Fixed doc (#16)

Author: remydubois

docs/api.rst

@@ -4,14 +4,20 @@ API Reference
 illico package
 --------------
 
-.. automodule:: illico.asymptotic_wilcoxon
+.. automodule:: illico
    :members: asymptotic_wilcoxon
    :undoc-members:
    :show-inheritance:
+   :no-index:
 
 ovo subpackage
 --------------
 
+.. automodule:: illico.ovo
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 .. automodule:: illico.ovo.dense_ovo
    :members:
    :undoc-members:
@@ -25,6 +31,11 @@ ovo subpackage
 ovr subpackage
 --------------
 
+.. automodule:: illico.ovr
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 .. automodule:: illico.ovr.dense_ovr
    :members:
    :undoc-members:

docs/conf.py

@@ -38,13 +38,15 @@
     "anndata",
     "joblib",
     "loguru",
-    "tqdm",
     "numba",
     "h5py",
     "numpy",
     "scipy",
     "pandas",
     "sklearn",
+    "statsmodels",
+    "tqdm",
+    "illico.rust_backend",
 ]
 
 # Autodoc settings

docs/overview.md

@@ -2,13 +2,13 @@
 
 *illico* is a python library performing blazing fast asymptotic wilcoxon rank-sum tests (same as `scanpy.tl.rank_genes_groups(…, method="wilcoxon")`), useful for single-cell RNASeq data analyses and processing. `illico`'s features are:
 
-1. 🚀 Blazing fast: On K562 (essential) dataset (~300k cells, 8k genes, 2k perturbations), `illico` computes DE genes (with `reference="non-targeting"`) in a mere 20 seconds. That's more than 100 times faster than both `pdex` or `scanpy` with the same compute ressources (8 CPUs).
+1. 🚀 Blazing fast: On K562 (essential) dataset (~300k cells, 8k genes, 2k perturbations), `illico` computes DE genes (with `reference="non-targeting"`) in a mere 15 seconds. That's more than 100 times faster than both `pdex` or `scanpy` with the same compute ressources (8 CPUs).
 2. 💠 No compromise: on synthetic data, `illico`'s p-values matched `scipy.stats.mannwhitneyu` up to a relative difference of 1.e-12, and an absolute tolerance of 0.
 3. ⚡ Thread-first: `illico` eventually parallelizes the processing (if specified by the user) over **threads**, never processes. This saves you from all the fixed cost of multiprocessing, such as spanning processes, duplicating data across processes, and communication costs.
 4. 🐞 Data format agnostic: whether your data is dense, sparse along rows, or sparse along columns, `illico` will deal with it while never converting the whole data to whichever format is more optimized.
 5. 🪶 Lightweight: `illico` will process the input data in batches, making any memory allocation needed along the way much smaller than if it processed the whole data at once.
-6. 📈 Scalable: Because thread-first and batchable, `illico` scales reasonably with your compute budget. Tests showed that spanning 8 threads brings a 7-fold speedup over spanning 1 single thread.
-7. 💾 Out-of-core: `illico` supports h5-based, on-disk-backed, dense and CSC datasets natively.
+6. 📈 Scalable: Because thread-first and batchable, `illico` scales reasonably with your compute budget. Tests showed that spanning 16 threads brings a 14-fold speedup over spanning 1 single thread.
+7. 💾 Out-of-core: `illico` supports h5-based, on-disk-backed, dense, CSC and CSR datasets natively.
 8. 🎆 All-purpose: `illico` performs both one-versus-reference (useful for perturbation analyses) and one-versus-rest (useful for clustering analyses) wilcoxon rank-sum tests, both equally optimized and fast.
 
 Approximate speed benchmarks ran on k562-essential can be found in the Benchmarks section. All the code used to generate those numbers can be found in `tests/test_asymptotic_wilcoxon.py::test_speed_benchmark`.