Quantify Guide

The Quantify module analyses the ensemble of STOMP maps produced by pseudo-debias. It separates true crystallographic signal from bias and outputs debiased CCP4 maps.

---

Quick start

pseudo-quantify --input_path /scratch/results/my_experiment

The command auto-detects all experiments inside the workspace. For a single crystal, point it at the crystal subdirectory:

pseudo-quantify --input_path /scratch/results/my_experiment/target_5e5z

---

CLI options

Flag	Short	Default	Description
--input_path	-p	required	Workspace root produced by pseudo-debias.
--stem	-s	auto	Explicit experiment stem (inferred if omitted).
--k_factor	-k	1.0	Radius multiplier K for atom ownership spheres.
--map_cap	-c	50	Limit to the first N maps.
--force	-f	False	Overwrite existing quantify_results/.

---

Python API

from quantify.api import run_quantification

run_quantification(
    input_path="/scratch/results/my_experiment",
    k_factor=1.0,
    map_cap=50,
    force=False,
)

---

Required input layout

pseudo-quantify expects the directory structure created by pseudo-debias:

<crystal>/
├── processed/
│   └── {stem}_updated.pdb
├── metadata/
│   └── {stem}_omission_map.json
└── results/
    ├── {stem}_0/{stem}_0.mtz
    ├── {stem}_1/{stem}_1.mtz
    └── ...

---

Outputs

Results are written to <crystal>/quantify_results/k_{k}_cap_{cap}/:

File	Description
{stem}_signal.ccp4	Mean debiased density (true signal)
{stem}_noise.ccp4	Std-dev of cleaned densities (local noise)
{stem}_snr.ccp4	Signal-to-noise ratio — input to pseudo-analyse
{stem}_p_value.ccp4	Voxel-wise significance (t-test vs bulk solvent)

A null-distribution parameter file is also saved to metadata/ for use by pseudo-analyse to set a data-driven significance threshold.

---

Algorithm overview

Ownership

For each voxel v, atoms within distance R(element, resolution) × k_factor are identified as owners. This is determined via a KD-tree of atom positions and produces a binary status matrix S (shape N_maps × N_owners): 1 = atom present in that map, 0 = omitted.

Bias estimation and removal

For each owner j:
    if S[:,j] has both 0s and 1s:
        B_j = mean(D[S[:,j]=1]) − mean(D[S[:,j]=0])

CleanedVector = D − S · B
Signal = mean(CleanedVector)
Noise  = std(CleanedVector)

---

HPC / SLURM submission

For large screening workspaces, wrap the command in sbatch and use --num_processes to parallelise across crystals within the job:

sbatch --partition cs05r \
       --cpus-per-task 8 \
       --mem-per-cpu 5G \
       --time 3-00:00:00 \
       --wrap "pseudo-quantify --input_path /scratch/results/my_screen \
                               --num_processes 8"

--num_processes controls how many crystals are processed in parallel inside the job. Set it to match --cpus-per-task.

---

Parameter reference

See Configuration Reference — Quantify.