Kinase inhibitors over a dose range

pki() serves cpg0008-pki, a dose-response set from the JUMP pilot [Chandrasekaran et al., 2023]. Fifteen kinase inhibitors were run in U2OS cells over a seven-point dose range, eleven of them at three doses and four at a single dose, across eight plates with 32 to 64 replicate wells per treatment. The object is well-level.

The design spends wells on replicates rather than on a full concentration ladder, so it is the workhorse compound screen here for hit calling and normalization, not for curve fitting. For a screen laid out the other way, many concentrations per compound for fitting a concentration-response curve, see OASIS pilot.

Use it if your own data is a compound screen with many replicate wells per treatment and you want to call hits, normalize against controls or check for plate artifacts. Hits, effects and cell loss, Normalize, select, aggregate and Plate artifacts and corrections all run on it.

import pandas as pd

import mantispy as mt

adata = mt.ds.pki()
adata

AnnData object with n_obs × n_vars = 3072 × 5839
    obs: 'Metadata_plate_map_name', 'Metadata_broad_sample', 'Metadata_mg_per_ml', 'Metadata_mmoles_per_liter', 'Metadata_solvent', 'Metadata_Plate', 'Metadata_Well', 'Metadata_Site_Count', 'Metadata_Count_Cells', 'Metadata_Count_CellsIncludingEdges', 'Metadata_Count_Cytoplasm', 'Metadata_Count_Nuclei', 'Metadata_Count_NucleiIncludingEdges', 'Metadata_Object_Count', 'Metadata_Barcode', 'Metadata_Supplier', 'Metadata_Supplier_Catalog', 'Metadata_pert_type', 'Metadata_control_type', 'Metadata_CellCount', 'Metadata_SiteCount', 'Metadata_Control', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_MOA', 'Metadata_Perturbation', 'Metadata_Perturbation_Type'
    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature'
    uns: 'mantispy'
    layers: None (.X)

The dose design

Metadata_Compound names the compound and Metadata_Concentration its dose in millimoles per litre. Counting wells per compound and dose shows the ladder each treatment was run on. Metadata_Control marks the DMSO wells; the positive controls are treatments and are left unflagged.

obs = adata.obs
design = pd.crosstab(obs["Metadata_Compound"], obs["Metadata_Concentration"])
design.iloc[:8]

Metadata_Concentration	0.004	0.010	0.040	0.200	0.400	1.000	2.000
Metadata_Compound
BRD-K15179513-001-03-4	0	0	0	0	0	0	32
BRD-K15819326-001-01-6	0	0	0	0	0	0	32
BRD-K40109029-001-06-9	0	0	0	0	0	0	32
BRD-K95785537-001-26-9	0	0	0	0	0	0	32
BRD-U00086672-001-01-9	0	0	0	64	64	64	0
BRD-U00086673-001-01-9	0	0	0	64	64	64	0
BRD-U00086674-001-01-9	0	0	64	64	64	0	0
BRD-U00086675-001-01-9	0	0	64	64	64	0	0

Cells thin out as the dose rises

Reading cells per well against concentration already shows the cytotoxic arm of the dose response, before any profile distance is computed: the highest doses leave fewer cells than DMSO.

mt.pl.cell_counts(adata, groupby="Metadata_Concentration")