From e7872fdd124645be61d44ddebe3b094794161259 Mon Sep 17 00:00:00 2001
From: Kasper Skytte Andersen <github@kasper.slmail.me>
Date: Fri, 16 Feb 2024 14:40:42 +0100
Subject: [PATCH] some additions for the manuscript

---
 Pipfile               |   22 +
 Pipfile.lock          | 1407 +++++++++++++++++++++++++++++++++++++++++
 analysis/analysis.Rmd |  222 +++++--
 analysis/functions.R  |   22 +-
 4 files changed, 1620 insertions(+), 53 deletions(-)
 create mode 100644 Pipfile
 create mode 100644 Pipfile.lock

diff --git a/Pipfile b/Pipfile
new file mode 100644
index 0000000..3b1d854
--- /dev/null
+++ b/Pipfile
@@ -0,0 +1,22 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+tensorflow = "==2.13.0"
+numpy = "*"
+pandas = "*"
+argparse = "*"
+keras = "*"
+scipy = "*"
+scikit-learn = "*"
+matplotlib = "*"
+pathlib = "*"
+seaborn = "*"
+radian = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.8.10"
\ No newline at end of file
diff --git a/Pipfile.lock b/Pipfile.lock
new file mode 100644
index 0000000..86c0d18
--- /dev/null
+++ b/Pipfile.lock
@@ -0,0 +1,1407 @@
+{
+  "_meta": {
+      "hash": {
+          "sha256": "b12e369cc468fc1c58f79ea0d956e4839994391aefadc955d4ade4b08c6d377f"
+      },
+      "pipfile-spec": 6,
+      "requires": {
+          "python_version": "3.8.10"
+      },
+      "sources": [
+          {
+              "name": "pypi",
+              "url": "https://pypi.org/simple",
+              "verify_ssl": true
+          }
+      ]
+  },
+  "default": {
+      "absl-py": {
+          "hashes": [
+              "sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3",
+              "sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5"
+          ],
+          "markers": "python_version >= '3.7'",
+          "version": "==2.0.0"
+      },
+      "argparse": {
+          "hashes": [
+              "sha256:62b089a55be1d8949cd2bc7e0df0bddb9e028faefc8c32038cc84862aefdd6e4",
+              "sha256:c31647edb69fd3d465a847ea3157d37bed1f95f19760b11a47aa91c04b666314"
+          ],
+          "index": "pypi",
+          "version": "==1.4.0"
+      },
+      "astunparse": {
+          "hashes": [
+              "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872",
+              "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"
+          ],
+          "version": "==1.6.3"
+      },
+      "cachetools": {
+          "hashes": [
+              "sha256:95ef631eeaea14ba2e36f06437f36463aac3a096799e876ee55e5cdccb102590",
+              "sha256:dce83f2d9b4e1f732a8cd44af8e8fab2dbe46201467fc98b3ef8f269092bf62b"
+          ],
+          "markers": "python_version >= '3.7'",
+          "version": "==5.3.1"
+      },
+      "certifi": {
+          "hashes": [
+              "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082",
+              "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"
+          ],
+          "markers": "python_version >= '3.6'",
+          "version": "==2023.7.22"
+      },
+      "cffi": {
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+              "sha256:5c5aa28df055697d7c37d2099a7bc09f559d5053c3349b1ad0c39000e611d317",
+              "sha256:5fc8e02f5984a55d2c653f5fea93531e9836abbd84342c1d1e17abc4a15084c2",
+              "sha256:63424c681923b9f3bfbc5e3205aafe790904053d42ddcc08542181a30a7a51bd",
+              "sha256:64b1df0f83706b4ef4cfb4fb0e4c2669100fd7ecacfb59e091fad300d4e04640",
+              "sha256:74934ebd71950e3db69960a7da29204f89624dde411afbfb3b4858c1409b1e98",
+              "sha256:75669d77bb2c071333417617a235324a1618dba66f82a750362eccbe5b61d248",
+              "sha256:75760a47c06b5974aa5e01949bf7e66d2af4d08cb8c1d6516af5e39595397f5e",
+              "sha256:76407ab327158c510f44ded207e2f76b657303e17cb7a572ffe2f5a8a48aa04d",
+              "sha256:76e9c727a874b4856d11a32fb0b389afc61ce8aaf281ada613713ddeadd1cfec",
+              "sha256:77d4c1b881076c3ba173484dfa53d3582c1c8ff1f914c6461ab70c8428b796c1",
+              "sha256:780c82a41dc493b62fc5884fb1d3a3b81106642c5c5c78d6a0d4cbe96d62ba7e",
+              "sha256:7dc0713bf81287a00516ef43137273b23ee414fe41a3c14be10dd95ed98a2df9",
+              "sha256:7eebcdbe3677e58dd4c0e03b4f2cfa346ed4049687d839adad68cc38bb559c92",
+              "sha256:896689fddba4f23ef7c718279e42f8834041a21342d95e56922e1c10c0cc7afb",
+              "sha256:96177eb5645b1c6985f5c11d03fc2dbda9ad24ec0f3a46dcce91445747e15094",
+              "sha256:96e25c8603a155559231c19c0349245eeb4ac0096fe3c1d0be5c47e075bd4f46",
+              "sha256:9d37ac69edc5614b90516807de32d08cb8e7b12260a285ee330955604ed9dd29",
+              "sha256:9ed6aa0726b9b60911f4aed8ec5b8dd7bf3491476015819f56473ffaef8959bd",
+              "sha256:a487f72a25904e2b4bbc0817ce7a8de94363bd7e79890510174da9d901c38705",
+              "sha256:a4cbb9ff5795cd66f0066bdf5947f170f5d63a9274f99bdbca02fd973adcf2a8",
+              "sha256:a74d56552ddbde46c246b5b89199cb3fd182f9c346c784e1a93e4dc3f5ec9975",
+              "sha256:a89ce3fd220ff144bd9d54da333ec0de0399b52c9ac3d2ce34b569cf1a5748fb",
+              "sha256:abd52a09d03adf9c763d706df707c343293d5d106aea53483e0ec8d9e310ad5e",
+              "sha256:abd8f36c99512755b8456047b7be10372fca271bf1467a1caa88db991e7c421b",
+              "sha256:af5bd9ccb188f6a5fdda9f1f09d9f4c86cc8a539bd48a0bfdc97723970348418",
+              "sha256:b02f21c1e2074943312d03d243ac4388319f2456576b2c6023041c4d57cd7019",
+              "sha256:b06fa97478a5f478fb05e1980980a7cdf2712015493b44d0c87606c1513ed5b1",
+              "sha256:b0724f05c396b0a4c36a3226c31648385deb6a65d8992644c12a4963c70326ba",
+              "sha256:b130fe77361d6771ecf5a219d8e0817d61b236b7d8b37cc045172e574ed219e6",
+              "sha256:b56d5519e470d3f2fe4aa7585f0632b060d532d0696c5bdfb5e8319e1d0f69a2",
+              "sha256:b67b819628e3b748fd3c2192c15fb951f549d0f47c0449af0764d7647302fda3",
+              "sha256:ba1711cda2d30634a7e452fc79eabcadaffedf241ff206db2ee93dd2c89a60e7",
+              "sha256:bbeccb1aa40ab88cd29e6c7d8585582c99548f55f9b2581dfc5ba68c59a85752",
+              "sha256:bd84395aab8e4d36263cd1b9308cd504f6cf713b7d6d3ce25ea55670baec5416",
+              "sha256:c99f4309f5145b93eca6e35ac1a988f0dc0a7ccf9ccdcd78d3c0adf57224e62f",
+              "sha256:ca1cccf838cd28d5a0883b342474c630ac48cac5df0ee6eacc9c7290f76b11c1",
+              "sha256:cd525e0e52a5ff16653a3fc9e3dd827981917d34996600bbc34c05d048ca35cc",
+              "sha256:cdb4f085756c96a3af04e6eca7f08b1345e94b53af8921b25c72f096e704e145",
+              "sha256:ce42618f67741d4697684e501ef02f29e758a123aa2d669e2d964ff734ee00ee",
+              "sha256:d06730c6aed78cee4126234cf2d071e01b44b915e725a6cb439a879ec9754a3a",
+              "sha256:d5fe3e099cf07d0fb5a1e23d399e5d4d1ca3e6dfcbe5c8570ccff3e9208274f7",
+              "sha256:d6bcbfc99f55655c3d93feb7ef3800bd5bbe963a755687cbf1f490a71fb7794b",
+              "sha256:d787272ed958a05b2c86311d3a4135d3c2aeea4fc655705f074130aa57d71653",
+              "sha256:e169e957c33576f47e21864cf3fc9ff47c223a4ebca8960079b8bd36cb014fd0",
+              "sha256:e20076a211cd6f9b44a6be58f7eeafa7ab5720eb796975d0c03f05b47d89eb90",
+              "sha256:e826aadda3cae59295b95343db8f3d965fb31059da7de01ee8d1c40a60398b29",
+              "sha256:eef4d64c650f33347c1f9266fa5ae001440b232ad9b98f1f43dfe7a79435c0a6",
+              "sha256:f2e69b3ed24544b0d3dbe2c5c0ba5153ce50dcebb576fdc4696d52aa22db6034",
+              "sha256:f87ec75864c37c4c6cb908d282e1969e79763e0d9becdfe9fe5473b7bb1e5f09",
+              "sha256:fbec11614dba0424ca72f4e8ba3c420dba07b4a7c206c8c8e4e73f2e98f4c559",
+              "sha256:fd69666217b62fa5d7c6aa88e507493a34dec4fa20c5bd925e4bc12fce586639"
+          ],
+          "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
+          "version": "==1.15.0"
+      },
+      "zipp": {
+          "hashes": [
+              "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31",
+              "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"
+          ],
+          "markers": "python_version < '3.10'",
+          "version": "==3.17.0"
+      }
+  },
+  "develop": {}
+}
\ No newline at end of file
diff --git a/analysis/analysis.Rmd b/analysis/analysis.Rmd
index 0b8b4d2..0eebd15 100755
--- a/analysis/analysis.Rmd
+++ b/analysis/analysis.Rmd
@@ -71,39 +71,91 @@ as.data.frame(readstats, check.names = FALSE)
 ```{r}
 bp_all_list <- boxplot_all(
   results_batch_dir,
-  save = FALSE
+  save = TRUE
 )
 
-# customize axis breaks a bit for the specific data
-bp_all_list[["Bray Curtis"]] <- bp_all_list[["Bray Curtis"]] + 
-  scale_y_continuous(
-    trans = "sqrt",
-    breaks = c(0.5, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8)
-  )
-bp_all_list[["Mean Absolute Error"]] <- bp_all_list[["Mean Absolute Error"]] + 
-  scale_y_continuous(
-    trans = "sqrt",
-    breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1.0, 2, 3, 4, 5, 6)
+
+## Bray-Curtis only, flip x and y (otherwise too big plot for manuscript)
+runs <- list.dirs(
+  results_batch_dir,
+  full.names = TRUE,
+  recursive = FALSE
+)
+
+d_list <- lapply(
+  runs,
+  read_performance,
+  add_dataset_info = "numsamples"
+)
+names(d_list) <- runs
+combined <- rbindlist(
+  d_list,
+  idcol = "results_folder",
+  fill = TRUE
+)[
+  !is.na(cluster_type) & value > 0
+]
+
+# copied from boxplot_all() just for this plot
+#order datasets by the number of samples (extracted from dataset names)
+combined[,unique(dataset)] %>%
+  stringi::stri_extract_all_regex("\\(.*$", "") %>%
+  stringi::stri_extract_all_regex("[0-9]+") %>%
+  as.numeric -> nsamples
+datasets_ordered <- combined[
+  ,
+  unique(dataset)
+][
+  order(nsamples, decreasing = F)
+]
+combined[, dataset := factor(dataset, levels = datasets_ordered)]
+bp_all_BC <- ggplot(
+  combined[error_metric == "Bray Curtis"],
+  aes(
+    value,
+    cluster_type,
+    color = cluster_type
   )
-bp_all_list[["Mean Squared Error"]] <- bp_all_list[["Mean Squared Error"]] + 
-  scale_y_continuous(
+) +
+  geom_boxplot(
+    outlier.shape = 1,
+    outlier.size = 1
+  ) +
+  facet_grid(
+    rows = vars(dataset),
+    scales = "free"
+  ) +
+  theme_bw() +
+  theme(
+    legend.position = "bottom",
+    legend.title = element_blank(),
+    legend.text = element_text(size = 14),
+    axis.text.y = element_blank(),
+    axis.text.x = element_text(size = 14),
+    axis.title = element_blank(),
+    axis.ticks.y = element_blank(),
+    #strip.text.x = element_blank(),
+    strip.text.y = element_text(size = 14, angle = 0),
+    panel.grid.major.y = element_blank(),
+    panel.grid.minor.x = element_blank(),
+    panel.grid.major.x = element_line(color = "gray70"),
+    panel.background = element_rect(fill = "white")
+  ) +
+  scale_x_continuous(
     trans = "sqrt",
-    breaks = c(0.5, 1, 5, 10, 20, 30, 40)
-  )
+    breaks = c(0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8)
+  ) +
+  scale_color_brewer(palette = "Set2")
 
-# Compose all plots in the list using patchwork
-bp_all_plot <- purrr::reduce(
-  bp_all_list,
-  `/`
-)
 ggsave(
-  file.path(results_batch_dir, "boxplot_all.png"),
-  plot = bp_all_plot,
-  width = 16,
-  height = 12
+  file.path(results_batch_dir, "boxplot_all_BC.png"),
+  plot = bp_all_BC,
+  width = 10,
+  height = 15
 )
 ```
 
+
 ## Check some clusters from AAE
 ```{r}
 plot_performance(
@@ -130,17 +182,43 @@ boxplot_all(
 )
 ```
 
-## Randers
+## Mariagerfjord
 ```{r}
-boxplot_all(
+bp_mar_predwin_list <- boxplot_all(
   "results/predwindow_mariagerfjord",
   add_dataset_info = "predwindow",
-  plot_width = 8,
-  plot_height = 8
+  save = FALSE
+)
+
+
+bp_mar_predwin_list[["Bray Curtis"]] <- bp_mar_predwin_list[["Bray Curtis"]] +
+  scale_y_continuous(
+    trans = "sqrt",
+    breaks = c(0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5, 0.7)
+  ) + expand_limits(y = 0.0)
+bp_mar_predwin_list[["Mean Absolute Error"]] <- bp_mar_predwin_list[["Mean Absolute Error"]] +
+  scale_y_continuous(
+    trans = "sqrt",
+    breaks = c(0.0, 0.5, 1.0, 2.0, 3.0, 4.0)
+  ) + expand_limits(y = 0.0)
+bp_mar_predwin_list[["Mean Squared Error"]] <- bp_mar_predwin_list[["Mean Squared Error"]] +
+  scale_y_continuous(
+    trans = "sqrt",
+    breaks = c(0, 1, 5, 10, 20, 30, 50, 80)
+  ) + expand_limits(y = 0.0)
+bp_mar_predwin_plot <- purrr::reduce(
+  bp_mar_predwin_list,
+  `/`
+)
+ggsave(
+  "results/predwindow_mariagerfjord/boxplot_all.png",
+  plot = bp_mar_predwin_plot,
+  width = 8,
+  height = 10
 )
 ```
 
-## Timeseries examples
+## Timeseries examples (predwindow)
 ```{r}
 dir <- "results/predwindow_mariagerfjord"
 runs <- list.dirs(
@@ -244,14 +322,30 @@ plots <- lapply(
         sample_trajectory_group = "Date"
       ) +
       scale_color_manual(
-        values = c("grey80", RColorBrewer::brewer.pal(3, "Set2")[c(1:3)])
+        values = c("grey80", RColorBrewer::brewer.pal(3, "Set2")[c(2,3,1)]),
+        labels = c(
+          real = "Real",
+          train = "Prediction - Train",
+          val = "Prediction - Validation",
+          test = "Prediction - Test"
+        ),
+        breaks = c(
+          "real",
+          "train",
+          "val",
+          "test"
+        )
       ) +
+      scale_x_continuous(breaks = breaks_pretty(n = 3)) +
+      scale_y_continuous(breaks = breaks_pretty(n = 3)) +
       theme(
         legend.title = element_blank(),
         legend.text = element_text(size = 16),
         axis.text = element_text(size = 16),
-        axis.title = element_text(size = 16)
-      )
+        axis.title = element_text(size = 16),
+        legend.position = "bottom"
+      ) +
+      coord_fixed(ratio = 1)
     
     # real_data <- data.table(plot$data)[predicted == "real"]
     # # extract first and last row (corresponds to first and last sample in time)
@@ -263,13 +357,15 @@ plots <- lapply(
         results_batch_dir,
         paste0("PCoA - ", basename(wwtp), ".png")
       ),
-      plot = plot
+      plot = plot,
+      width = 10,
+      height = 10
     )
     return(plot)
   }
 )
 names(plots) <- basename(wwtp_results)
-hideme <- lapply(plots, print)
+#hideme <- lapply(plots, print)
 ```
 
 # Time Series examples
@@ -364,12 +460,12 @@ d <- combine_abund(
 )
 test_predicted <- amp_filter_samples(
   d,
-  split_dataset %chin% "test",
-  predicted %chin% "predicted"
+  split_dataset %in% "test",
+  predicted %in% "predicted"
 )
 test_real <- amp_filter_samples(
   d,
-  split_dataset %chin% "real",
+  split_dataset %in% "real",
   Date %in% test_predicted$metadata$Date
 )
 test_combined <- amp_merge_ampvis2(
@@ -382,6 +478,29 @@ test_combined$metadata$predicted <- factor(
   levels = c("real", "predicted")
 )
 
+# manually clean up long taxonomy strings and reverse the order
+# this is NOT generic for other datasets, it's just for the manuscript
+clean_tax <- function(charfctr) {
+  charfctr <- gsub(
+    "s__|g__|Ca_[^_]+_|Sulfuritalea_",
+    "",
+    charfctr
+  )
+  charfctr <- sapply(
+    strsplit(
+      charfctr,
+      "; "
+    ),
+    function(x)
+      paste0(rev(x), collapse = "; "),
+    simplify = TRUE
+  )
+  return(charfctr)
+}
+test_combined$tax$OTU <- clean_tax(test_combined$tax$OTU)
+rownames(test_combined$tax) <- clean_tax(rownames(test_combined$tax))
+rownames(test_combined$abund) <- clean_tax(rownames(test_combined$abund))
+
 realvspredictedheatmap <- amp_heatmap(
   test_combined,
   tax_aggregate = "OTU",
@@ -392,12 +511,13 @@ realvspredictedheatmap <- amp_heatmap(
   facet_by = "Date"
 ) +
   theme(
-    strip.text = element_text(angle = 90, size = 12),
-    axis.text.x = element_text(size = 12),
-    axis.text.y = element_text(size = 14),
+    strip.text = element_text(angle = 90, size = 14),
+    axis.text.x = element_text(size = 14),
+    axis.text.y = element_text(size = 16),
     legend.text = element_text(size = 14),
     legend.title = element_text(size = 14)
   )
+
 ggsave(
   plot = realvspredictedheatmap,
   filename = file.path(
@@ -469,18 +589,22 @@ statistics <- lapply(
     if(is.null(combined))
       return(NULL)
     #extract predicted samples (test set only)
-    predicted <- amp_filter_samples(
+    predicted <- suppressMessages({
+      amp_filter_samples(
       combined,
-      split_dataset %chin% "test",
-      predicted %chin% "predicted"
+      split_dataset %in% "test",
+      predicted %in% "predicted"
     )
+    })
     #extract real samples from the same dates as the predicted
     dates <- predicted[["metadata"]][["Date"]]
-    real <- amp_filter_samples(
+    real <- suppressMessages({
+      amp_filter_samples(
       combined,
-      split_dataset %chin% "real",
+      split_dataset %in% "real",
       Date %in% dates
     )
+    })
     combined_test <- amp_merge_ampvis2(
       predicted,
       real,
@@ -564,5 +688,11 @@ combined <- rbindlist(
 ]
 #prediction accuracies for each cluster for each dataset+cluster type+error metric
 stats <- combined[, .(fivenum_stats = fivenum(value)), by = .(dataset, cluster_type, error_metric)]
-stats[error_metric == "Bray Curtis", .(median = .SD[3,fivenum_stats]), by = c("dataset", "cluster_type")]
+
+# extract medians for BC
+mediansBC <- stats[error_metric == "Bray Curtis", .(median = .SD[3,fivenum_stats]), by = c("dataset", "cluster_type")]
+mediansBC[, nsamples := as.integer(gsub("[^0-9]", "", dataset))]
+
+# linear relationship between BC+graph accuracy and number of samples
+summary(lm(nsamples ~ median, data = mediansBC[cluster_type == "Graph"]))
 ```
diff --git a/analysis/functions.R b/analysis/functions.R
index 0fc16b5..bba7bd1 100644
--- a/analysis/functions.R
+++ b/analysis/functions.R
@@ -333,6 +333,7 @@ boxplot_all <- function(
             cols = vars(dataset),
             scales = "free"
           ) +
+          theme_bw() +
           theme(
             legend.position = "none",
             legend.title = element_blank(),
@@ -342,8 +343,9 @@ boxplot_all <- function(
             axis.ticks.x = element_blank(),
             strip.text.x = element_blank(),
             strip.text.y = element_text(size = 10),
+            panel.grid.minor = element_blank(),
             panel.grid.major.x = element_blank(),
-            panel.grid.minor.y = element_blank()
+            panel.grid.major.y = element_line(color = "gray70")
           ) +
           scale_color_brewer(palette = "Set2")
       }
@@ -594,6 +596,12 @@ combine_abund <- function(results_dir, cluster_type) {
     by_refseq = FALSE
   )
 
+  # Order dataset type (split_dataset) by real-train-val-test
+  combined$metadata$split_dataset <- factor(
+    combined$metadata$split_dataset,
+    levels = c("real", "train", "val", "test")
+  )
+
   return(combined)
 }
 
@@ -655,20 +663,20 @@ plot_timeseries <- function(
     values = c(
       "grey10",
       "#E31A1C",
-      "#33A02C",
-      "#1F78B4"
+      "#1F78B4",
+      "#33A02C"
     )[1:data[, length(unique(split_dataset))]],
     labels = c(
       real = "Real",
       train = "Prediction - Train",
-      test = "Prediction - Test",
-      val = "Prediction - Validation"
+      val = "Prediction - Validation",
+      test = "Prediction - Test"
     )[1:data[, length(unique(split_dataset))]],
     breaks = c(
       "real",
       "train",
-      "test",
-      "val"
+      "val",
+      "test"
     )[1:data[, length(unique(split_dataset))]]
   ) +
   #breaks should start from january, regardless of data