reinitialising

.github/workflows/r.yml

@@ -0,0 +1,22 @@
+name: 🚀
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: r-lib/actions/setup-r@v2
+      - name: Install dependencies on Ubuntu
+        run: |
+          sudo apt install -y libcurl4-openssl-dev libharfbuzz-dev libfribidi-dev
+      - name: Install dependencies
+        run: |
+          install.packages(c("devtools", "rextendr", "testthat"))
+        shell: Rscript {0}
+      - name: Tests
+        run: |
+          Rscript tests/tests.R

.gitignore

@@ -6,11 +6,11 @@
 /tests/apple.vcf
 /tests/soybean.vcf
 /misc/
-/res/eval/*.csv
-/res/eval/*.rds
-/res/eval/*.sync
-/res/eval/*.out
-/res/eval/*.tmp
-/res/eval/*.vcf
-/res/eval/*BK*
+/res/*.csv
+/res/*.rds
+/res/*.sync
+/res/*.out
+/res/*.tmp
+/res/*.vcf
+/res/*BK*
 /.vscode/

DESCRIPTION

@@ -0,0 +1,14 @@
+Package: imputef
+Title: Imputing allele frequencies for individual polyploid genotype data and pools of individuals or population genotype data
+Version: 0.0.0.1
+Authors@R: 
+    person("Jeff", "Paril", , "[email protected]", role = c("aut", "cre", "mai"),
+           comment = c(ORCID = "0000-0002-5693-4123"))
+Description: Imputation of genotype data from sequencing of more than 2 sets of genomes, i.e. polyploid individuals, population samples, or pools of individuals. This library can also perform simple genotype data filtering prior to imputation. Two imputation methods are available: (1) mean value imputation which uses the arithmentic mean of the locus across non-missing pools (`?imputef::mvi`); (2) adaptive linkage-informed k-nearest neighbour imputation (`?imputef::aldknni`). This is an attempt to extend the [LD-kNNi method of Money et al, 2015, i.e. LinkImpute](https://doi.org/10.1534/g3.115.021667), which was an extension of the [kNN imputation of Troyanskaya et al, 2001](https://doi.org/10.1093/bioinformatics/17.6.520). Similar to LD-kNNi, LD is estimated using Pearson's product moment correlation across loci per pair of samples, but instead of computing this across all the loci, we divide the genome into windows which respect chromosomal/scaffold boundaries. We use Euclidean distance which accomodates for continuous allele frequencies instead of genotype classes as in taxicab or Manhattan distance used in LD-kNNi. The adaptive behavior of our algorithm can be described in cases where the sparsity in the data is too high resulting to:
+  - completely undefined correlation (LD) matrix, at which point we will use all the loci to compute distances between samples, and when
+  - all k-nearest neighbours are missing at the locus which needs to be imputed, then we increase `k` until one of the neighbours has data to be used for weighted imputation of the missing allele.
+License: `use_gpl3_license()`
+Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.2.3
+Config/rextendr/version: 0.3.1

NAMESPACE

@@ -0,0 +1,5 @@
+# Generated by roxygen2: do not edit by hand
+
+export(aldknni)
+export(mvi)
+useDynLib(imputef, .registration = TRUE)

R/extendr-wrappers.R

@@ -0,0 +1,17 @@
+# Generated by extendr: Do not edit by hand
+
+# nolint start
+
+#
+# This file was created with the following call:
+#   .Call("wrap__make_imputef_wrappers", use_symbols = TRUE, package_name = "imputef")
+
+#' @docType package
+#' @usage NULL
+#' @useDynLib imputef, .registration = TRUE
+NULL
+
+impute <- function(fname, imputation_method, min_coverage, min_allele_frequency, max_missingness_rate_per_locus, pool_sizes, min_depth_below_which_are_missing, max_depth_above_which_are_missing, frac_top_missing_pools, frac_top_missing_loci, window_size_bp, min_loci_per_window, min_loci_corr, max_pool_dist, optimise_for_thresholds, optimise_n_steps_corr, optimise_n_steps_dist, optimise_n_reps, n_threads, fname_out_prefix) .Call(wrap__impute, fname, imputation_method, min_coverage, min_allele_frequency, max_missingness_rate_per_locus, pool_sizes, min_depth_below_which_are_missing, max_depth_above_which_are_missing, frac_top_missing_pools, frac_top_missing_loci, window_size_bp, min_loci_per_window, min_loci_corr, max_pool_dist, optimise_for_thresholds, optimise_n_steps_corr, optimise_n_steps_dist, optimise_n_reps, n_threads, fname_out_prefix)
+
+
+# nolint end

README.md

@@ -1,10 +1,10 @@
-# imputepolypools
+# imputef
 
 Reduce genotype data sparsity through imputation of genotype classes or allele frequencies of individual polyploids or pools of individuals or populations.
 
 |**Build Status**|**License**|
 |:--------------:|:---------:|
-| <a href="https://github.com/jeffersonfparil/imputepolypools/actions"><img src="https://github.com/jeffersonfparil/imputepolypools/actions/workflows/r.yml/badge.svg"></a> | [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) |
+| <a href="https://github.com/jeffersonfparil/imputef/actions"><img src="https://github.com/jeffersonfparil/imputef/actions/workflows/r.yml/badge.svg"></a> | [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) |
 
 ## Manual installation and development tools
 
@@ -13,9 +13,9 @@ Reduce genotype data sparsity through imputation of genotype classes or allele f
 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
 sh ./Miniconda3-latest-Linux-x86_64.sh
 # Download the repo
-git clone https://jeffersonfparil:<API_KEY>@github.com/jeffersonfparil/imputepolypools.git some_branch
+git clone https://jeffersonfparil:<API_KEY>@github.com/jeffersonfparil/imputef.git some_branch
 # Create the development environment
-conda env create -n rustenv --file imputepolypools/tests/rustenv.yml
+conda env create -n rustenv --file imputef/tests/rustenv.yml
 conda activate compare_genomes
 ```
 
@@ -24,16 +24,16 @@ conda activate compare_genomes
 ```R
 usethis::use_git_config(user.name="USERNAME", user.email="[email protected]")
 credentials::set_github_pat() ### Enter access token
-remotes::install_github("jeffersonfparil/imputepolypools")
+remotes::install_github("jeffersonfparil/imputef")
 ```
 
 ## Usage
 
 ```R
-?imputepolypools::mvi
-?imputepolypools::aldknni
-imputepolypools::mvi(fname="tests/test.vcf")
-imputepolypools::aldknni(fname="tests/test.vcf")
+?imputef::mvi
+?imputef::aldknni
+imputef::mvi(fname="tests/test.vcf")
+imputef::aldknni(fname="tests/test.vcf")
 ```
 
 ### Functions
@@ -168,71 +168,71 @@ This is used for genotype classes, i.e., binned allele frequencies: $g = {{1 \ov
 
 ### Autotetraploid (Lucerne) mean absolute error
 
-![mae_barplots](./res/eval/lucerne-Mean_absolute_error.svg)
+![mae_barplots](./res/lucerne-Mean_absolute_error.svg)
 
 ### Pool (Soybean pools) mean absolute error
 
-![mae_barplots](./res/eval/soybean-Mean_absolute_error.svg)
+![mae_barplots](./res/soybean-Mean_absolute_error.svg)
 
 ### Diploid (Zucchini) mean absolute error
 
-![mae_barplots](./res/eval/zucchini-Mean_absolute_error.svg)
+![mae_barplots](./res/zucchini-Mean_absolute_error.svg)
 
 ### Diploid (Apple) mean absolute error
 
-![mae_barplots](./res/eval/apple-Mean_absolute_error.svg)
+![mae_barplots](./res/apple-Mean_absolute_error.svg)
 
 ### Diploid (Grape) mean absolute error
 
-![mae_barplots](./res/eval/grape-Mean_absolute_error.svg)
+![mae_barplots](./res/grape-Mean_absolute_error.svg)
 
 ------------------------------------------------------------------------------------
 ------------------------------------------------------------------------------------
 ------------------------------------------------------------------------------------
 
 ### Autotetraploid (Lucerne) concordance of observed and imputed genotype classes
 
-![concordance_genotype_classes_barplots](./res/eval/lucerne-Concordance.svg)
+![concordance_genotype_classes_barplots](./res/lucerne-Concordance.svg)
 
 ### Pool (Soybean pools) concordance of observed and imputed genotype classes
 
-![concordance_genotype_classes_barplots](./res/eval/soybean-Concordance.svg)
+![concordance_genotype_classes_barplots](./res/soybean-Concordance.svg)
 
 ### Diploid (Zucchini) concordance of observed and imputed genotype classes
 
-![concordance_genotype_classes_barplots](./res/eval/zucchini-Concordance.svg)
+![concordance_genotype_classes_barplots](./res/zucchini-Concordance.svg)
 
 ### Diploid (Apple) concordance of observed and imputed genotype classes
 
-![concordance_genotype_classes_barplots](./res/eval/apple-Concordance.svg)
+![concordance_genotype_classes_barplots](./res/apple-Concordance.svg)
 
 ### Diploid (Grape) concordance of observed and imputed genotype classes
 
-![concordance_genotype_classes_barplots](./res/eval/grape-Concordance.svg)
+![concordance_genotype_classes_barplots](./res/grape-Concordance.svg)
 
 ------------------------------------------------------------------------------------
 ------------------------------------------------------------------------------------
 ------------------------------------------------------------------------------------
 
 ### Autotetraploid (Lucerne) coefficient of determination
 
-![r2_barplots](./res/eval/lucerne-Coefficient_of_determination.svg)
+![r2_barplots](./res/lucerne-Coefficient_of_determination.svg)
 
 ### Pool (Soybean pools) coefficient of determination
 
-![r2_barplots](./res/eval/soybean-Coefficient_of_determination.svg)
+![r2_barplots](./res/soybean-Coefficient_of_determination.svg)
 
 ### Diploid (Zucchini) coefficient of determination
 
-![r2_barplots](./res/eval/zucchini-Coefficient_of_determination.svg)
+![r2_barplots](./res/zucchini-Coefficient_of_determination.svg)
 
 ### Diploid (Apple) coefficient of determination
 
-![r2_barplots](./res/eval/apple-Coefficient_of_determination.svg)
+![r2_barplots](./res/apple-Coefficient_of_determination.svg)
 
 ### Diploid (Grape) coefficient of determination
 
-![r2_barplots](./res/eval/grape-Coefficient_of_determination.svg)
+![r2_barplots](./res/grape-Coefficient_of_determination.svg)