Fixes

scripts/run_benchmark/param_sweep/mapmycells_params.yaml

@@ -0,0 +1,34 @@
+# Parameter sweep for the mapmycells (Allen cell_type_mapper / MapMyCells) cell-type
+# annotation method. Committed source of truth for run_test_mapmycells_nebius.sh (read
+# from GitHub via a raw URL, since the Nebius compute env pulls the repo but cannot see
+# the launch host's local files).
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf). For every method the workflow builds:
+#   * one "default" variant using the `default:` args below, and
+#   * one extra variant per value in each `sweep:` list, with that ONE arg overridden.
+# The benchmark varies a SINGLE parameter at a time (a "star" around the default, not
+# a full grid), so total mapmycells variants = 1 default + sum(sweep list lengths) = 5.
+#
+# See src/methods_cell_type_annotation/mapmycells/NOTES.md ("Optimization / tuning") for
+# the rationale.
+#
+# ⚠️ bootstrap_iteration / bootstrap_factor were JUST ADDED to config.vsh.yaml, so the
+# build/main container must be rebuilt + pushed before this sweep runs (check-component).
+parameters:
+  mapmycells:
+    # Baseline (fast anchor). NB the SHIPPED component default is (1, 1.0) = no
+    # bootstrapping. Here bootstrap_factor is pinned to the tool default 0.9 (not 1.0)
+    # ON PURPOSE: with factor 1.0 every bootstrap iteration is identical, which would
+    # make the bootstrap_iteration sweep below inert. 0.9 makes the axis meaningful.
+    default:
+      bootstrap_iteration: 1
+      bootstrap_factor: 0.9
+    sweep:
+      # bootstrap_iteration: how many times each query cell is re-mapped on a random
+      # 90% (bootstrap_factor) subset of markers before the majority vote. 1 (the
+      # default variant above) = single pass; the Allen tool default for bootstrapped
+      # correlation mapping is 100. Walks the shipped speed-tuned value up toward the
+      # tool default. 1 is omitted here (covered by the default variant); 100 is the
+      # slowest / most-robust endpoint.
+      bootstrap_iteration: [10, 25, 50, 100]

scripts/run_benchmark/param_sweeps_full/baysor_params.yaml

@@ -0,0 +1,32 @@
+# Parameter sweep for the baysor transcript assignment method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_transcript_assignment.qmd
+# from the sweep results in ~/projects/txsim_results/param_sweep_tests/transcript_assignment/baysor
+#
+# Selection: the 3 parameter families that reach the Pareto front (max cells,
+# max negative marker purity) in the most datasets, tie-broken by how many datasets
+# they beat `default` in and then by Euclidean distance from `default`. Within each
+# family: every value on the front in >=1 dataset, plus the highest-distance value,
+# topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total baysor variants = 1 default + 6 swept values.
+parameters:
+  baysor:
+    default:
+      force_2d: true
+      min_molecules_per_cell: 50
+      scale: -1.0
+      scale_std: "25%"
+      n_clusters: 4
+      prior_segmentation_confidence: 0.8
+    sweep:
+      # on front in 2/3 datasets, beats default in 3; picked: 0.2 (front + max distance), 0.5 (distance top-up)
+      prior_segmentation_confidence: [0.2, 0.5]
+      # on front in 1/3 datasets, beats default in 3; picked: 10 (front + max distance), 25 (distance top-up)
+      min_molecules_per_cell: [10, 25]
+      # on front in 0/3 datasets, beats default in 1; picked: 8 (max distance), 6 (distance top-up)
+      n_clusters: [6, 8]

scripts/run_benchmark/param_sweeps_full/clustermap_params.yaml

@@ -0,0 +1,38 @@
+# Parameter sweep for the clustermap transcript assignment method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_transcript_assignment.qmd
+# from the sweep results in ~/projects/txsim_results/param_sweep_tests/transcript_assignment/clustermap
+#
+# Selection: the 3 parameter families that reach the Pareto front (max cells,
+# max negative marker purity) in the most datasets, tie-broken by how many datasets
+# they beat `default` in and then by Euclidean distance from `default`. Within each
+# family: every value on the front in >=1 dataset, plus the highest-distance value,
+# topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total clustermap variants = 1 default + 8 swept values.
+parameters:
+  clustermap:
+    default:
+      window_size: 700
+      xy_radius: 40
+      z_radius: 0
+      fast_preprocess: false
+      gauss_blur: true
+      sigma: 1.0
+      pct_filter: 0.0
+      LOF: false
+      contamination: 0
+      min_spot_per_cell: 5
+      dapi_grid_interval: 5
+      cell_num_threshold: 0.1
+    sweep:
+      # on front in 0/3 datasets, beats default in 2; picked: 60 (max distance), 30 (distance top-up), 20 (distance top-up)
+      xy_radius: [20, 30, 60]
+      # on front in 0/3 datasets, beats default in 2; picked: 0.01 (max distance), 0.05 (distance top-up), 0.2 (distance top-up)
+      cell_num_threshold: [0.01, 0.05, 0.2]
+      # on front in 0/3 datasets, beats default in 2; picked: 0.05 (max distance), 0.1 (distance top-up)
+      pct_filter: [0.05, 0.1]

scripts/run_benchmark/param_sweeps_full/comseg_params.yaml

@@ -0,0 +1,32 @@
+# Parameter sweep for the comseg transcript assignment method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_transcript_assignment.qmd
+# from the sweep results in /Users/daria.romanovskaia/projects/txsim_results/param_sweep_tests/transcript_assignment/comseg
+#
+# Selection: the 3 parameter families that reach the Pareto front (max cells,
+# max negative marker purity) in the most datasets, tie-broken by how many datasets
+# they beat `default` in and then by Euclidean distance from `default`. Within each
+# family: every value on the front in >=1 dataset, plus the highest-distance value,
+# topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total comseg variants = 1 default + 7 swept values.
+parameters:
+  comseg:
+    default:
+      mean_cell_diameter: 15.0
+      max_cell_radius: 25.0
+      alpha: 0.5
+      min_rna_per_cell: 5
+      norm_vector: false
+      allow_disconnected_polygon: true
+    sweep:
+      # on front in 1/3 datasets, beats default in 1; picked: 10.0 (front + max distance), 20.0 (on front)
+      mean_cell_diameter: [10.0, 20.0]
+      # on front in 1/3 datasets, beats default in 0; picked: 1.0 (front + max distance), 0.75 (on front), 0.25 (on front)
+      alpha: [0.25, 0.75, 1.0]
+      # on front in 0/3 datasets, beats default in 0; picked: 20 (max distance), 10 (distance top-up)
+      min_rna_per_cell: [10, 20]

scripts/run_benchmark/param_sweeps_full/fastreseg_params.yaml

@@ -0,0 +1,30 @@
+# Parameter sweep for the fastreseg transcript assignment method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_transcript_assignment.qmd
+# from the sweep results in /Users/daria.romanovskaia/projects/txsim_results/param_sweep_tests/transcript_assignment/fastreseg
+#
+# Selection: the 3 parameter families that reach the Pareto front (max cells,
+# max negative marker purity) in the most datasets, tie-broken by how many datasets
+# they beat `default` in and then by Euclidean distance from `default`. Within each
+# family: every value on the front in >=1 dataset, plus the highest-distance value,
+# topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total fastreseg variants = 1 default + 0 swept values.
+parameters:
+  fastreseg:
+    default:
+      molecular_distance_cutoff: 2.7
+      flagCell_lrtest_cutoff: 5
+      svmClass_score_cutoff: -2
+      cutoff_spatialMerge: 0.5
+    sweep:
+      # on front in 1/3 datasets, beats default in 0; picked: 
+      cutoff_spatialMerge: []
+      # on front in 1/3 datasets, beats default in 0; picked: 
+      flagCell_lrtest_cutoff: []
+      # on front in 1/3 datasets, beats default in 0; picked: 
+      molecular_distance_cutoff: []

scripts/run_benchmark/param_sweeps_full/moscot_params.yaml

@@ -0,0 +1,32 @@
+# Parameter sweep for the moscot cell type annotation method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_cell_type_annotation.qmd
+# from the sweep results in /Users/daria.romanovskaia/projects/txsim_results/param_sweep_tests/cell_type_annotation/moscot
+#
+# Selection: the 3 parameter families that reach the Pareto front (max per-cell-type
+# co-expression similarity, max negative marker purity) in the most datasets, tie-broken by
+# how many datasets they beat `default` in and then by Euclidean distance from `default`.
+# Within each family: every value on the front in >=1 dataset, plus the highest-distance
+# value, topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total moscot variants = 1 default + 7 swept values.
+parameters:
+  moscot:
+    default:
+      alpha: 0.8
+      epsilon: 0.01
+      tau: 0.3
+      rank: 500
+      batch_size: 1024
+      mapping_mode: "max"
+    sweep:
+      # on front in 0/3 datasets, beats default in 2; picked: sum (max distance)
+      mapping_mode: ["sum"]
+      # on front in 0/3 datasets, beats default in 2; picked: 0.1 (max distance), 0.001 (distance top-up), 0.05 (distance top-up)
+      epsilon: [0.001, 0.05, 0.1]
+      # on front in 0/3 datasets, beats default in 1; picked: 0.5 (max distance), 0.7 (distance top-up), 0.9 (distance top-up)
+      alpha: [0.5, 0.7, 0.9]

scripts/run_benchmark/param_sweeps_full/pciseq_params.yaml

@@ -0,0 +1,31 @@
+# Parameter sweep for the pciseq transcript assignment method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_transcript_assignment.qmd
+# from the sweep results in /Users/daria.romanovskaia/projects/txsim_results/param_sweep_tests/transcript_assignment/pciseq
+#
+# Selection: the 3 parameter families that reach the Pareto front (max cells,
+# max negative marker purity) in the most datasets, tie-broken by how many datasets
+# they beat `default` in and then by Euclidean distance from `default`. Within each
+# family: every value on the front in >=1 dataset, plus the highest-distance value,
+# topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total pciseq variants = 1 default + 8 swept values.
+parameters:
+  pciseq:
+    default:
+      InsideCellBonus: 2
+      MisreadDensity: 1e-05
+      nNeighbors: 3
+      Inefficiency: 0.2
+      rGene: 20
+    sweep:
+      # on front in 3/3 datasets, beats default in 2; picked: 0.001 (front + max distance), 1.0E-4 (on front), 1.0E-6 (distance top-up)
+      MisreadDensity: [1.0E-6, 1.0E-4, 0.001]
+      # on front in 2/3 datasets, beats default in 2; picked: 0 (front + max distance), 1 (on front), 6 (distance top-up)
+      InsideCellBonus: [0, 1, 6]
+      # on front in 1/3 datasets, beats default in 2; picked: 40 (front + max distance), 10 (distance top-up)
+      rGene: [10, 40]

scripts/run_benchmark/param_sweeps_full/proseg_params.yaml

@@ -0,0 +1,30 @@
+# Parameter sweep for the proseg transcript assignment method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_transcript_assignment.qmd
+# from the sweep results in /Users/daria.romanovskaia/projects/txsim_results/param_sweep_tests/transcript_assignment/proseg
+#
+# Selection: the 3 parameter families that reach the Pareto front (max cells,
+# max negative marker purity) in the most datasets, tie-broken by how many datasets
+# they beat `default` in and then by Euclidean distance from `default`. Within each
+# family: every value on the front in >=1 dataset, plus the highest-distance value,
+# topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total proseg variants = 1 default + 7 swept values.
+parameters:
+  proseg:
+    default:
+      cell_compactness: 0.04
+      nuclear_reassignment_prob: 0.2
+      diffusion_probability: 0.2
+      ncomponents: 10
+    sweep:
+      # on front in 1/3 datasets, beats default in 3; picked: 0.03 (front + max distance), 0.06 (distance top-up), 0.02 (distance top-up)
+      cell_compactness: [0.02, 0.03, 0.06]
+      # on front in 1/3 datasets, beats default in 2; picked: 0.5 (front + max distance), 0.05 (distance top-up)
+      nuclear_reassignment_prob: [0.05, 0.5]
+      # on front in 1/3 datasets, beats default in 1; picked: 15 (on front), 5 (max distance)
+      ncomponents: [5, 15]

scripts/run_benchmark/param_sweeps_full/resolvi_correction_params.yaml

@@ -0,0 +1,30 @@
+# Parameter sweep for the resolvi_correction expression correction method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_expression_correction.qmd
+# from the sweep results in /Users/daria.romanovskaia/projects/txsim_results/param_sweep_tests/expression_correction/resolvi_correction
+#
+# Selection: the 3 parameter families that reach the Pareto front (max per-cell-type
+# co-expression similarity, max negative marker purity) in the most datasets, tie-broken by
+# how many datasets they beat `default` in and then by Euclidean distance from `default`.
+# Within each family: every value on the front in >=1 dataset, plus the highest-distance
+# value, topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total resolvi_correction variants = 1 default + 4 swept values.
+parameters:
+  resolvi_correction:
+    default:
+      celltype_key: "cell_type"
+      n_hidden: 32
+      encode_covariates: false
+      downsample_counts: true
+    sweep:
+      # on front in 0/3 datasets, beats default in 3; picked: 128 (max distance), 64 (distance top-up)
+      n_hidden: [64, 128]
+      # on front in 0/3 datasets, beats default in 2; picked: false (max distance)
+      downsample_counts: [false]
+      # on front in 0/3 datasets, beats default in 1; picked: true (max distance)
+      encode_covariates: [true]

scripts/run_benchmark/param_sweeps_full/run_full_cellposev4_nebius.sh

@@ -28,10 +28,10 @@ set -e
 resources_s3=/scratch/task_ist_preprocessing/datasets
 publish_dir="/scratch/results/runs/$(date +%Y-%m-%d_%H-%M-%S)_cellposev4_full_sweep"
 
-# The sweep lives in a committed file, read from GitHub at runtime. $params_branch
-# defaults to the branch you are on; the file must be pushed there on GitHub.
+# The sweep lives in a committed file, read from GitHub at runtime from the `main`
+# branch; the file must be committed AND pushed to main on GitHub before launching.
 params_repo="openproblems-bio/task_ist_preprocessing"
-params_branch="$(git rev-parse --abbrev-ref HEAD)"
+params_branch="main"
 params_url="https://raw.githubusercontent.com/${params_repo}/${params_branch}/scripts/run_benchmark/param_sweeps_full/cellposev4_params.yaml"
 
 cat > /tmp/params_settings_cellposev4_full.yaml << HERE

scripts/run_benchmark/param_sweeps_full/run_full_stardist_nebius.sh

@@ -28,10 +28,10 @@ set -e
 resources_s3=/scratch/task_ist_preprocessing/datasets
 publish_dir="/scratch/results/runs/$(date +%Y-%m-%d_%H-%M-%S)_stardist_full_sweep"
 
-# The sweep lives in a committed file, read from GitHub at runtime. $params_branch
-# defaults to the branch you are on; the file must be pushed there on GitHub.
+# The sweep lives in a committed file, read from GitHub at runtime from the `main`
+# branch; the file must be committed AND pushed to main on GitHub before launching.
 params_repo="openproblems-bio/task_ist_preprocessing"
-params_branch="$(git rev-parse --abbrev-ref HEAD)"
+params_branch="main"
 params_url="https://raw.githubusercontent.com/${params_repo}/${params_branch}/scripts/run_benchmark/param_sweeps_full/stardist_params.yaml"
 
 cat > /tmp/params_settings_stardist_full.yaml << HERE

scripts/run_benchmark/param_sweeps_full/run_full_watershed_nebius.sh

@@ -28,10 +28,10 @@ set -e
 resources_s3=/scratch/task_ist_preprocessing/datasets
 publish_dir="/scratch/results/runs/$(date +%Y-%m-%d_%H-%M-%S)_watershed_full_sweep"
 
-# The sweep lives in a committed file, read from GitHub at runtime. $params_branch
-# defaults to the branch you are on; the file must be pushed there on GitHub.
+# The sweep lives in a committed file, read from GitHub at runtime from the `main`
+# branch; the file must be committed AND pushed to main on GitHub before launching.
 params_repo="openproblems-bio/task_ist_preprocessing"
-params_branch="$(git rev-parse --abbrev-ref HEAD)"
+params_branch="main"
 params_url="https://raw.githubusercontent.com/${params_repo}/${params_branch}/scripts/run_benchmark/param_sweeps_full/watershed_params.yaml"
 
 cat > /tmp/params_settings_watershed_full.yaml << HERE

scripts/run_benchmark/param_sweeps_full/segger_params.yaml

@@ -0,0 +1,30 @@
+# Parameter sweep for the segger transcript assignment method - FOLLOW-UP (narrowed) sweep.
+#
+# AUTO-GENERATED - do not hand-edit; regenerate by rendering
+#   results/param_sweeps/Yaml_to_heatmap_transcript_assignment.qmd
+# from the sweep results in /Users/daria.romanovskaia/projects/txsim_results/param_sweep_tests/transcript_assignment/segger
+#
+# Selection: the 3 parameter families that reach the Pareto front (max cells,
+# max negative marker purity) in the most datasets, tie-broken by how many datasets
+# they beat `default` in and then by Euclidean distance from `default`. Within each
+# family: every value on the front in >=1 dataset, plus the highest-distance value,
+# topped up by distance to 3 values where the family has that many.
+#
+# Consumed by the run_benchmark workflow via the `method_parameters_yaml` setting
+# (src/workflows/run_benchmark/main.nf): one "default" variant from `default:`,
+# plus one variant per value in each `sweep:` list, varying a SINGLE arg at a time.
+# Total segger variants = 1 default + 6 swept values.
+parameters:
+  segger:
+    default:
+      n_epochs: 20
+      prediction_graph_buffer_ratio: 0.05
+      prediction_mode: "cell"
+      node_representation_dim: 128
+    sweep:
+      # on front in 1/3 datasets, beats default in 2; picked: 0.1 (on front), 0.25 (max distance), 0.5 (distance top-up)
+      prediction_graph_buffer_ratio: [0.1, 0.25, 0.5]
+      # on front in 0/3 datasets, beats default in 2; picked: 60 (max distance), 40 (distance top-up)
+      n_epochs: [40, 60]
+      # on front in 0/3 datasets, beats default in 1; picked: nucleus (max distance)
+      prediction_mode: ["nucleus"]