From 211505a076e93db8edbacd8b12b06084e436fe64 Mon Sep 17 00:00:00 2001
From: jeffersonfparil <jeffersonparil@gmail.com>
Date: Thu, 11 Jan 2024 15:02:40 +1100
Subject: [PATCH] updated soybean pool data with hopefully better clustering +
 resolved issue #4

---
 res/bcftools.yml | 279 +++++++++++++++++++++++++++++++++++++++++++++++
 res/perf.R       |  44 ++++++--
 res/perf.md      |  27 ++---
 3 files changed, 326 insertions(+), 24 deletions(-)
 create mode 100644 res/bcftools.yml

diff --git a/res/bcftools.yml b/res/bcftools.yml
new file mode 100644
index 0000000..d679e26
--- /dev/null
+++ b/res/bcftools.yml
@@ -0,0 +1,279 @@
+name: bcftools
+channels:
+  - bioconda
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - _r-mutex=1.0.1=anacondar_1
+  - alsa-lib=1.2.8=h166bdaf_0
+  - arpack=3.7.0=hdefa2d7_2
+  - bat=0.23.0=h0f5d50b_0
+  - bcftools=1.16=hfe4b78e_1
+  - binutils_impl_linux-64=2.39=he00db2b_1
+  - biopython=1.80=py311hd4cff14_0
+  - bwa=0.7.17=h7132678_9
+  - bwidget=1.9.14=ha770c72_1
+  - bzip2=1.0.8=h7f98852_4
+  - c-ares=1.18.1=h7f98852_0
+  - ca-certificates=2023.11.17=hbcca054_0
+  - cairo=1.16.0=ha61ee94_1014
+  - coreutils=8.25=1
+  - curl=7.87.0=h6312ad2_0
+  - dbus=1.13.6=h5008d03_3
+  - expat=2.5.0=h27087fc_0
+  - fastqc-rs=0.3.2=he9f29cb_1
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=hab24e00_0
+  - fontconfig=2.14.1=hc2a2eb6_0
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - freetype=2.12.1=hca18f0e_1
+  - fribidi=1.0.10=h36c2ea0_0
+  - gatk4=4.3.0.0=py36hdfd78af_0
+  - gcc_impl_linux-64=12.2.0=hcc96c02_19
+  - gettext=0.21.1=h27087fc_0
+  - gfortran_impl_linux-64=12.2.0=h55be85b_19
+  - giflib=5.2.1=h36c2ea0_2
+  - git=2.39.1=pl5321ha3eba64_0
+  - glib=2.74.1=h6239696_1
+  - glib-tools=2.74.1=h6239696_1
+  - gmp=6.2.1=h58526e2_0
+  - graphite2=1.3.13=h58526e2_1001
+  - gsl=2.7=he838d99_0
+  - gxx_impl_linux-64=12.2.0=hcc96c02_19
+  - harfbuzz=6.0.0=h8e241bc_0
+  - htop=3.2.2=h8228510_0
+  - htslib=1.16=h6bc39ce_0
+  - icu=70.1=h27087fc_0
+  - java-jdk=7.0.91=1
+  - jpeg=9e=h166bdaf_2
+  - julia=1.8.5=h783901f_0
+  - kernel-headers_linux-64=2.6.32=he073ed8_15
+  - keyutils=1.6.1=h166bdaf_0
+  - krb5=1.20.1=hf9c8cef_0
+  - lcms2=2.14=h6ed2654_0
+  - ld_impl_linux-64=2.39=hcc3a1bd_1
+  - lerc=4.0.0=h27087fc_0
+  - libblas=3.9.0=16_linux64_openblas
+  - libcblas=3.9.0=16_linux64_openblas
+  - libcups=2.3.3=h36d4200_3
+  - libcurl=7.87.0=h6312ad2_0
+  - libdeflate=1.13=h166bdaf_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=h516909a_1
+  - libffi=3.4.2=h7f98852_5
+  - libgcc=7.2.0=h69d50b8_2
+  - libgcc-devel_linux-64=12.2.0=h3b97bd3_19
+  - libgcc-ng=12.2.0=h65d4601_19
+  - libgfortran-ng=12.2.0=h69a702a_19
+  - libgfortran5=12.2.0=h337968e_19
+  - libgit2=1.5.1=ha98c156_0
+  - libglib=2.74.1=h606061b_1
+  - libgomp=12.2.0=h65d4601_19
+  - libhwloc=2.8.0=h32351e8_1
+  - libiconv=1.17=h166bdaf_0
+  - liblapack=3.9.0=16_linux64_openblas
+  - libnghttp2=1.51.0=hdcd2b5c_0
+  - libnl=3.8.0=hd590300_0
+  - libnsl=2.0.0=h7f98852_0
+  - libopenblas=0.3.21=pthreads_h78a6416_3
+  - libopenblas-ilp64=0.3.21=pthreads_h44c56fb_3
+  - libpng=1.6.39=h753d276_0
+  - libsanitizer=12.2.0=h46fd767_19
+  - libsqlite=3.40.0=h753d276_0
+  - libssh2=1.10.0=haa6b8db_3
+  - libstdcxx-devel_linux-64=12.2.0=h3b97bd3_19
+  - libstdcxx-ng=12.2.0=h46fd767_19
+  - libtiff=4.4.0=h0e0dad5_3
+  - libunwind=1.6.2=h9c3ff4c_0
+  - libutf8proc=2.8.0=h166bdaf_0
+  - libuuid=2.32.1=h7f98852_1000
+  - libwebp-base=1.2.4=h166bdaf_0
+  - libxcb=1.13=h7f98852_1004
+  - libxml2=2.10.3=h7463322_0
+  - libzlib=1.2.13=h166bdaf_4
+  - make=4.3=hd18ef5c_1
+  - mbedtls=3.3.0=hcb278e6_0
+  - metis=5.1.0=h58526e2_1006
+  - mpfr=4.1.0=h9202a9a_1
+  - ncurses=6.3=h27087fc_1
+  - nextflow=22.10.4=h4a94de4_0
+  - numpy=1.24.1=py311hbde0eaa_0
+  - openblas-ilp64=0.3.21=pthreads_h3d04fff_3
+  - openjdk=11.0.13=h87a67e3_0
+  - openlibm=0.8.1=h7f98852_0
+  - openssl=1.1.1w=hd590300_0
+  - p7zip=16.02=h9c3ff4c_1001
+  - pandoc=3.1.3=h32600fe_0
+  - pango=1.50.12=hd33c08f_1
+  - parallel=20221122=ha770c72_0
+  - pcre2=10.40=hc3806b6_0
+  - perl=5.32.1=2_h7f98852_perl5
+  - picard=2.27.5=hdfd78af_0
+  - pip=22.3.1=pyhd8ed1ab_0
+  - pixman=0.40.0=h36c2ea0_0
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - python=3.11.0=h10a6764_1_cpython
+  - python_abi=3.11=3_cp311
+  - r-ape=5.7_1=r42h08d816e_1
+  - r-argparse=2.2.2=r42hc72bb7e_1
+  - r-askpass=1.2.0=r42h57805ef_0
+  - r-assertthat=0.2.1=r42hc72bb7e_4
+  - r-backports=1.4.1=r42h57805ef_2
+  - r-base=4.2.2=h6b4767f_2
+  - r-base64enc=0.1_3=r42h57805ef_1006
+  - r-brew=1.0_8=r42hc72bb7e_2
+  - r-brio=1.1.3=r42h57805ef_2
+  - r-bslib=0.5.1=r42hc72bb7e_0
+  - r-cachem=1.0.8=r42h57805ef_1
+  - r-callr=3.7.3=r42hc72bb7e_1
+  - r-cli=3.6.1=r42ha503ecb_1
+  - r-clipr=0.8.0=r42hc72bb7e_2
+  - r-cluster=2.1.4=r42h8da6f51_0
+  - r-codetools=0.2_19=r42hc72bb7e_1
+  - r-commonmark=1.9.0=r42h57805ef_1
+  - r-cpp11=0.4.6=r42hc72bb7e_0
+  - r-crayon=1.5.2=r42hc72bb7e_2
+  - r-credentials=2.0.1=r42hc72bb7e_0
+  - r-curl=4.3.3=r42h06615bd_1
+  - r-desc=1.4.2=r42hc72bb7e_2
+  - r-devtools=2.4.5=r42hc72bb7e_2
+  - r-diffobj=0.3.5=r42h57805ef_2
+  - r-digest=0.6.33=r42ha503ecb_0
+  - r-doparallel=1.0.17=r42hc72bb7e_2
+  - r-downlit=0.4.3=r42hc72bb7e_0
+  - r-dplyr=1.1.3=r42ha503ecb_0
+  - r-ellipsis=0.3.2=r42h57805ef_2
+  - r-evaluate=0.22=r42hc72bb7e_0
+  - r-fansi=1.0.5=r42h57805ef_0
+  - r-fastmap=1.1.1=r42ha503ecb_1
+  - r-findpython=1.0.8=r42hc72bb7e_1
+  - r-fontawesome=0.5.2=r42hc72bb7e_0
+  - r-foreach=1.5.2=r42hc72bb7e_2
+  - r-fs=1.6.3=r42ha503ecb_0
+  - r-generics=0.1.3=r42hc72bb7e_2
+  - r-gert=1.9.2=r42hf3f2ec2_0
+  - r-gh=1.4.0=r42hc72bb7e_1
+  - r-gitcreds=0.1.2=r42hc72bb7e_2
+  - r-glue=1.6.2=r42h57805ef_2
+  - r-highr=0.10=r42hc72bb7e_1
+  - r-htmltools=0.5.7=r42ha503ecb_0
+  - r-htmlwidgets=1.6.2=r42hc72bb7e_1
+  - r-httpuv=1.6.12=r42ha503ecb_0
+  - r-httr=1.4.7=r42hc72bb7e_0
+  - r-httr2=0.2.3=r42hc72bb7e_1
+  - r-ini=0.3.1=r42hc72bb7e_1005
+  - r-iterators=1.0.14=r42hc72bb7e_2
+  - r-jquerylib=0.1.4=r42hc72bb7e_2
+  - r-jsonlite=1.8.7=r42h57805ef_0
+  - r-knitr=1.45=r42hc72bb7e_0
+  - r-later=1.3.1=r42ha503ecb_1
+  - r-lattice=0.21_9=r42h57805ef_0
+  - r-lifecycle=1.0.3=r42hc72bb7e_2
+  - r-magrittr=2.0.3=r42h57805ef_2
+  - r-mass=7.3_60=r42h57805ef_1
+  - r-matrix=1.6_1.1=r42h316c678_0
+  - r-memoise=2.0.1=r42hc72bb7e_2
+  - r-memuse=4.2_3=r42h57805ef_1
+  - r-mgcv=1.9_0=r42h316c678_0
+  - r-mime=0.12=r42h57805ef_2
+  - r-miniui=0.1.1.1=r42hc72bb7e_1004
+  - r-nlme=3.1_162=r42hac0b197_0
+  - r-openssl=2.0.5=r42hb1dc35e_0
+  - r-permute=0.9_7=r42hc72bb7e_2
+  - r-pillar=1.9.0=r42hc72bb7e_1
+  - r-pinfsc50=1.2.0=r42ha770c72_2
+  - r-pkgbuild=1.4.2=r42hc72bb7e_0
+  - r-pkgconfig=2.0.3=r42hc72bb7e_3
+  - r-pkgdown=2.0.7=r42hc72bb7e_1
+  - r-pkgload=1.3.3=r42hc72bb7e_0
+  - r-praise=1.0.0=r42hc72bb7e_1007
+  - r-prettyunits=1.2.0=r42hc72bb7e_0
+  - r-processx=3.8.2=r42h57805ef_0
+  - r-profvis=0.3.8=r42h57805ef_3
+  - r-promises=1.2.1=r42ha503ecb_0
+  - r-ps=1.7.5=r42h57805ef_1
+  - r-purrr=1.0.2=r42h57805ef_0
+  - r-r6=2.5.1=r42hc72bb7e_2
+  - r-ragg=1.2.4=r42hc1f6985_0
+  - r-rappdirs=0.3.3=r42h57805ef_2
+  - r-rcmdcheck=1.4.0=r42h785f33e_2
+  - r-rcpp=1.0.11=r42h7df8631_0
+  - r-rematch2=2.1.2=r42hc72bb7e_3
+  - r-remotes=2.4.2.1=r42hc72bb7e_0
+  - r-rlang=1.1.1=r42ha503ecb_1
+  - r-rmarkdown=2.25=r42hc72bb7e_0
+  - r-roxygen2=7.2.3=r42ha503ecb_1
+  - r-rprojroot=2.0.3=r42hc72bb7e_1
+  - r-rstudioapi=0.15.0=r42hc72bb7e_0
+  - r-rversions=2.1.2=r42hc72bb7e_2
+  - r-sass=0.4.7=r42ha503ecb_0
+  - r-sessioninfo=1.2.2=r42hc72bb7e_2
+  - r-shiny=1.7.5.1=r42h785f33e_0
+  - r-sourcetools=0.1.7_1=r42ha503ecb_1
+  - r-stringi=1.7.12=r42h1ae9187_0
+  - r-stringr=1.5.0=r42h785f33e_1
+  - r-sys=3.4.2=r42h57805ef_1
+  - r-systemfonts=1.0.5=r42haf97adc_0
+  - r-testthat=3.2.0=r42ha503ecb_0
+  - r-textshaping=0.3.6=r42hbb20487_4
+  - r-tibble=3.2.1=r42h57805ef_2
+  - r-tidyselect=1.2.0=r42hc72bb7e_1
+  - r-tinytex=0.48=r42hc72bb7e_1
+  - r-txtplot=1.0_4=r42h142f84f_0
+  - r-urlchecker=1.0.1=r42hc72bb7e_2
+  - r-usethis=2.2.2=r42hc72bb7e_0
+  - r-utf8=1.2.3=r42h57805ef_1
+  - r-vcfr=1.14.0=r42h33b523d_1
+  - r-vctrs=0.6.3=r42ha503ecb_0
+  - r-vegan=2.6_4=r42hb20cf53_0
+  - r-viridislite=0.4.2=r42hc72bb7e_1
+  - r-waldo=0.5.1=r42hc72bb7e_1
+  - r-whisker=0.4.1=r42hc72bb7e_1
+  - r-withr=2.5.1=r42hc72bb7e_0
+  - r-xfun=0.41=r42ha503ecb_0
+  - r-xml2=1.3.3=r42h044e5c7_2
+  - r-xopen=1.0.0=r42hc72bb7e_1005
+  - r-xtable=1.8_4=r42hc72bb7e_5
+  - r-yaml=2.3.7=r42h57805ef_1
+  - r-zip=2.3.0=r42h57805ef_1
+  - readline=8.1.2=h0f457ee_0
+  - rust=1.74.0=h70c747d_0
+  - rust-std-x86_64-unknown-linux-gnu=1.74.0=h2c6d0dc_0
+  - samtools=1.16.1=h6899075_1
+  - sed=4.8=he412f7d_0
+  - setuptools=66.1.0=pyhd8ed1ab_0
+  - suitesparse=5.10.1=h9e50725_1
+  - sysroot_linux-64=2.12=he073ed8_15
+  - tbb=2021.7.0=h924138e_1
+  - tk=8.6.12=h27826a3_0
+  - tktable=2.10=hb7b940f_3
+  - trimmomatic=0.39=hdfd78af_2
+  - tzdata=2022g=h191b570_0
+  - wheel=0.38.4=pyhd8ed1ab_0
+  - xorg-fixesproto=5.0=h7f98852_1002
+  - xorg-inputproto=2.3.2=h7f98852_1002
+  - xorg-kbproto=1.0.7=h7f98852_1002
+  - xorg-libice=1.0.10=h7f98852_0
+  - xorg-libsm=1.2.3=hd9c2040_1000
+  - xorg-libx11=1.7.2=h7f98852_0
+  - xorg-libxau=1.0.9=h7f98852_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xorg-libxext=1.3.4=h7f98852_1
+  - xorg-libxfixes=5.0.3=h7f98852_1004
+  - xorg-libxi=1.7.10=h7f98852_0
+  - xorg-libxrender=0.9.10=h7f98852_1003
+  - xorg-libxt=1.2.1=h7f98852_2
+  - xorg-libxtst=1.2.3=h7f98852_1002
+  - xorg-recordproto=1.14.2=h7f98852_1002
+  - xorg-renderproto=0.11.1=h7f98852_1002
+  - xorg-xextproto=7.3.0=h7f98852_1002
+  - xorg-xproto=7.0.31=h7f98852_1007
+  - xz=5.2.6=h166bdaf_0
+  - zlib=1.2.13=h166bdaf_4
+  - zstd=1.5.2=h3eb15da_6
+
diff --git a/res/perf.R b/res/perf.R
index 6473947..828b291 100644
--- a/res/perf.R
+++ b/res/perf.R
@@ -122,19 +122,49 @@ fn_simulate_missing_data = function(vcf, mat_genotypes, maf=0.25, missing_rate=0
     ))
 }
 ### Imputation accuracy metrics
-fn_metrics = function(y_predicted, y_expected) {
-    deviation = y_predicted - y_expected
+fn_metrics = function(q_predicted, q_expected) {
+    ## Overall metrics
+    deviation = q_predicted - q_expected
     mae = mean(abs(deviation), na.rm=TRUE)
     mse = mean(deviation^2, na.rm=TRUE)
     rmse = sqrt(mse)
-    r2 = 1.00 - (mean(mse, na.rm=TRUE) / mean((y_expected-mean(y_expected))^2, na.rm=TRUE))
-    concordance = mean(y_predicted == y_expected, na.rm=TRUE)
+    r2 = 1.00 - (mean(mse, na.rm=TRUE) / mean((q_expected-mean(q_expected))^2, na.rm=TRUE))
+    concordance = mean(q_predicted == q_expected, na.rm=TRUE)
+    ### Metrics across the range of expected allele frequencies
+    vec_q_max = c(0.01, 0.05, c(1:9)/10, 0.95, 0.99, 1.00)
+    vec_n = rep(0, each=length(vec_q_max))
+    vec_mae = rep(NA, each=length(vec_q_max))
+    vec_mse = rep(NA, each=length(vec_q_max))
+    vec_rmse = rep(NA, each=length(vec_q_max))
+    vec_r2 = rep(NA, each=length(vec_q_max))
+    vec_concordance = rep(NA, each=length(vec_q_max))
+    for (i in 1:length(vec_q_max)) {
+        # i = 1
+        if (i==1) {
+            q_min = 0.0
+        } else {
+            q_min = vec_q_max[i-1]
+        }
+        q_max = vec_q_max[i]
+        idx = which((q_expected > q_min) & (q_expected <= q_max))
+        if (length(idx) > 0) {
+            vec_n[i] = length(idx)
+            vec_deviations = q_predicted[idx] - q_expected[idx]
+            vec_mae[i] = mean(abs(vec_deviations), na.rm=TRUE)
+            vec_mse[i] = mean(vec_deviations^2, na.rm=TRUE)
+            vec_rmse[i] = sqrt(vec_mse[i])
+            vec_r2[i] = 1.00 - (mean(vec_mse[i], na.rm=TRUE) / mean((q_expected[idx]-mean(q_expected[idx]))^2, na.rm=TRUE))
+            vec_concordance[i] = mean(q_predicted[idx] == q_expected[idx], na.rm=TRUE)
+        }
+    }
+    df_metrics_across_allele_freqs = data.frame(q=vec_q_max, n=vec_n, mae=vec_mae, mse=vec_mse, rmse=vec_rmse, r2=vec_r2, concordance=vec_concordance)
     return(list(
         mae=mae,
         mse=mse,
         rmse=rmse,
         r2=r2,
-        concordance=concordance
+        concordance=concordance,
+        df_metrics_across_allele_freqs=df_metrics_across_allele_freqs
     ))
 }
 ### Assess imputation accuracies
@@ -196,11 +226,11 @@ fn_imputation_accuracy = function(fname_imputed, list_sim_missing, ploidy=4, str
     # print(paste0("Total number of simulated missing data = ", n_missing))
     # print(paste0("Total number of imputed missing data = ", n_imputed))
     ### Metrics using allele frequencies
-    metrics_allele_frequencies = fn_metrics(y_predicted=vec_imputed, y_expected=list_sim_missing$expected_allele_frequencies)
+    metrics_allele_frequencies = fn_metrics(q_predicted=vec_imputed, q_expected=list_sim_missing$expected_allele_frequencies)
     ### Metrics using genotype classes
     vec_expected_classes = fn_classify_allele_frequencies(mat_genotypes=list_sim_missing$expected_allele_frequencies, ploidy=ploidy, strict_boundaries=strict_boundaries)
     vec_imputed_classes = fn_classify_allele_frequencies(mat_genotypes=vec_imputed, ploidy=ploidy, strict_boundaries=strict_boundaries)
-    metrics_genotype_classes = fn_metrics(y_predicted=vec_imputed_classes, y_expected=vec_expected_classes)
+    metrics_genotype_classes = fn_metrics(q_predicted=vec_imputed_classes, q_expected=vec_expected_classes)
     return(list(
         frac_imputed = n_imputed / n_missing,
         mae_freqs = metrics_allele_frequencies$mae,
diff --git a/res/perf.md b/res/perf.md
index 3bca906..46764b3 100644
--- a/res/perf.md
+++ b/res/perf.md
@@ -8,13 +8,10 @@
     + **ALDKNNI_OPTIM_COUNTS**: adaptive LD-kNN imputation with optimisation for the number of linked loci correlation and k-nearest neighbours
     + **MVI**: mean value imputation
     <!-- + **SAMP**: Luke's LD-kNN imputation algorithm vias sampling from a normal distribution whose parameters depend on the k-nearest neighbours -->
-- 4 minor allele frequency thresholds:
+- 2 minor allele frequency thresholds:
     + 0.01
     + 0.05
-    + 0.10
-    + 0.25
-- 11 sparsity levels (missing rate):
-    + 0.0017
+- 10 sparsity levels (missing rate):
     + 0.01
     + 0.1
     + 0.2
@@ -30,6 +27,10 @@
     + pools of diploid *Glycine max* (2n=2x=20; 1.15 Gb genome; 478 pools (each pool comprised of 42 individuals) x 39,636 biallelic loci; source: [http://gong_lab.hzau.edu.cn/Plant_imputeDB/#!/download_soybean](http://gong_lab.hzau.edu.cn/Plant_imputeDB/#!/download_soybean))
     + diploid *Vitis vinifera* (2n=2x=38; 0.5 Gb genome; 77 samples x 8,506 biallelic loci; source: [https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/g3journal/5/11/10.1534_g3.115.021667/5/021667_files1.zip?Expires=1706750617&Signature=yMBQeDumKhnNHIhhUdwsdac~D81t~5RfRi39Bqs4fA8sdE27FVMiyYI7xL8OvLupTqXUim2qC5mgvd5eqby4WCWxCw8x25xnkd6~05gC6puXpHloQSbesTQGrTFios7JeCnXUf306Z~p2vMi0TRgX8qpNTWiwGwwyn2wYAr1tbWIN4EwTQvN8~BgJF31Tj8xJoCVJm2uTpA7~hhsSidJgxVqL4aO20CvwAI1iDcx1gxvienNDS1rYTOruLhwXDif4RGFv8tAb2W5SK3qt4bjgpD6mP8gghv7BWGf0g-arYQywL1fmLCia35qJr7Umxc3LM8iPvWabo5K0sTlRH1oHw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA](https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/g3journal/5/11/10.1534_g3.115.021667/5/021667_files1.zip?Expires=1706750617&Signature=yMBQeDumKhnNHIhhUdwsdac~D81t~5RfRi39Bqs4fA8sdE27FVMiyYI7xL8OvLupTqXUim2qC5mgvd5eqby4WCWxCw8x25xnkd6~05gC6puXpHloQSbesTQGrTFios7JeCnXUf306Z~p2vMi0TRgX8qpNTWiwGwwyn2wYAr1tbWIN4EwTQvN8~BgJF31Tj8xJoCVJm2uTpA7~hhsSidJgxVqL4aO20CvwAI1iDcx1gxvienNDS1rYTOruLhwXDif4RGFv8tAb2W5SK3qt4bjgpD6mP8gghv7BWGf0g-arYQywL1fmLCia35qJr7Umxc3LM8iPvWabo5K0sTlRH1oHw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA))
 
+- **Genome partitioning:**
+    + **By chromosomes or scaffolds**
+    + **Rename all chromosomes as chr1**
+
 *prepping_datasets.sh*
 
 ```shell
@@ -83,25 +84,17 @@ rownames(G) = vec_loci_names
 colnames(G) = vec_sample_names
 ### Create n pools of the most related individuals
 print("Clustering. This may take a while.")
-n = length(vec_sample_names)
-for (i in )
-
-
-
-
+PCA = prcomp(G, rank=100)
+C = PCA$rotation
 p = length(vec_loci_names)
-C = t(G[seq(from=1, to=p, length=1000), ])
-clustering = kmeans(x=C, centers=200)
+# C = t(G[seq(from=1, to=p, length=1000), ])
+clustering = kmeans(x=C, centers=200, iter.max=20)
 vec_clusters = table(clustering$cluster)
 vec_clusters = vec_clusters[vec_clusters >= min_pool_size]
 n = length(vec_clusters)
 GT = matrix("AD", nrow=p, ncol=n+1)
 pb = txtProgressBar(min=0, max=n, initial=0, style=3)
 for (i in 1:n) {
-    # i = 1
-    # ini = ((i-1)*min_pool_size) + 1
-    # fin = ((i-1)*min_pool_size) + min_pool_size
-    # q = rowMeans(G[, ini:fin])
     idx = which(clustering$cluster == i)
     q = rowMeans(G[, idx])
     for (j in 1:p) {