code":""}]