DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction

Mustapha Bounoua; Giulio Franzese; Pietro Michiardi

cs.LGSep 28, 2026

DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction

Authors: Mustapha Bounoua, Giulio Franzese, Pietro Michiardi

Organizations: Department of Data Science EURECOM, France

Abstract

Predicting cellular responses to perturbations is a central problem in cellular biology, with broad applications in systems biology and drug discovery. This task is challenging because cellular responses can be complex and cell-state dependent, intrinsic cell-to-cell variability can be confounded with perturbation effects, and destructive single-cell RNA sequencing precludes paired measurements of the same cell before and after treatment. Flow matching transports control cells to perturbed states flexibly, but acting on the full cell state can confound perturbation effects with pre-existing cell-to-cell variability. Disentangled approaches separate responsive from invariant components, but model perturbations through prescribed mechanisms, such as latent shifts or graph edits, limiting their flexibility. We address both limitations in a unified framework. A variational encoder disentangles each cell into an invariant block, capturing state unaffected by the perturbation, and a responsive block, capturing state it changes, through conditional priors and an information-theoretic invariance constraint. Conditional flow matching transports only the responsive block, conditioned on the perturbation and invariant state, yielding a flexible, data-driven model of perturbation effects without confounding pre-existing variability. Across several benchmarks, our method outperforms the strongest published method in settings involving combinatorial and unseen perturbation prediction.

Figures & tables

Panel of Figure 1 — Figure 1: Overview of Drift . The two stages share one architecture. Stage 1 trains a variational autoencoder on control and perturbed cells, encoding each into an invariant code $z_{\mathrm{nr}}$ (driven by the covariates $c$ : cell line, batch, sequencing depth) and a responsive latent $z_{r}$ (driven by the perturbation $u$ ). Each block is associated to a learned conditional prior, $p(z_{\mathrm{nr}}\mid c)$ and $p(z_{r}\mid u)$ , and $z_{\mathrm{nr}}$ is optimized to carry no perturbation information, $I(z_{\mathrm{nr}};u){=}0$ ; the concatenation $[z_{\mathrm{nr}},z_{r}]$ is decoded to reconstruct the input, control or perturbed. Stage 2 freezes the encoder and decoder and learns the mechanism $p(z_{r}\mid z_{\mathrm{nr}},u)$ as a transport: a conditional flow $v_{\theta}(z_{t},t\mid z_{\mathrm{nr}},u)$ generates only the responsive latent $z_{r}^{\prime}$ from its control to its perturbed state along the interpolant $z_{t}$ ( $t{:}\,0\to 1$ ), while $z_{\mathrm{nr}}$ is copied from the input cell. Decoding $[z_{\mathrm{nr}},z_{r}^{\prime}]$ yields the predicted perturbed cell.

Panel of Figure 2 — Figure 2: The generative model of Drift ; shaded nodes are observed. The non-responsive block $z_{\mathrm{nr}}$ is generated from the assay covariates $c$ ; the responsive block $z_{r}$ from the perturbation $u$ and $z_{\mathrm{nr}}$ (the mechanism); the profile $x$ from both. The non-responsive block is invariant to the perturbation, $z_{\mathrm{nr}}\perp u$ (Eq. 3 ), the property that defines it, while the mechanism $z_{\mathrm{nr}}\!\to\!z_{r}$ lets the response depend on the basal state.

Type	Method	$\rho\Delta\uparrow$	$\rho\Delta^{\mathrm{D}}\uparrow$	$\mathrm{ACC}\Delta\uparrow$	$\mathrm{ACC}\Delta^{\mathrm{D}}\uparrow$	DES $\uparrow$	PDS $\uparrow$	L2 $\downarrow$	MSE $\downarrow$	MAE $\downarrow$
Sta.	Control	–	–	–	–	–	0.5000	9.25	0.0487	0.1720
	Linear	0.6211	0.7075	0.8007	0.8906	0.3192	0.5363	6.76	0.0265	0.1233
	Linear-scGPT	0.6925	0.7514	0.8424	0.9688	0.5023	0.7913	5.31	0.0155	0.0942
Found.	scGPT	0.4408	0.4416	0.8125	0.4839	0.2504	0.5022	7.35	0.0296	0.1285
	scFoundation	0.6813	0.4778	0.8768	0.7453	0.5409	0.7994	4.91	0.0138	0.0852
	GeneCompass	0.6897	0.4810	0.8916	0.7484	0.5948	0.8024	4.77	0.0124	0.0808

Table 1: Norman additive split. Bold : best; underline : second best.

Type	Method	$\rho\Delta\uparrow$	$\rho\Delta^{\mathrm{D}}\uparrow$	$\mathrm{ACC}\Delta\uparrow$	$\mathrm{ACC}\Delta^{\mathrm{D}}\uparrow$	DES $\uparrow$	PDS $\uparrow$	L2 $\downarrow$	MSE $\downarrow$	MAE $\downarrow$
Single gene perturbation
Sta.	Control	–	–	–	–	–	0.5000	4.33	0.0108	0.0685
	Linear	0.5924	0.6021	0.7695	0.8476	0.4548	0.5119	3.46	0.0067	0.0516
	Linear-scGPT	0.6072	0.6352	0.7620	0.8095	0.4584	0.6571	3.60	0.0072	0.0575
Found.	scGPT	0.5172	0.5358	0.7509	0.8286	0.5987	0.5048	3.58	0.0071	0.0530
	scFoundation	0.4453	0.3144	0.6882	0.7429	0.3897	0.6619	3.65	0.0079	0.0568

Table 2: Norman holdout split. Bold : best; underline : second best.

Type	Method	$\rho\Delta\uparrow$	$\rho\Delta^{\mathrm{D}}\uparrow$	$\mathrm{ACC}\Delta\uparrow$	$\mathrm{ACC}\Delta^{\mathrm{D}}\uparrow$	DES $\uparrow$	PDS $\uparrow$	L2 $\downarrow$	MSE $\downarrow$	MAE $\downarrow$
Sta.	Control	–	–	–	–	–	0.5000	7.03	0.0171	0.0768
	Linear	0.2720	0.3630	0.5816	0.6919	0.2149	0.5318	14.13	0.0916	0.1491
	Linear-scGPT	0.3404	0.5438	0.5852	0.7681	0.2497	0.5338	7.60	0.0172	0.0890
Found.	scGPT	0.2840	0.5691	0.5882	0.7759	0.2980	0.5001	9.49	0.0246	0.1037
	scFoundation	0.2037	0.3632	0.5489	0.6908	0.2331	0.5098	7.07	0.0155	0.0812
	GeneCompass	0.3113	0.4299	0.5758	0.7148	0.2873	0.5038	7.18	0.0162	0.0822

Table 3: Replogle RPE1. Bold : best; underline : second best.

Method	$\rho\Delta\uparrow$	DE-Spearman $\rho\uparrow$	DS $\uparrow$	L2 $\downarrow$	MSE $\downarrow$	MAE $\downarrow$
Control	N.A.	N.A.	0.5714	5.3716	0.0324	0.0698
scGPT	0.8322	$-0.1261$	0.8571	1.6934	0.0031	0.0251
CPA	0.8150	0.7906	0.8980	1.6592	0.0029	0.0240
scDFM	0.8933	0.8289	0.8776	1.6567	0.0028	0.0220
DRIFT (ours)	0.9467	0.8901	0.8816	1.2804	0.0019	0.0150

Table 4: ComboSciPlex. Bold : best; underline : second best.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Category	Hyperparameter	Norman	Replogle	ComboSciPlex
Model	Invariant block $d_{\mathrm{nr}}$	64
	Responsive block $d_{r}$	192
	Encoder and decoder width	1024 ( $2$ layers)
	Condition code $d_{\mathrm{pert}}$	128
	Perturbation encoder width	256
	Perturbation features	$3\times 128$	$3\times 128$	16

Appendix

Table 5: Configuration of DRIFT, read from the resolved configuration of the checkpoints that produced the tables. A single centered entry means the setting is shared by the three datasets.

			probe accuracy				MINE (nats)
			linear		MLP
Dataset	Conditions	chance	$z_{\mathrm{nr}}$	$z_{r}$	$z_{\mathrm{nr}}$	$z_{r}$	$z_{\mathrm{nr}}$	$z_{r}$
Norman additive	top 20	0.0500	0.157	0.981	0.171	0.991	0.14	3.35
	top 50	0.0200	0.100	0.994	0.093	0.998	0.15	4.00
	top 100	0.0100	0.053	0.980	0.056	0.981	0.17	3.96
	all	0.0060	0.042	0.938	0.037	0.962	0.11	3.68

Appendix

Table 6: Decoding which perturbation a cell received from each latent block. Classes are balanced, so chance is

1/K

. MINE Belghazi et al. (2018) is a mutual-information lower bound in nats.

Figure 3: Norman additive: UMAPs of the responsive block $z_{r}$ and invariant block $z_{\mathrm{nr}}$ , colored by perturbation.

Figure 4: The invariant block of Figure 3 , colored by the numeric covariates in $c$ .

Figure 5: UMAP density visualizations on representative Norman additive perturbations.

Type	Method	Discr. Cos $\uparrow$	E-Dist $\downarrow$	Wasserstein $\downarrow$
Sta.	Control	-0.3009	13.5125	16.5615
	Linear	0.3967	11.1376	15.3741
Found.	scFoundation	0.6218	1.3710	11.2452
	GeneCompass	0.5623	1.3059	11.1429
Gra.	GEARS	0.5871	1.2377	11.0829
Gen.	CellFlow	0.6397	1.1979	10.7579

Appendix

Table 7: Distributional metrics on Norman additive (Appendix A ). Baselines are reported by Ruan et al. (2026) ; DRIFT is the mean and standard deviation over five seeds. Bold : best; underline : second best.

Configuration	$\rho\Delta\uparrow$	$\rho\Delta^{\mathrm{D}}\uparrow$	$\mathrm{ACC}\Delta\uparrow$	DES $\uparrow$	PDS $\uparrow$	rank
Norman additive
DRIFT	0.8871	0.8914	0.9222	0.9017	0.9167	1.40
w/o disentanglement	0.8588	0.8236	0.9220	0.8753	0.9042	3.40
w/o conditioning regularization	0.8974	0.8926	0.9183	0.8965	0.9051	1.80
w/o invariance penalty	0.8706	0.8778	0.9146	0.8964	0.8992	3.40
Norman holdout (single)

Appendix

Table 8: Component ablations. Each variant removes only the named component. Rank is the mean rank across five metrics (1 = best), averaged over 5 seeds.

Split	Method	$\rho\Delta\uparrow$	$\rho\Delta^{\mathrm{D}}\uparrow$	$\mathrm{ACC}\Delta\uparrow$	$\mathrm{ACC}\Delta^{\mathrm{D}}\uparrow$	DES $\uparrow$	PDS $\uparrow$	L2 $\downarrow$	MSE $\downarrow$	MAE $\downarrow$
Norman additive	scBIG	0.8496	0.8230	0.9197	0.9906	0.8593	0.8548	3.92	0.0091	0.0689
	DRIFT , ESM2 only	0.8878	0.8938	0.9203	0.9989	0.9020	0.9045	3.22	0.0062	0.0569
	DRIFT	0.8871	0.8914	0.9222	0.9977	0.9017	0.9167	3.12	0.0057	0.0549
Replogle RPE1	scBIG	0.4875	0.5925	0.6471	0.8089	0.5145	0.5520	6.12	0.0118	0.0676
	DRIFT , ESM2 only	0.4919	0.6032	0.6465	0.8124	0.6865	0.5696	6.04	0.0115	0.0670
	DRIFT	0.5085	0.6160	0.6537	0.8149	0.7021	0.6044	5.68	0.0101	0.0633

Appendix

Table 9: Contribution of genetic perturbation features. Drift represents each perturbed gene using ESM2

\oplus

STRING

\oplus

Reactome; ESM2 only removes STRING and Reactome while leaving all other components unchanged. Ruan et al. (2026) already integrates all three sources. Bold : best; underline : second best.

Component	$\rho\Delta$	$\rho\Delta^{\mathrm{D}}$	$\mathrm{ACC}\Delta$	DES	PDS	mean
Norman additive
Disentanglement	+10.6	+36.9	+0.2	+4.5	+3.3	+11.1
Conditioning regularization	$-$ 3.9	$-$ 0.7	+3.2	+0.9	+3.0	+0.5
Invariance penalty	+6.2	+7.4	+6.3	+0.9	+4.6	+5.1
Norman holdout (single)
Disentanglement	+40.7	+43.5	+19.0	+13.0	+2.3	+23.7

Appendix

Table 10: Component contributions, expressed as a percentage of DRIFT’s margin over the Linear baseline:

(\text{DRIFT}-\text{without component})/(\text{DRIFT}-\text{Linear})

for each dataset and metric. Green values indicate improvement; grey values lie within standard error across seeds; red values are negative.

Method	$\rho\Delta$	$\rho\Delta^{\mathrm{D}}$	$\mathrm{ACC}\Delta$	$\mathrm{ACC}\Delta^{\mathrm{D}}$	DES	PDS	L2	MSE	MAE
GEARS	0.0055	0.0151	0.0031	0.0080	0.0081	0.0048	0.0649	0.0006	0.0014
scGPT	0.0006	0.0045	0.0010	0.0010	0.0084	0.0026	0.0197	0.0002	0.0004
scFoundation	0.0009	0.0031	0.0004	0.0036	0.0067	0.0029	0.0044	0.0001	0.0002
CellFlow	0.0035	0.0146	0.0013	0.0213	0.0033	0.0074	0.0636	0.0004	0.0014
scBIG	0.0026	0.0038	0.0023	0.0029	0.0015	0.0096	0.0411	0.0002	0.0009
DRIFT (ours)	0.0034	0.0045	0.0031	0.0019	0.0028	0.0101	0.0558	0.0002	0.0010

Appendix

Table 11: Standard deviation over five seeds, Norman additive split.

Method	$\rho\Delta$	$\rho\Delta^{\mathrm{D}}$	$\mathrm{ACC}\Delta$	$\mathrm{ACC}\Delta^{\mathrm{D}}$	DES	PDS	L2	MSE	MAE
scGPT	0.0008	0.0027	0.0003	0.0031	0.0155	0.0006	0.0039	0.0001	0.0001
scFoundation	0.0072	0.0122	0.0042	0.0091	0.0158	0.0023	0.0227	0.0000	0.0005
CellFlow	0.0026	0.0098	0.0013	0.0038	0.0042	0.0092	0.0053	0.0000	0.0001
scBIG	0.0016	0.0036	0.0021	0.0014	0.0042	0.0026	0.0105	0.0000	0.0002
DRIFT (ours)	0.0048	0.0041	0.0023	0.0046	0.0061	0.0040	0.0238	0.0001	0.0004

Appendix

Table 12: Standard deviation over five seeds, Norman holdout split.

Type	Method	$\rho\Delta\uparrow$	$\rho\Delta^{\mathrm{D}}\uparrow$	$\mathrm{ACC}\Delta\uparrow$	$\mathrm{ACC}\Delta^{\mathrm{D}}\uparrow$	DES $\uparrow$	PDS $\uparrow$	L2 $\downarrow$	MSE $\downarrow$	MAE $\downarrow$
Gen.	DRIFT (ours)	0.5085 $\pm$ 0.0074	0.6160 $\pm$ 0.0058	0.6537 $\pm$ 0.0013	0.8149 $\pm$ 0.0015	0.7021 $\pm$ 0.0072	0.6044 $\pm$ 0.0067	5.68 $\pm$ 0.03	0.0101 $\pm$ 0.0001	0.0633 $\pm$ 0.0002

Appendix

Table 13: Replogle RPE1, mean and standard deviation over 5 seeds of DRIFT .

Method	$\rho\Delta\uparrow$	DE-Spearman $\rho\uparrow$	DS $\uparrow$	L2 $\downarrow$	MSE $\downarrow$	MAE $\downarrow$
DRIFT (ours)	0.9467 $\pm$ 0.0057	0.8901 $\pm$ 0.0057	0.8816 $\pm$ 0.0171	1.2804 $\pm$ 0.0536	0.0019 $\pm$ 0.0002	0.0150 $\pm$ 0.0006

Appendix

Table 14: ComboSciPlex, mean and standard deviation over five end-to-end seeds of Drift .

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Predicting cellular transcriptional responses to genetic perturbations is a central problem in single-cell biology, especially in the zero-shot setting where the perturbed gene or gene combination is unseen during training. A major difficulty is that perturbation effects are not determined by expression state alone: they depend on how the perturbed gene product influences other genes and proteins, how those downstream factors act on cis-regulatory elements, and which regulatory programs are active in the current cell state. To better capture this biological complexity, we propose CisTransCell, a cell-conditioned multi-modal framework for single-cell perturbation prediction that augments each gene with two complementary priors: a regulatory-sequence prior that captures how the gene is controlled, and a coding-sequence prior that captures what the gene product does. By integrating these priors with cellular expression state, CisTransCell models perturbation response as a cascade from gene function to regulatory control to downstream transcriptional change. Experiments on benchmark single-cell perturbation datasets show that CisTransCell achieves strong performance in zero-shot perturbation prediction.

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