Semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation
Authors: Robin Khatri
Pierre Machart
Stefan Bonn
Venue: Submitted to ICLR 2023 (2023)
Keywords: Cell deconvolution
consistency regularization

Abstract: Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data with unknown composition. In biomedical research, cell deconvolution, which is a source separation task, is used to obtain mechanistic and diagnostic insights into human diseases. An unmet challenge in cell deconvolution, however, is the scarcity of realistic training data and the strong domain shift observed in synthetic training data that is used in contemporary methods. Here, we hypothesize that simultaneous consistency regularization of the target and training domains will improve deconvolution performance. By adding this biologically motivated consistency loss to two novel deep learning-based deconvolution algorithms, we achieve state-of-the-art performance on both cell fraction and gene expression estimation. Our method, DISSECT, outperforms competing algorithms across several biomedical gene expression datasets and can be easily adapted to deconvolve other biomedical data types, as exemplified by our spatial expression deconvolution experiments.

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SEMI-SUPERVISED CONSISTENCY REGULARIZATION FOR ACCURATE CELL TYPE FRACTION AND GENE EX- PRESSION ESTIMATION
Anonymous authors Paper under double-blind review
ABSTRACT
Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data with unknown composition. In biomedical re- search, cell deconvolution, which source separation task, is used obtain mechanistic and diagnostic insights into human diseases. An unmet challenge in cell deconvolution, however, the scarcity of realistic training data and the strong domain shift observed in synthetic training data that is used in contempo- rary methods. Here, we hypothesize that simultaneous consistency regularization of the target and training domains will improve deconvolution performance. By adding this biologically motivated consistency loss to two novel deep learning- based deconvolution algorithms, achieve state-of-the-art performance both cell fraction and gene expression estimation. Our method, DISSECT, outperforms competing algorithms across several gene expression datasets and can be easily adapted to deconvolve other biomedical data types, as exemplified by our spatial expression deconvolution experiments.
INTRODUCTION
prominent approach study tissue-specific gene expression changes in human development and disease is RNA sequencing (bulk RNA-seq). Tissues, however, usually consist of multiple cell types in different quantities, with different gene expression programs. a consequence, bulk RNA-seq from tissues measures average gene expression across the constituent cells, disregarding cell type- specific changes. The quantification of the cellular composition and cell type-specific expression that underlies bulk RNA-seq data is therefore of pivotal importance to understand disease mechanisms and to identify potential therapeutic interventions (Li & Wang 2021).
A recent technological advancement, single-cell RNA-seq, allows for the investigaaion of gene pression in single cells for thousands of individual cells of given tissue sample in a single ex- periment. While it provides unprecedented insights into single cell biology, it suffers from severe technical limitations, most notably gene expression 'dropouts' (Lähnemann et al. (202)). In addition, the technology still very costly, which largely prohibits its application in clinical and diagnostic settings. Bulk RNA-seq, on the other hand, can be performed for a fraction of the cost and is widely used in clinical oncology and drug discovery (Zhou et al. 2019 Roberts al. 2014).
To computationally infer cell fraction and cell type-specific gene expression information from bulk RNA-seq data, recent computational methods utilize single cell sequencing data create simulated references with known fraction and expression information for training (Avila Cobos et al.) 2020 Menden et al. 2020; Newman et al., 2019 Wang al. 2019). While this approach achieves good deconvolution results, its performance suffers from the strong domain gap between single-cell RNA- seq training (reference) data and the bulk RNA-seq target data. Among many possible sources of variation, two most obvious are the presence of batch effects which refers to technological differ- ences between two sequencing experiments and gene expression differences of biological nature. In the next section formally define the task of cell deconvolution. and present our hypothesis that semi-supervised consistency regularization minimizes the bulk RNA-seq deconvolution error while learning from single cell RNA-seq data.

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2 CELL DECONVOLUTION
Given m gene expression matrix B consisting of m bulk gene expression vectors measuring genes, the goal of deconvlution is to find a m c matrix of cell type fractions, where the number of cell types present in bulk samples such that,
XS,
where fractions and gene expression satisfy non-negativity X and S Skj. [], Vj
[1, n| and Ak [1, c] and sum-to-1 criterion i.e. Σ x 1, vi [1. Here, S is known the signature matrix and is unobserved. Each row S₁. is gene expression profile (or signature) of cell type k. To utilize reference based framework, can replaced with S,ef derived from single-cell experiment.
The problem of reference-based cell deconvolution can alternatively formulated as a learning problem, where a function such that S(B) X is learnt. Since only B is available and X is generally unknown, simulations from single-cell reference can be used to learn f. Clearly, from the above formulation of the cell deconvolution task, it is reasonable to assume linearity of deconvo- lution, i.e., each bulk mixture a linear combination of expression vectors of cells spanned with corresponding cell type fractions. Thus, defined in (Menden al., (220), multiple single cells can be combined in random proportions to generate training examples B and Xsim, where each row of Bsim defined as,
B B
where eñ is expression vector of cell belonging cell type k and œg is the number of cells belonging cell type sampled to construct B Correspondingly, each element of Xsim is pro- portion of a cell type k in that sample and is defined as,
O.ki Σ
X ik
and aR3s
In this case, since each simulated sample has a distinct signature (i.e. gene expression profile), S is three dimensional matrix with each element Skys denoting gene expression of gene j in cell type k for sample i. is computed as following,
0 Σ 1=1
Ssim k-4
Og
The predictor f. learned from a simulated dataset, can then be applied to to estimate X. Note that, the genes expressed may differ between vectors and B and as such before learning function f. each of is subsetted to include genes common with B. This is the reason why this learning problem is transductive and a separate model needs to be reconstructed for each B.
EXPLOITING LINEARITY OF DECONVOLUTION
2. ASSUMPTION
From Section 2 it is evident that the relationship between B and S is linear. However, S is unob- served and learning is done using simulations. To address the inherent domain shift, hypothesize that a consistency based regularization penalizing non-line linearittyy of mixtures of real and simulated samples would result in mapping f that is closer to true f. We define it in Section 2.1.2
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2.1.2 CONSISTENCY REGULARIZATION
Consider represents gene expression matrices of real (i.e. test) bulk RNA-seq that we want to deconvolve and and B represents gene expression matrix of simulated bulk samples. The number of rows (representing samples) in these two matrices may differ. To simplify the notation, we use the same index for real bulk samples, simulations (sim) and their mixtures (mix, defined further). Given true bulk ReA-seq B,,, and simulated sample with paired proportions (B X defined over common gene-set, we can generate mixture B such that
mix = BB, + (1 B)B
(2)
Which gives us relation
X mix mix 4.
S. + x)
Cell types are characterized by few marker genes that are invariant across cell states and even across tissues (Dominguez Conde et al., (022). A network that accurately predicts cell type fractions based on gene expression of simulated (or real) bulks would thus have to learn them. Thus, to estimate cell type fractions, we assume that the expression of these marker genes should be identical in signatures smix S., and ssim Hence,
X miix PX, + (1 3) sim
(4)
where B E [0. 1]. Equation enables the use of consistency regularization without having to ex- plicitly estimate signatures. In an iterative learning process X, can be replaced with predictions of the algorithm from the previous iteration. Naturally, it is also possible to only mix real samples with each other, however, the number of samples available from true bulk RNA seq is considerably lesser (usually ranging from couple to less than thousand) than the amount single-cells present in a single-cell experiment (usually in thousands). The equation 4 allows to generate pseudo ground truth proportions for mixtures B at the each step of learning cell type fractions, while Equation 3 allows to generate pseudo ground truth signatures at each step of learning gene expression profiles. We define the network architecture and loss functions in 2.2
2.2 NETWORK ARCHITECTURE AND LEARNING PARADIGM
We approach the two tasks, estimation of cell type fractions and estimation of gene expression profiles per cell type as two different tasks because of their differing assumptions. For estimation of cell type fractions, we assume that signatures are identical for each sample, both simulated and bulk, while to estimate gene expression, we relax this condition use full consistency regularization (Equation 3)
2.2.1 ESTIMATION OF CELL TYPE FRACTIONS
The underlying algorithm of the first part of deconvolution method an average ensemble of multilayered perceptrons (MLPs). Each MLP consists of the same architecture initialized with dif- ferent weights. This is done to reduce the variance by averaging different runs (Ju et al. 2018). Each MI.P has an architecture: Input (# genes) ReL.U6 (512) ReLU6 (256)- ReLU6 (128) ReLU6 (64) - Linear (# cell types) Softmax. ReLU6 (Output of ReLU activation clipped by maximum value of 6) (Hannun et al. 2014: Sandler et al.) 2018) was chosen out of tested activations over grid search on [Linear, RELU, ReLU6, Swish IRamachandran et al. 2017)] The final application of Softmax activation allows to achieve non-negativity and sum to I criteria of deconvolution. we train the network with batch size 64 to minimize the loss function defined below with an Adam Optimizer with initial learning rate of le 5.
Liota(( (X.i f(B
" X, xmix (B.ii)) = LKLdivergence (X ((B.i))) + A1 * Leens (X (B.
(5)
where Lit.divergence (+::) is the Kullback-Leibler divergence and Lcoms(;))) the consistency loss defined
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A
Simulation
Single cell data
Simulated bulk
Cell fractions
Cell type specific gene expression
B
Represic
Recomstracti the
IV' te
60
Mirro
Coudivecy Lan
Figure I: A. Illustration of simulation using reference single-cell data. The figure shows the simu- lation of one sample which consists of cell type fractions, simulated gene expression and cell type specific gene expression profiles (i.e. signature matrix). B. Detailed overview of an MLP used to estimate cell type fractions, and C. Overview of autoencoder used to estimate cell type specific gene expression profiles.
Coons (x .f(B.it)) = X
f(B mix >|
and
X mix = 3f. +1 8)
To generate mixtures, for each batch, we sample 3 and uniformly at random for Equation 4 The interval [0.1, 0.9] was chosen for the uniform distribution to allow for least some real and some simulated gene expression in the mixture. This loss similar to the semi-supervised framework proposed in MixMatch (Berthelot al. 2019). MixMatch match sample predictions and generalizes semi-supervised framework, while the loss defined in Equation 5 aaddressss the limited samples available from true bulk RNA-seq, unavailability of sample fractions and is derived from the definition of task itself. In essence, Equation integrates domain knowledge into the objective.
unlabelled samples to MixUp and
To avoid scenario where the network doesn't learn and outputs predictions such that f(Bmik) (B.i") = (B..), which is a solution to Equation 4 we first let the model learn purely from sim- ulated examples. This allows the model to learn meaningful expression profiles to achieve accurate results on simulated examples. We selected Y based on grid search over constant and step-wise functions. We adopt step-wise function for Åι, given as:
step < 2000,
\ =
clif 2000 I step < 4000,
10 else.
We train the network for a predefined number of steps as opposed to epochs, since it is possible to generate infinitely many simulated samples without increasing the intrinsic dimensionality of the data. In our experiments, we set their number to 5000 as found optimal in Menden et al. (2020)
2.2.2 ESTIMATION OF PER SAMPLE CELL TYPE SPECIFIC GENE EXPRESSION PROFILES
Estimation of cell type fractions from bulk RNA-seq requires an assumption that signatures cell types are shared across single cell and bulk RNA-seq. However, cell type gene expression profiles
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(at least for genes that are not invariant across tissue states) may differ between samples. Previously, works such as CSx (Newman et al., 2019) and TAPE (Chen et al. (021) have explored utilizing cell type fractions to estimate gene expression per sample. Here, autoencoder with standard normal distribution as prior to estimate average gene expression of the different cell types from bulk RNA-seq expression levels. To jointly train the network on all cell types, we condition the decoder (at its input layer) with cell type labels. This allows for training a single model to estimate gene expression of each cell type for a sample. To make use of bulk RNA seq during the training, we regularize the reconstruction loss with a consistency loss defined over per cell type signature. Denoting before and g( k) as the output of the autoencoder with condition k (corresponding to cell type label) on the decoder input, this consistency loss is defined as:
make use of a B- B-variational
Yons (f+ g. B B X
S
[l (Bg ,k) -t)-3ff0 - )49 +) "
Ski
ssim
mix) ) is the estimated proportion of cell type l in sample i. In implementation, we replace f (B:.)) )a with (B,,,) + (1- _. Thus, this loss forces the learned signature for cell type k. @ k). to be closer to signatures for both real and simulated bulk samples. This loss function makes the assumption that mixing two bulk samples is similar to mixing individual cell type specific signatures that constitute those bulks. We added this loss function with regularization parameter X2 (with default value 0.1) the loss of the standard 3- variational autoencoder (the weight on the KL divergence, denoted as 3 is set to 0.1 by default). The total loss function sums up to:
where Brixx is given by Equation 2 f (B
CYAE/ total (f.g. Bsi BRix B,, Xsim sim Ssim = || - g gBB 1)11
VAE X2 CoME (f 9, B.T.. B,, X im S
BVAE AE LxLdivergence (N(,,
where ((0, 1) is standard normal distribution, and a are the empirical mean and standard devia- tion estimated from the output of the encoder. Both the encoder and decoder consist of two hidden layers. We train the network to minimize the loss function with an Adam optimizer with initial learning rate of lE - 4. By default, the network trained with a batch size of 32 for 10000 x c number of steps. The architecture of the network is summarized in Figure] I
3 RELATED WORK AND COMPETING ALGORITTMS
Several methods for cell deconvolution have been developed. Avila Cobos et al. (2020) and Jin Liu (02T)) provided a benchmark and review of state of the art cell deconvolution algorithms. Here, focus on MuSiC, CSx, Scaden and TAPE although here several additional methods such as DWLS (Tsoccas et al. (211)) and Bisque (Jew et al.) (020) etc., because both MuSiC and CSx are single cell reference based methods that have performed well on simulation studies in aforementioned benchmarking studies. Scaden and TAPE are selected as both are deep learning based deconvolution approaches. We briefly detail these approaches below. Out of these methods, CSx and TAPE can also estimate per sample cell type-specific gene expression signatures.
MuSiC WWag et al. 2019) uses weighted non-negative least squares. MuSiC maintains cross-cell and cross-sample consistencies by appropriately weighting genes based on their informativity dur- ing an iterative procedure. MuSiC is provided as an package. Deconvolution using MuSiC was performed according to the authors recommendations. Since MuSiC is a method that utilizes multi- subject scRNA-seq datasets, when available, we used cells from multiple subjects in deconvolution with MuSIC. CibersortX (CSx) (Newman et al., 2019) is deconvolution method that addresses domain gap problems with scRNA-seq and bulk samples by aiming to correct batch effects. It uses scRNA-seq to generate a cell type specific signature matrix and uses y/support vector regression as the underlying algorithm. CSx comprises two modes, S- and B-modes, to address the domain gap. S-mode is used when deconvolving with a signature matrix constructed using scRNA-seq dataset, while B-mode is used when deconvolving with a signature matrix constructed using purified sam- ples. We followed the documentation provided by the authors to run CSx and used the S-mode. CSx
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can also predict gene expression signatures for each sample for which it uses Non- Non-negative matrix factorization based iterative algorithm. Scaden (Menden et al. (2020) is an average ensemble of three deep neural networks with different architectures that was developed for cell fraction deconvolution. Fach network is trained only on simulated pseudo bulk data generated from an scRNA-seq reference similar described above. Scaden is provided package with the instructions provided by the authors to train the networks. TAPE (Chen et al.) 2021 is a fully-connected autoencoder where the bottleneck consists of cell type fractions. The architecture of the encoder is similar to the archictecture of Scaden but with CeLU activations. The decoder consists of linear activations and outputs gene expression of the input vector. The adaptive mode of TAPE aims at optimizing the network for bulk samples, while the overall mode trains for fractions with an added loss function that reconstructs input bulk expression from fractions. Since TAPE-A reconstructs gene expression from fractions (bottleneck), the signature matrix is visible in the (linear) decoder. To estimate gene expression signatures for each bulk sample, decoder weights optimized per-sample using iterative optimization strategy. Network weights changed during the two modes, we compare with both and refer to TAPE in overall mode as TAPE-O and in adaptive mode TAPE-A. Linear MLPs; The solution the deconvolution problem could be, in principle, a linear function. For this reason we also compared to an MLP ensemble that is based on the architecture in Section 2.2 but in which we replaced all non-linear activations with an identity function and removed the consistency loss.
Python package. We used the official Scaden
4 EXPERIMENTS AND RESULTS
4.1 DATASETS
We evaluated the algorithms on six datasets consisting of peripheral blood mononuclear cells (PBMCs) and corresponding ground-truth cell type fractions experimentally quantified using flow cytometry. Details of these datasets are given in Table
Table 1: Real bulk datasets with known ground truth cell fraction information for the evaluation of deconvolution performance.
Dataset name SDY67 Monaco bulk
# samples
genes
Original Source
Type
12
11328
Zimmermann et al. (2016)
RNA-seq
12
17487
Monaco al. (2019)
RNA-seq
Monaco microarray GSE65133 GSE1 GSE1 20502
164
38593
Monaco et al.
2019
Microarray
20
11328
Newman et al.
2015
Microarray
9
19423
Finotello et al.
2019
RNA-seq
250
20343
Harrison et al.
2019
RNA-seq
To deconvolve these datasets, we used the PBMC8K (Table a reference single-cell dataset from a healthy donor for all methods considered here. To maintain same genes between the single-cell data and bulk RNA-seq, we subset both datasets over common gene-set. Number of common genes between PBMC8K and each of the bulk samples are as follows: SDY67: 10717, Monaco bulk: 13122, Monaco microarray: 13467, GSE65133: 10717, GSE107572: GSE120502: 13699.
To deconvolve with deep learning based methods (Scaden, TAPE-O, TAPE-A, Linear MLPs and DISSECT), we use this single-cell data from healthy donors to create simulated bulk data with known fractions as described in section 2 The non-deep learning methods (MuSiC and CibersortX) do not require simulations and as such single-cell data is used without simulations. For the esti- mation of cell type specific gene expression per sample, we utilized simulations in the absence of corresponding ground truth in the real bulk RNA-seq. For this, in addition to PBMC8K, we consid- ered three other reference datasets, namely PBMC6k, donorA and donorC (Table 2). The results are given Section 4.3
Since the number of cell types is unknown apriori in a bulk RNA-seq dataset that we want to decon- volve, we create an "unknown" " cell type label in the reference dataset by merging cells not belonging
to any of the cell types present in corresponding tissue (Menden et al. 2020 Chen et al. 2021). This unknown cluster allows comparison of fractions measured at relative scale. PBMCs consist of five main cell types namely CD4 T cells, CD8 T cells, NK cells, Monocytes and B cells (Bittersohl &

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Table 2: Single-cell data used for the creation of simulated reference data for supervised training. We manually annotated cells with these five cell types based on expression of cell type marker genes. performed filter out poor quality cells and genes
QC refers to Quality Control step (Appendix that are expressed in too few cells.
Dataset name
cells
# genes (Pre-QC)
genes (Post-QC)
Title in 10X Genomics' 1
PBMC8K 8381
32738
14343
8k PBMCs from Healthy Donor
PBMC6k
5419
33694
18340
6k PBMCs from a Healthy Donor
DonorA 2900
32738
13067
Frozen PBMCs (Donor A) Frozen PBMCs (Donor C)
DonorC
9519
32738
15275
Steimer 2016). Thereby, we end up with six cell types (including unknown cell type). Similarly, for the bulk RNA-seq datasets (Table [ we grouped the ground truth cell type proportions not be- longing to these five cell types in single label "Unknown" following the methodology in Menden ct al. 2020).
To preprocess single-cell datasets, utilized the procedure described in Appendix C which in- cludes quality control (QC) and simulations. detailed information on on the parameters used for simulations are provided in Appendix
For the Monaco bulk dataset (Table I. more granular level fractions of cell type subsets are quan- tified using flow cytometry. We utilized this information evaluate methods discerning closely related or searce cell type subsets. Since it is notoriously hard to identify cell types at such granu- larity in PBMC single-cell datasets from healthy donors, considered 9852 RNA-seq samples of purified cells from Ota et al. (202T) as references. Purified RNA-seq samples are average profiles for thousands of cells of the cytometry from Monaco bulk, we harmonize cell type labels. Resulting ground truth and reference had 18 cell subsets defined are given Appendix D
cell type. To match cell types between the purified reference and flow
4.2 EVALUATION METRICS
We used Pearson correlation and root mean squared (RMSE) for the evaluation of deconvolu- tion results. Since some cell types are much more abundant than others, it is important to consider the overall and per cell type average correlation and RMSE (see Apppendix B
4.3 RESULTS
In this section, present results experiments detailed in Section 4.1 Additional experiments and results on datasets without corresponding flow cytometry fractions are presented in Appendix [ There we utilize validated biological hypotheses evaluate DISSECT against competing methods.
4.3.1 ESTIMATION OF CELLTYPE FRACTIONS
To evaluate deconvolution performance, we deconvolved each of the datasets Table [using the PBMC8K reference dataset (Table 2). For MuSiC, we also evaluate using all 10x PBMC datasets listed in Table 2 as well as blood data from Immune Cell Atlas (ICA) (Appendix table 7 since MuSiC can take advantage of multi-sample reference 33. and shows significantly improved correlations in 9 out of 12 comparisons and the lowest RMSE in 11 out of 12 comparisons, across different datasets.
that DISSECT
Next, we evaluated the cell fraction deconvolution performance on the Monaco bulk (Section 4.1 dataset that contains several closely related and rare cell types and constitutes relatively harder task. With a correlation of 0.6, DISSECT's average performance is 14 percentage points better than the second best performance by Scaden (Appendix Table For out of 18 cell types it reaches the best correlation, while Scaden performs best for 3 out of cell types. With an RMSE of 0.03, Scaden's performance percentage point better than DISSECT (Appendix Table In summary, DISSECT displays the best correlation and highly competitive RMSE in the cell fraction deconvolution task.
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Table 3: Average (overall) Pearson correlation between estimations and flow cytometry cell type fractions computed over all cell types.
Dataset
MuSiC
MuSiC
MuSiC
CSx
Scaden
TAPE-
TAPE-
Linear ML.Ps
DISSECT
(All)
(ICA)
0
SDY67
nan (-
0.39 (-
0.557
0.59
0.507
0.53
0.513
0.51
0.631
0.301)
0.201)
(0.23)
(0.52)
(0,419)
(0.465)
(0.152)
(0.739)
(0.789)
Monaco
nan
0.534
0.684
0.429
0,685
0.695
0.664
0,534
0.727
bulk
(0.006)
(-0.4)
(0.475)
(0.634)
(0.649)
(0.569)
(0.662)
(0.73)
(0.783)
Monaco
nan
nan (-
0.592
0.66
0.791
0.71
nan
0.71
0.786
Microarray
(-0.16)
0.418)
(-0.41)
(0.398)
(0.189)
(0.317)
(0.03)
(0.233)
(0.799)
GSE65133 133
nan (-
nan (-
0.554
0.642
0.771
0.729
0.719
0.77
0,821
GSE107572 0.245)
0.3)
(-0.17)
(0.686)
(0.413)
(0.413)
(0.252)
(0.421)
(0.918)
nan
0.65
0.542
0.669
0.627
0.615
0.012
0.486
0.705
(0.155)
(0.24)
(0.76)
(0.527)
(0.723)
(0.658)
(0.617)
(0.71)
(0.79)
GSE120502
0.103 (-
0.451
0.567
0.529
0.688
0.692
0.632
0.63
0.662
0.302)
(0.718)
(0.77)
(0.776)
(0.718)
(0.883)
(0.779)
(0.379)
(0.864)
Table Average (overall) RMSE between estimations and flow cytometry cell type fractions com- puted over all cell types.
Dataset
MuSiC
MuSiC
MuSiC
CSx
Scaden
TAPE-
TAPE-
Linear MLPs
DISSECT
(All)
(ICA)
SDY67
0.3
0,201
0.173
0.147
0.134
0.11
0.136
0.13
0.089
(0.348)
(0.226)
(0.21)
(0.15)
(0.13)
(0.10)
(0.12)
(0.128)
(0.10)
Monaco
0.247
0.185
0.113
0.125
0.081
0.086
0.065
0.08
0.06
bulk
(0.256)
(0.194)
(0.132)
(0.094)
(0.07)
(0.097)
(0.068)
(0.077)
(0.069)
Monaco
0.27
0.292
0.21
0.151
0.139
0.15
0.17
0.285
0.07
Microarray
(0.296)
(0,304)
(0.23)
(0.134)
(0.162)
(0.15)
(0.183)
(0.307)
(0.069)
GSE65133
0.308
0.283
0.2
0.11
0.13
0.122
0.14
0,21
0.06
(0.381)
(0.292)
(0.212)
(0.121)
(0.149)
(0.119)
(0.136)
(0.253)
(0.058)
GSH107572
0.245
0.167
0.099
0.12
0.105
0.086
0.116
0.11
0.068
(0.267)
(0.196)
(0.109)
(0.137)
(0.128)
(0.099)
(0.128)
(0.11)
(0.076)
GSE120502
0.358
0.162
0.101
0.12
0.104
0.087
0,093
0.143
0.086
(0.458)
(0.172)
(0.105)
(0.12)
(0.121)
(0.093)
(0.102)
(0.15)
(0.087)
Finally, we performed an ablation study that validates our hypothesis that the consistency loss is primarily responsible for DISSECT's improved deconvolution performance (Appendix Table [18]. We also evaluated the output of DISSECT at the end of simulation-phase only. These results are provided in Appendix Tables 17 (Correlation) and 18 (RMSE) where simulation-based phase lags behind the full consissency-regullriiee training.
4.3.2 ESTIMATION OF CELL TYPE-SPECIFIC GENE EXPRESSION
Next, we evaluated the performance of DISSECT's cell type-specific gene expression inference on simulated bulk RNA-seg data. We used simulated data we could not obtain bulk RNA-seq and corresponding cell type-specific expression information. To maintain a domain shift between the training and test datasets, simulated data for training and testing were created using different single- cell datasets. Here, we compare our approach with the only two state-of-the -hett-rrrt methods that can infer cell type-specific gene expression per sample, TAPE and CSx (Section 3). We simulated bulk samples from one of the four reference single-cell PBMC datasets listed in Table 2 and created training simulations from the remaining three. To assess the performances, we computed sample- (Table 13) and gene-wise (Table () Pearson correlations. DISSECT displays the best sample- and wene-wise correlations 3 out of 4 experiments, each, taking first place in overall cell tppe-specific gene expression deconvolution performance.
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Table 5: Pearson correlation between ground truth and estimated gene expression profiles simu- lated datasets averaged over samples. The column Dataser indicates the single-cell dataset used to create simulations for the test set.
Dataset PBMC6k
CSx
TАРE-A
DISSECT
0.71 + 0.12
0.83 0.09
0.82 + 0.08
PBMC&K
0.82 + 0.13
0.79 + 0.09
0.84 + 0.11
DonorA
0.78 + 0.12
0.85 0.11
0.89 - 0.10
DonorC
0.75 + 0.09
0.81 + 0.12
0.83 0.08
Table Pearson correlation between ground truth and estimated gene expression profiles on simu- lated datasets averaged over estimated genes.
Dataset
CSx
TAPE-A
DISSECT
PBMC6k
0.37 + 0.22
0.42 + 0.14
0.46 + 0.15
PBMC8k
0.36 + 0.25
0.51 0.12
0.48 + 0.14
DonorA
0.42 0.21
0.48 + 0.20
0.48 = 0.18
DonorC
0.46 0.18
0.45 + 0.11
0.49 + 0.12
DISCUSSION
We detailed how the of a linear consistency is suitable for the task of deconvolution, especially in the absence of real ground truth training information, as often the case biomedical settings. Our approach relies on the supervised learning simulated data and unsupervised domain adap- tation to the target data of interest. This semi-supervised learning approach results in state-of-the-art deconvolution performance, for both cell fraction and gene expression estimation. While we only focused on MLPs for estimation of cell type fractions and autoencoders for gene expression esti- mation in this work, we surmise that consistency regularization might improve other deconvolution algorithms as well. We envision further work in this area.
The task of deconvolution plays an important role in spatial transcriptomics (ST) and cell-free DNA methylation (cfDNA). Recently, several algorithms have been developed for ST (Li et al. 2022) and efDNA deconvolution (Jeong al. 2022). We surmise that consistency regularization might also improve ST and cfDNA deconvolution by adjusting DISSECT's simulation procedure to mimic ST or cfDNA. We provide two proof-of-concee ST deconvolution results using consistency reg- ularization in Appendix F Similar arguments be made for the deconvolution of several other biomedical data types, such as epigenetic, proteomic, and metabolomic data, for instance.
LIMITATIONS
While DISSECT displays favorable deconvolution performance compared to other methods, the results are far from perfect. Especially for hard deconvolution tasks, such samples with many similar cell types and very scarce cell populations, an increase in deconvolution performance is warranted. Future research into semi-supervised and contrastive algorithms as well data aug- mentation and integration techniques should further enhance DISSECT's performance. As stated in Section 4.3.2 we had rely on the simulation based experiment evaluate gene expression estima- tions. Nevertheless, we still explored how well DISSECT can estimate gene expression using an ST dataset (Appendix H). Further evaluations with quality ground truths will be beneficial.
CODE AND DATA AVAILABILITY
All considered datasets are publicly available from respective sources. Code available anony- mously at https::///// //t:://///ooouuuss 4open hctpenc///////S/////C111

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A
Table Details on Blood single-cell data from Immune Cell Atlas (Domínguez Conde et al. (2022) used multi-sample reference scRNA-seq for MuSiC. Cell types that were present in less than
samples were dropped. The same cell types were selected as in scRNA-seq datasets listed in Table 2] Bcells, CD4Tcells, CD8Tcells, Monocytes and NK. The rest of the cell types were merged to form the unknown cluster (Section 4.1).
Donor name
# cells
Post-QC number of genes
621B
103
23658
637C
760
23658
A35
1368
23658
A36
3124
23658
D496
9065
23658
D503
12208
23658
Table Pearson correlation between estimates from different methods and flow cytometry for gran- ular cell type fractions in Monaco bulk.
Celltype
MuSiC
CSx
Scaden
TAPE-O
TAPE-A
Linear MLPs
DISSECT
B
nan
0.43
0.22
0.030
0.10
0.32
NSM
nan
-0.08
0.10
-0.15
-0.22
0.09
B Naive
nan
0.95
0.87
0.8
0.43
0.71
0.96
B SM
0.85
0.57
0.45
0.15
0.26
0.63
Monocytes C
0.30
0.29
0.63
0.57
0.52
0.11
0.62
Monocytes
0.41
0.36
0.87
0.81
0.54
0.93
Monocytes NC
0.25
0.09
0,31
0.35
0.48
0.19
0.66
NK
0.80
0.82
0.58
0.59
0.65
0.49
0.82
Neutrophils LD
0.2
0.89
0.48
0.57
0.03
0.56
Plasmablasts
0.62
0.85
0.86
0.65
0.66
0.42
0.92
CD4 Naive
0.66
0.47
0.70
0.34
0.14
CD4 T Memory
0.47
-0.15
0.27
0.27
0.12
0.08
0.24
CD8 T Naive
0.52
0.7
0.36
0.38
0.32
0.27
0.49
CD8 T CM
-0.65
0.19
0.13
0.21
0.01
0.12
CDS EM
nan
nan
0.47
0.45
0.11
0.62
CD8 TE
0.25
0.9
0.28
0.35
0,36
0.35
0.86
mDC
nan
0.46
0.47
0.39
0.40
0.05
0.68
pDC
0.55
0.57
0.19
0.42
0.31
0.3
0.55
Average
0.49 2
0.40
0.46
0.45
0.37
0.22
0.60
B NOTE ON AVERAGE AND OVERALL CORRELATIONS FOR PERFORMANCE EVALUATION.
Overall metrics can be deceiving when the model performs comparatively very well or very poor on a largely abundant cell type as illustrated by results in Figures and Overall metrics are provided for completeness as it has been extensively used in evaluation of other deconvolution works and
benchmarks (Avila Cobos et al. 2020 Newman et al.) 2019 Wang al. 2019 Chen et al. (02T).
C QUALITY CONTROL AND PREPROCESSING
C.1 QUALITY CONTROL:
Before simulating from reference datasets, remove cells with less than 200 expressed genes and genes which are expressed in less than 3 cells. Further, we also remove cells expressing more than 4% mitochondrial genes. Thereafter, before each deconvolution, subset reference and bulk
Calculated real values.
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Table 9: RMSE between estimates from different methods and flow cytometry for granular cell type
fractions in Monaco bulk.
Celltype
MuSiC
CSx
Scaden
TAPE-O
TAPE-A
Linear MLPs
DISSECT
B Ex
0.01
0.05
0.04
0.04
0.01
0.02
0.02
B NSM
0.02
0.01
0.02
0.03
0.05
0.04
0.02
B Naive
0.01
0.03
0.03
0.03
0.04
0.06
0.03
B SM
0.05
0.01
0.01
0.02
0.02
0.03
0.01
Monocytes
0.05
0.02
0.04
0.02
Monocytes
0.12
0.15
0.03
0.06
0.10
0.12
0.04
Monocytes NC
0.20
0.09
0.02
0.07
0.05
0.10
0.02
NK
0.05
0.08
0.08
0.11
0.11
0.05
0.08
Neutrophils
0.02
0.03
0.01
0.01
0.01
0.01
0.02
Plasmablasts
0.01
0.01
0.02
0.01
0.04
0.01
0.04
CD4 T Naive
0.02
0.03
0.05
0.05
0.02
0.02
0.05
CD4 T Memory
0.10
0.07
0.03
0.12
0.15
0.21
0.03
CD8 T Naive
0.21
0.07
0.04
0.05
0.05
0.01
0.04
CDS CM
0.01
0.08
0.05
0.12
0.01
0.03
CD8 T EM
0.02
0.01
0.02
0.02
0.01
0.03
0.02
CD8 T TE
0.01
0.07
0.09
0,08
0.11
0.16
0.08
mDC
0.01
0.04
0.04
0.08
0.09
0.03
0.03
pDC
0.02
0.00
0.02
0.01
0.01
0.01
0.02
Average
0.06
0.05
0.03
0.05
0.06
0.05
0.04
The Nonaco Microarral, Scacon Avarage corrilaiion samo: 0.791.
C5E120502, The Avorage conclation samp: 0.692.
0.188.
0.883.
Strnen CLAilook Roullood'
Vonneyles C24 bo
Eatmetedd
03 Cround truth
62
02
05
Ground truth
Figure 2:
Correlations between Ground truth fractions from Monaco Microarray (Table T and the estimations using Scaden.
Figure 3: Correlations between the ground truth fractions from GSE/20502 (Table I and the esti- mations using TAPE-O.
datasets include only the common genes between the This quality control step was identical for all methods.
C.2 SIMULATIONS
For deep learning methods, sampled og,i uniformly simulate based procedure described in Section 2 with Σ an, 100, Vi if the dataset is single-cell. For experiments on granular level 1=1
cell types where simulations are done from purified cell samples, we modify simulation procedure to reflect this. In this case, a simulated sample is given by B = Σ x where bj is the
expression vector purified sample belonging to cell type All other notations are same as in Section 2
For all experiments, we simulated total 1000 x c simulations where is number of cell types in the reference dataset.
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C.3 PREPROCESSING:
Estimation of cell type fractions:
Scaden, TAPE, Linear MLPs and DISSECT: Before passing simulated and real bulk samples to the network, we normalize samples to sum to a million counts (CPM: Counts per million) and log scale them with base after adding 1. CPM normalization mRNA expressed per gene to be out of a fixed total gene expression, and CPM is widely used in computational genomics. During training, for each batch, normalize each sample by Mir scaling. These are standard preprocessing steps (Menden et al. 2020).
performed to maintain total
For MuSiC and CibersortX (under S-mode), data supplied linear scale suggested in their respective publications and no change was made to the default normalization methods of both Wang et
2019: Newman et al. 2019).
Estimation of per sample cell type specific gene expression profiles: To estimate cell type specific gene expression profiles, we need to maintain relationship between gene expression of individual cell types and simulated bulks, which would be lost if we perform CPM normalization of both simulated samples and corresponding cell type specific gene expression profiles. Hence, instead of performing CPM normalization of simulated bulks, we normalize each test bulk sample to sum to the mean of sums of simulated samples. Further, for estimating cell type specific gene expression, we want to maintain gene level information across samples. To achieve this, instead of normalizing each sample using M innMa aæ scaling, we perform MinMar ax scaling globally over all samples.
For TAPE, since the signature matrix is observed in decoder (Section 3 preprocessing step is similar to the preprocessing done in estimating cell type fractions. For CibersortX, data was supplied on a linear scale under S-mode (Newman et al. 2019).
D CELL SUBSETS FOR ESTIMATIONS OF GRANULAR SUBSETS ON MONACO BULK.
B cells (4 subsets): B Naive (Naive B cells), B Ex (Exhausted B cells), B NSM (Non-switched memory B cells), SM (Switched memory B cells)
CD4 T cells (2 subsets): CD4 T Naive (Naive CD4 T cells), CD4 T Memory (Memory CD4 T cells).
CD8 T cells (4 subsets): T CD8 Naive (Naive CD8 T cells). CD8 T CM (Central Memory CD8 cells), CD8 TE (Terminally effector CD8 cells), and CD8 T EM (Effector Memory CD8 cells),
Monocytes (3 subsets): Monocytes C (Classical monocytes), Monocyte NC (Non classical mono- eytes) and Monocytes (Intermediate monocytes),
Dendritic cells (2 subsets): mDC (myeloid dendritic cells), pDCs (Plasmacytoid dendritic cells)
Plasmablasts
Neutrophils LD (Low density neutrophils)
NK cells.
E FURTHER EVALUATION OF DECONVOLUTION PERFORMANCE USING DIVERSE TISSUE DATASETS.
To assess the performance of DISSECT and other algorithms further bulk RNA-seq datasets, we consider additional experiments. In Section E.I we consider paired scRNA-seq and bulk RNA-seq data. In Section E.2 looked at the performance of the methods to recover established biological findings and Section E.3, we assessed how the performance changes when the reference scRNA- seq dataset is swapped with another reference.
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Table 10: Harmonization of cell subset labels between Monaco bulk and reference from Ota et al.
2021
Cell subset
Subset(S) in flow cytometry of Monaco bulk
Merged subset(S) in Ota et al. 2021
B Ex
B Ex
DN_L
B NSM
B NSM
USMBB
Naive
Naive
Naive_B
SM
B SM
SM_B
Monocytes C
Monocytes C
CI._Mono
Monocytes
Monocytes I
Int_Mono
Monocytes NC
Monocytes NC
NC Mono
NK
NK
Neutrophils LD
Neutrophils LD
LDG
Plasmablasts
Plasmablasts
Plasmablast
CD4 T Naive
T CD4 Naive
Naive_CD4
CD4 T Memory
Th1, Th2.Th17,Th//hh1,, Tregs
Th1, Th2, Th17, Tth, Fe Fe_lL_eTreg
CD8 Naive
CD8 Naive
Naive CD8
CD8 T CM
T TCD8 CM
CM CD8
CD8 T EM
T CDS EM
EM CDS
CDS TE
T CDS TE
CEMRA.CDS Mem.CD8
mDC
mDCs
mDC
PDC
pDCs
pDC
E. PAIRED SCRNA-SEQ AND BULK RNA-SEQ
To evaluate deconvolution methods on further bulk RNA-seq datasets, we obtained paired scRNA- seq and bulk RNA-seq from tissues: mammary gland and lung. The details of these datasets provided in Table IT The ground truth for bulk RNA-seq was generated using the fractions cell types as observed in the scRNA-seq.
The Tables 12 and 13 present the results on these two tissues. For the mammary gland dataset, the results are calculated per sample and averaged since the dataset contains only two samples as done in the original publication of data.
Table 11: Details on paired single-cell and bulk RNA-seq datasets considered. Cell type labels were used as provided in the corresponding sources.
Dataset name Mammary gland
# Post-QC cells
samples (bulk)
Original Source
3991
Dong et al. (2021) Delorey et al. 20021)
Lung
93246
17
Table 12: Average Pearson correlation and RMSE between ground truth and estimated cell type fractions on the Lung dataset over all cell types.
Dataset
MuSiC
CSx
Scaden
TAPE-
TAPE-
Linear MLPs
DISSECT
0.51
0.56 0.07
0.55
0.53 0.08
0.54 0.08
0.48
0.56
rmse
0.09
0.07
0.12
0.06
E.2 RELATIONSHIP BETWEEN CELL. TYPE FRACTIONS AND BIOLOGICAL PHENOTYPES
In this section, we rely on the established biological findings to evaluate deconvolution methods. For this purpose considered diverse set of tissues: brain, kidney and pancreas. Table 14 lists these datasets and corresponding hypothesis based on literature. The single cell datasets corresponding to these tissues presented in [5]
Here we are interested in investigating the deconvolution methods are faithful to the established biological findings (Presented and discussed further in this Section). We are also interested in how
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Table 13: Average Pearson correlation and RMSE between ground truth and estimated cell type fractions on the Mammary gland dataset over samples.
Dataset
MuSiC
CSx
Scaden
TAPE-
TAPE-
Linear MLPs
DISSECT
O
A
0.85
0.87
0.77
0.84
0.81
0.74
0.91
imse
0.09
0.07
0.1
0.09
0.1
0.1
0.06
Table 14: Details on bulk RNA-seq datasets used to evaluate deconvolution methods on biological phenotypes. Biological hypotheses based on literature serve proxy ground truths.
Tissue
Dataset
# samples
Biological hypothesis based on lit- erature
original
Source
Pancreas
GSE50244
(77 with information on hemoglo- bic 1C levels)
Fraction of beta cells are negatively
Fadista et (2014)
associated with severity of type 2 diabetes indicated by hemoglobin Alc (hbalC) level (Alejandro et al.
2015
Saisho
2015
Wang et al.
2013
Kidney
GSES 1492
10
Tubule cells diminish with chronic kidney disease (CKD) (Venkatacha-
Beckerman et al. (0177)
lam et al., 2015 Liu et al. 2018
Malhotra et al. 2019).
Brain
ROSMAP
508 with cor-
(463
1.
Neurodegeneration with ad-
Bennett et al. (2018)
vanced Braak stage (Streit et al. Hindle [2010] Fu et al.
responding
2009
annotation of Braak stages)
2019). and 2. Approximately 70-
30 ratio of excitatory and inhibitory
neurons (Contreras) 2004 Chen &
Dzakpasu 2010).
methods behave when different single-cell reference datasets used (Presented and discussed in Section E.3).
Deconvolution of ROSMAP with reference scRNA-seq from Allen Brain Atlas:
ROSMAP cohort consists of samples from healthy individuals and patients with Alzheimer's disease (AD), Here, we consider two biological ground truths: first is the neurodegeneration,
the loss of neurons with increasing Braak Stages (Braak et al.) 2003) (Table [1], and the second is the ratio of excitatory neurons inhibitory neurons. We deconvolved the ROSMAP using reference from Allen Brain Arlas. The results are presented in Figure 8 Nearly all methods capture a negative association between the median fractions excitatory neurons and Braak stages. Scaden, TAPE-O, DISSECT maintain a higher proportion of excitatory neurons compared to inhibitory neurons. How- ever, DISSECT estimates show the cecitatory-inhibiee and MuSiC on the other hand show opposite of what is expected.
neurons ratio be almost 70-30. TAPE-A
Deconvolution GSE50244 with reference scRNA-seg from Segersiolpe:
GSE50244 is a bulk RNAseq dataset from pancreas and consists of samples from healthy and T2D (Type diabetes) individuals. Here our biological ground truth is the negative association between beta cell proportions and the level of hemoglobin Alc (hbalC) (Table [1].. We performed the de- convolution using Segerstolpe. We restricted dataset to contain alpha, beta, gamma, delta, acinar and ductal cell types following the methodology in Wang al. (2019). The results are presented in Figure W note that all deconvolution methods successfully reveal the significant association be- tween beta cell proportions and lc level, however since the extent of the association is unknown, further quantification would be speculative.
Deconvolution of GSE81492 with reference scRNA-seq from Park:
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Table 15: Details on single-cell datasets used deconvolve corresponding tissue samples.
Tissue
Dataset name
# cells
Original Source
Pancreas
Baron
8569
Baron al. (2016)
Pancreas
Segerstolpe
3514
Segerstolpe ct al.
2016
Pancreas
Xin
1492
Xin al. (2016)
Kidney
Park
43745
Park et al.
20188
Kidney
Miao
16887
Miao al.
Brain
Allen Brain Atlas
49418
bra
A
Music
CibasortXX
TAPE-A
de
0.8
0.
0.8
n0.00
3.
0.
0.8
0.2
0.2
02
0.2
02
02
02
0.0
0.0
0.0
6
3.0
0.0
00
Mbalic
Healo
hbelc
MuSIC
CiterattX
Scader
1.0
TAPE-O
TAPE-A
MtPs
DISSECT
Soperaogo
c.0
0.0
0.0
3.0
0.8
0.0
0.0
0.4
24
CA
02
50
Figure 4: A: Distribution of relative fractions of alpha, beta, gamma, delta, acinar and ductal cells estimated on 77 bulk RNA-seq samples from GSE52044 using Seger single-cell reference dataset. B: Relationship between Hemoglobin AIC (hbalc) levels and estimated fractions of beta cells in 77 bulk RNA-seq samples from GSE52044 using Seger. Corresponding Method for each plot is indicated in the title. Pearson correlation of the relationship and associated p-value is indicated in the plot. p-value shown is obtained for beta cells from a multiple linear regression model considering age, sex and bod mass index (BMI) as covariates, i.e. model hblac Constant fractions of beta cells + Age + Sex + BMI.
GSE81492 is a dataset consisting of APOLI mutant mice (a Chronic Kidney, CKD, disease mouse model) (Table [4). Here the biological ground truth is the decrease in tubule cells Proximal tubule (PT) cells ductal convoluted tubule (DCT) cells in CKD samples compared We deconvolved the aforementioned dataset using single cell reference dataset Park. We present the results per method per reference in Figure 5 All methods reveal loss of proximal tubule (PT) cells in APOLI mice, while showing higher proportion of immune cells (B lymph, Fib, Maco and NK cells) in APOLI, However, DCT cells (Duetal convoluted tubule) cells which are also known to diminish in the APOLI mice, MuSiC shows an increase. This is also observed in Wang et al. 2019). However, other methods were successful in revealing loss of DCTs in APOLI.
the healthy state.
E.3 RELATIONSHIP BETWEEN CELL TYPE FRACTIONS AND BIOLOGICAL PHENOTYPES USING MULTIPLE REFERENCE SCRNA-SEQ
Deconvolution of GSE50244 with reference scRNA-seq from Segerstolpe, Baron and Xin:
For GSE50244, we performed deconvolution using three reference single-cell datasets. These ref- erences differ in technologies with which the cells were sequenced. These technologies are Baron: inDrop, Segerstolpe: Smart-seq2, and Xin: SMARTer. Further, these reference datasets contain cells belonging to different states. We used cells belonging to only healthy individuals in Baron and Segerstolpe while both healthy and T2D individuals are used in Xin. Further, since our goal is to compare the divergence in performance when the reference single-cell dataset is changed, we subsetted all three single-cell datasets to contain same cell types. Osoows the distribution of alpha, beta, gamma and delta cells for each deconvolution method. There is a lack of concordance between three distributions of cell types across all methods. Beta and alpha cells are generally the
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two most abundant of these four cell types in pancreatic islets (Henquin & Rahier 2011 This is correctly observed with estimations from the considered deep learning based methods. However, for Scaden, the relative proportions of alpha and beta cells are inverted between Baron and Xin. While for DISSECT, they are predicted almost at the same level for the three datasets, which more varying beta cell fractions when Baron is the reference. TAPE-O also achieves this trend, however, TAPE-O incorrectly predicts Delta cells as being at the same level as alpha cells for reference Baron. Linear MLPs show the most variance and predict almost 80% alpha cells for Segerstolpe and Xin. Fur- ther, with Linear MLPs, the least abundant gamma and delta cells are predicted to be negative for almost all samples. Next, we looked at the association between beta cell fractions and T2D severity (Table 14). Across all three datasets, DISSECT estimations are significantly negatively correlated with hblac. As in other experiments, we observe a wide discrepancy between TAPE-O and TAPE-A results.
Deconvolution of GSE81492 with reference scRNA-seq from Park and Mian:
We deconvolved GSE81492 using two single cell reference datasets Park and Miao. Here, we sub- setted these two reference datasets to contain same cell types. We present the results per method per reference in Figure 2 To enable comparisons, same y-axes were used for both single-cell datasets. Both of these datasets come from same technologies (10x Genomics). Despite this, almost all meth- ods show variation in their estimates when changing the reference, wit DISSECT showing the least variance and giving similar associations between cell type fractions and tissue state.
These experiments show that while deconvolution methods in general follow biology but the results differ across the single-cell reference used. DISSECT, however, shows more robustness compared other methods in this regard.
F APPLICATION TO SPATIAL TRANSCRIPTOMICS
Here we illustrate applicability of DISSECT on spatial transcriptomics (ST). We focused on two publicly available tissue slides (Mouse brain and human lymph node) which ST has been per- formed.
In brain, cortical neuronal layers structured spatially. To verify whether DISSECT estimates may be valid in ST, we deconvolved a sagittal mouse brain slice available as part of Seurat 3, As reference, we used a mouse brain single-cell dataset from Allen Brain Institute consisting of approximately 14000 cells sequenced using Smart-seqv2 protocol (Tasic et al., (016). We adjusted simulation procedure to mimic ST datasets. 10x Visium (one of the technologies to generate ST samples)
consists of around 10 cells per spot 4 5 To reflect this, we simulated between 5-12 cells to generate one spot (i.e. Σ ane Cai
[5, 12).. Since ST is much sparser, to generate one spot, we kept between
2-6 cell types. Figure shows fractions of cell types overlaid on the hematoxylin and cosin (H&E) stained images tissue slide, and Figure [T shows jointly cortical neuronal proportions which shows a spatially structured arrangement of neurons.
To evaluate how DISSECT behaves on ST deconvolution on granular level subsets, we deconvolved human lymph node slide using corresponding integrated single-cell datasets on which granular level cell types are annotated. Both of these datasets are obtained from (Kleshchevnikov et al.) 2022}. Remarkably, DISSECT is able to identify spatial patterns of cell type fractions, along with correctly predicting co-localization of cycling B cells and germinal center B cells. This is illustrated in Figures and Several germinal center zones are correspondigly visible in h&E stained image (Figure 13
These results demonstrate the usability of DISSECT on spatial deconvolution and warrant evaluation of consistency loss further in data modalities other than bulk RNA-seq.
gnette html 'Fah spot location which is sequenced in tissue slide. Thus, each spot is analogous bulk RNA-seq. alheit much sparser due to less number cells per spot.
https:: h/sp:::///aiii /ttt /etpe/eettttttttteeeeeeeeeeeeeee
hht /kb. Oxgenomi cs. /t
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G ABLATION
Table 16: Average performance over five random experiments for SDY67 (Table I indicates the additional part.
Each column
Metric
Linear MLP
Activations
KI. Divergence
KI. Divergence + Consistency 0.63 + 0.005
0.51 + 0.018 0.13 + 0.008
0.55 + 0.016
0.54 + 0.006
rmse
0.13 + 0.006
0.11 0.004
0.09 + 0.002
Table 17: Average Pearson correlation between estimations and flow cytometry cell type fractions for the only after simulation phase (Step 2000, À = 0) in comparison to the full training.
Dataset
Simulation-phase only
DISSECT
SDY67
0.359
0.631
Monaco bulk
0.698
0.727
Monaco microarray
0.682
0.786
GSE65133
0.66
0.821
GSE107572
0.53
0.705
GSE120502
0.64
0.662
Table 18: Average RMSE between estimations and flow cytometry cell type fractions for the only after simulation phase (Step 2000, À = 0) in comparison to the full training.
Dataset
Simulation-phase only
DISSECT
SDY67
0.226
0.089
Monaco bulk
0.081
0.06
Monaco microarray
0.139
0.07
GSE65133
0.14
0.06
GSE107572 GSE120502
0.131
0.068
0.09
0.086
H ESTIMATING CELL TYPE SPECIFIC GENE EXPRESSION ON A BRAIN ST SLIDE
To evaluate DISSECT on the estimation of cell type specific gene expression, we utilize simulations from PBMC scRNA-seq datasets (Table 2) This is due to the unavailability of bulk RNA-seq from paired tissue and cell populations. However, to investigate further how DISSECT performs prac- tice, investigated quallity of gene expression estimation for brain ST using ScRNA-seq data from Allen Brain Atlas. Details of both datasets are provided in Section In this experiment, we were interested in answering two questions: 1. Does DISSECT estimates of gene expression reflect what is observed for that cell type in the literature? 2. Can DISSECT identify heterogeneity of the same cell type across samples (in this case spots) without having pre-annotate cell type subsets? To accomplish answering of the second question, we merged excitatory neuronal subsets together and excita- tory neurons after estimation or not. This resulted in 17 final cell types. We also filtered our cells where the proportion of corresponding cell type is less than 1/(no. of cell types). This is reasonable as 10x Visium spots contain between 1-10 cells.
labeled them as "exc neurons". This allows
test whether we observe heterogeneity
The first question relates to accuracy of the predicted signature, and the second question is about whether the biological reality of the sample at hand is preserved. To evaluate our results, first computed PCA and UMAP embeddings of the predicted cell type specific gene expression and identified disjoint cell type clusters (Figure 15). Second,
tested for differential expression (DE) of genes for each cell type. Top 5 DE genes for each cell type are visualized over the ST slide (Figure 16). To verify whether the DE genes make sense in the broader context of the literature,
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performed gene set enrichment using Enrichr with gene sets available from PanglaoDB Franzén et al. ) a curated database of single-cells from different tissues. The results are presented Figures 17 and 18 Correct gene sets are enriched for each predicted signature (e.g. Astrocyte for cluster "Astro", Neurons or interneurons for neuronal subsets such as Lamp5, exc neurons etc., Microglia and Macrophage for "Macrophage", Oligodendrocytes for the cluster "Oligo"). Next, focused on the excitatory neurons. Figure ( shows the expression of expected positive and negative marker genes (taken from the Allen Brain Atlas) over the ST slide. We observe that excitatory neurons do express positive markers but not the negative ones. This positively supports the first of our aforementioned questions.
To investigate whether we observe spatial heterogeneity in the predicted gene expression of excita- tory neurons, we performed unsupervised clustering using louvain clustering with default resolution of 1. Figure 20 the louvain clusters over UMAP and over the ST slide. We observe that clus- ters have spatial variability and may be linked with the location.
verify this further, we looked at some genes, Cux2, Rorb and Fezf2, which are used in creating neuronal taxonomy and in situ val- idation excitatory cell types (Hodge et al. (2019), Tasic et al. (2116). We observe the expression of these genes the correct spatial locations (Cux2, Rorb and Fezf2 in this order with increased depth). Further, since these are only three genes and taken from the literature, we wanted to look into what the genes differentially expressed amongst these 9 clusters indicate. To this end, per- formed DE analysis and used Allen Brain Atlas Up gene sets that are included Enrichr 6 Here we couldn't 't use PanglaoDB as it does not provide detailed taxonomy gene sets of neurons. Figure 21 presents the results of the gene set enrichment. In total, six of the clusters resulted in DE gene sets with default settings (p adjusted value cutoff of 0.01 and absolute log2 fold change cutoff of I). We identify that each cluster is associated with certain brain regions. This positively supports our second question regarding identification of heterogeneity within cell type label.
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GibersorXX
0.0150
Blyngh
0.02
0.01
0.01000
0.000
0.086
0.065
-0.55
0.00
0.000
0.000
0.00
0.000
0.04
0.04
0.06
0,1000
0,1076
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0.100
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0.02
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0.062
0.15
0.1
0.10
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0.060
0.06
0.075
0.15
0.17
0.1775
0.075
0.000
0.06
0.10
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0.15
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0.35
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0.04
0.04
0.08
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0.00
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0,050
0.02
0.005
-0.08
0.00
0.04
0.12
0.00
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0.00
0.10
0.11
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0.755
0.088
0.01
0.088
0.04
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0.02
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0.00
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0.01
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0.0000
0.008
0.10
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0.002
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0.7
0.7
0.6
E
0.6
0.20
0.4
0.5
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0.010
0.007
0.0550
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-0.10
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0.010
0.06s
0.0825
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0.09
Lamph
0.02
0.055
-0.05
0.03
0.000
0.01
0.02
0.10
-0.10
0.000
Figure 5: Estimated fractions of different cell types in 10 bulk RNA-seq samples from GSE8/492 (Ctrl: Control mice, == and APOLI: Apolipoprotein LI transgenic mice, n=4) mice using single- cell reference dataset Park. Corresponding Methods are indicated in the title. Each row corresponds a cell type. DCT: Distal convoluted tubule, Endo: Endothelial cells, LOH: Loope of Henle, Macro: Macrophages, Neutro: Neutrophils, PT: Proximal Tubule, Podo: Podocytes, CD-PC: col- lecting duct principal cell; CD-IC: collecting duet intercalated cell.
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Figure 6: Distribution of relative fractions of alpha, beta, gamma and delta cells (present in all three single cell datasets) estimated on 77 bulk RNA seg samples from GSE52044 using three different single-cell reference datasets. Each row corresponds to the single-cell reference dataset.
Della
Delta
Delta
DISSECT
Gamma mmaa
DISSECT
Gamma
DISSECT
Gamma
Bela
Bela
Beta
Alpha
Alpha
Alpha
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Della
Delta
Delta
Linear ML Ps
Gamma
Linear MLPs
Gamma
Linear MLPs
Gamma
Bela
Bela
Beta
Alpha
Alpha
Alpha
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Delta
Detta
Delta
Gamma
Gamma
Gamma
TAPE-A
TAPE-A
TAPE-A
Beta
Bela
Bela
Alpha
Alpha
Alpha
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Delta
Detta
Delta
Gamma
Gamma
Gamma
TAPE-O
TAPE-
TAPE-O
Beta
Bela
Bela
Alpha
Alpha
Alpha
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Delta
Delta
Delta
Gamma
Gamma
Gamma
Scaden
Scaden
Scaden
Beta
Beta
Beta
Alpha
Alpha
Alpha
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Delta
Delta
Delta
CibersortX
Gamna
CibersortX
Gamma
CibersortX
Gamma
Beta
Bela
Beta
Alpha
Alpha
Alpha
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Delta
Della
Delta
Gamma
Gamma
Gamma
Music
Music
Music
Beta
Beta
Beta
Alpha
Alpha
Alpha
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Baron
X
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Figure Relationship between Hemoglobin AIC (hbal Ic) levels and estimated fractions of beta cells in 77 bulk RNAA-eq samples from GSE52044 using three different single-cell reference datasets. Corresponding Method for each plot is indicated in the title. Each row corresponds to the single-cell reference dataset. Pearson correlation of the relationship and associated p- p-value is indicated in the plot. p-value shown is obtained for beta cells from a multiple linear regression model considering age, sex and bod mass index (BMI) as covariates, Lee. model hblat Constant fractions of beta cells Age Sex BMI.
DISSECT
10.468
6.0001
hbatc
DISSECT
1 40488
99
hbatc
DISSECT
0.0 44.449
#0 0000
hbatc
1.0
0.6
0.4
0.2
0.0
1.0
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
11
0.0
Linear MLPs
r. 10.255
pi0 042
Linear MLPs
p9:112
Linear MLPs
r.0 232
#00512
r.
hba 1c
hbatc
hbate
3
3
0.8
0.4
0.0
1.0
0.8
0.4
0.2
0.0
1.0
0.6
0.4
0.0
r.C.271
0..025
r. 0.083
r-C.415 p0.601
IAPE
hba fc
TAPE-A
TAPE-A
7 hba c
7
7
hbatc
5
5
5
3
0.6
0.4
0.0
1.0
0.6
0.4
0.2
0.0
1.0
0.8
0.5
0.4
02
r. 0.49
800.001
9
1:00 272
p.c:088
F-0 F-0.43
#0.001
TAPE-O
hbat 1c
TAPE-O
hba fc
TAPE-O
hba
5
5
3
0.6
0.4
1.0
0.6
0.4
0.2
0.0
1.0
0.8
0.5
0.4
02
0.0
Scaden
T.0.412
p.o 002
9
hbat6 1c
Scaden
r-0 472
p--0
hbate fc
Scaden
F-0 44
pp:001
hbat fc
0.8
0.6
0.4
0.0
1.0
0.6
0.4
0.2
0.0
1.0
0.6
0.4
12
0.0
Cibersorix
7.0.334
90.016
CibersortX
r:0 332
20-01
CibersertX
r-0 192
p.o.045
hba1c
hbatc
hbatc
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
3.6
0.4
37
5.0
r.03 -3.288
90.193
9
6-3 222
28-462
FC: 86
P0:489
MUSIC
hbatt
MusiC
hbatc
Music
hbatc
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.0 0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
02
0.0
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Figure 8: Fractions of cell types in 463 bulk RNA- RNA-seq, seq samples from ROSMAP Alzheimer's Disease cohort for whom corresponding Braak stages are available. Allen Brain Atlas is used as reference single-co cell data (Table 15]. Rows indicate methods and each column a cell type. OPC: Oligodendrocyee Precursor Cells. Excitatory and Inhibitory are two neuron subsets. Last two columns show fractions of excitatory and inhibitory neurons out of total neuronal content.
Scage
(out of nour of neurons)
Inhibitory
1.00
0.50
0.00
1.00
0.50
0.00
1.00
0.50
0.00
1.00
0.00
1.00
0.50
0.00
1.00
0.50
1.00
0.50
Excitatory
1.00
0.00
0.50
0.00
1.00
0.50
0.00
1.00
0.00
1.00
0.50
0.00
0.50
1.00
0.50
Neurons
1.00
0.50
0.00
1.00
0.50
0.00
1.00
0.50
0.00
1.00
0.50
0.00
1.00
0.50
0.00
0.50
0.00
1.00
0.50
Oligo dendrocyte
0.36
0.00
0.38
0.00
0.36
0:38
0.00
0.33
0:38
0.00
0.36
0:18
0.00
0.36
0.38
0.00
0.36
0.28
0.00
-
OPC
0.30
0.05
0.09
0.30
0.05
0.00
0.30
0.05
0.00
0.20
0.00
0.30
0.05
0.00
0.05
0.00
0.20
0.05
Microglia
0.10
0.05
0.00
0.20
0.00
0.10
0.05
0.10
0.00
0.10
0.05
0.00
0.05
0.10
0.05
Inhibitory
-14111
0.20
0.00
0.10
0.20
0.00
0.00
0.20
0.20
0.00
0.20
0.00
0.00
0.00
0.40
0.20
Excitatory
41:111
0.80
0.30
0.00
0.60
0.30
0.00
0.50
0.30
0.00
0.50
0.30
0.00
0.50
0.30
0.00
0.30
0.00
0.80
0.30
Endothelial
111
0.20
0.30
0.00
0.20
0.30
0.00
0.70
0.30
0.20
0:30
0.00
0,20
0.30
0.00
0.30
0.00
0.20
0.20
Astrocyte
0.20
0.00
0.10
0.20
0.00
0.40
020
0.00
0.40
0.20
0.00
0.40
0.20
0.00
0.40
00
0.00
0.40
0.20
Scaden
TAPE-O
TAPE-O
TAPE-A
Linear MLPs
26

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Under review as a conference paper at ICLR 2023
Figure 9: Estimated fractions different cell types in 10 bulk RNA ses samples from GSE81499 1492 (Ctrl: Control mice, n=6 and APOLI: Apolipoprotein L1 transgenic mice, n=4) mice using two separate single-cell reference datasets (Park and Miao). Corresponding Methods and reference datasets for each plot are indicated the title. Each row corresponds, to cell type. DCT: Distal convoluted tubule, Endo; Endothelia cells, LOH: Loope of Henle, Macro: Macrophages, Neutro: Neutrophils. PT: Proximal Tubule, Podo: Podocyles
DISSECT
APOL.1
Miao
0.4
02
0.0
0.4
0.0
0.4
022
0.0
0.5
02
0.
DISSECT
APOL-
Park
Crll
0.2
0.0
0.4
0.2
00
04
0.2
0.0
0.4
0.2
0.0
0.2
0.1
0.0
0.5
0.2
04
00
APOL1
Linear MLPs
Miao
Ctf
0.4
0.2
0.0
0.4
0.2
02
0.0
0.2
0.0
0.5
Linear MLPs Park
APDL.1
Chrl
0.4
02
0.0
0.4
0.2
0.4
0.2
0.0
0.4
0.2
0.
0.2
0.1
0.0
0.5
0.
011
0.0
TAPE-A
APOL1
Miao
CH
0.4
0.2
0.D
0.4
0.2
0.1
0.
0.0
0.4
0.2
0.1
0.0
0.5
6.2
0,1
0.0
TAPE-A
Park
APOLT
0.4
0.2
0.0
0.4
0.2
0.1
02
0.0
0.4
0.2
0.2
0.1
0.0
0.5
0.0
TAPE-O
APOLT
Miao
Oi
04
02
0.0
0.4
0.2
04
0.2
0.0
0.4
0.2
0.0
0.2
0.1
0.0
0.5
0.1
0.0
TAPE-O Park
APOLI
0.2
0.0
0.4
0,2
0.
02
0.0
0.4
0,2
0.1
0.0
0.5
Scaden Miao
APOLT
0.4
0.2
0.0
0.4
D22
0.4
0.2
0.0
0.4
0.2
0.0
0.2
0.1
0.0
0.5
0.2
0.0
Scaden Park
APGL1
Ci
0.
0.0
0.4
0,7
0.2
0.0
0.4
0.2
0.1
0.0
0.5
00
Cibersortx Miao
AROK11
0.4
0.2
0.0
0.4
0.2
044
02
0.0
0.4
0.2
0.2
0.1
0.0
0.5
0.0
Cibersortx Park
APOLI
04
0.2
0.0
044
0.2
04
0.2
0.0
0.4
0.2
0.0
0.2
0.1
0.0
0.5
81
0.0
Music Miao
APOLT
02
0.0
0-4
0.2
02
0.0
0.4
02
0.2
0.1
00
0.5
0.
MuSic Park
APOL1
Cm
04
0.2
0.0
0.4
041
0.2
0.0
0.4
0.0
0.1
0.0
0.5
80 P
101
Podd
27

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Under review as a conference paper at ICLR 2023
Astmar//s
3R
Froh
0.12
0.10
68
0.5
0.2
0.8
0.00
03
0.04
02
0.1
0.2
0.02
4p02211
14
15
LSPT
L8
0.5
088
0.4
04
04
0.3
6.1
spesa11
spataft
LSIT
L8c
Lamps cal hype
Maacpphuge
6.5
0.6
0.06
0.5
0:128
64
0.3
5000011
0:100
0.3
0.0
02
0.000
0.1
Sprsal1
Squlual Cl geddenchosttec
apalia
Vlee2 callyze
NP
Pell
0.5
608
604
0.2
02
Aprial1
spalell
Pabh of orlsys
Serpei1 cal
SMC
Snog pillyps
04
0.90
0.10
0.2
0.08
004
0.44
002
0.02
spusal1
Sat. cwilyps
Vi cothypn
VUMC:
-.10
0.25
0.20
2000012
0.15
0.0
0.10
02
0.06
spuial
spalaa!!
spafal1
Figure 10: Fractions of all cell types shown H&E image. Each dot corresponds spot quenced in the tissue slice.
28

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Under review as a conference paper at ICLR 2023
IT
L511
03
L5 PT
CT
rin
LAb
030
Figure 11: Cortical neurons overlaid on H&E image. The legend shows fractions.
29

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Under review as a conference paper at ICLR 2023
0.52 P0
00 mm
620
C12
00-399
030
coia
cos
COO
7:50
zen
Figure 12: Estimated fractions of 34 granular level cell types overlaid on H&E image of lymph node tissue slice.
30

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Under review as a conference paper at ICLR 2023
Figure 14: Fractions of Cycling and light zone (LZ) and dark zone (DZ) Germinal center B cells expected to be present in germinal centers.
Figure 13: H&E image of lymph node tissue slice.
31

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Under review as a conference paper at ICLR 2023
A
Celltype
Celltype
VUNIC
Astro
Macrophage
Pvolb
NP
UMAP2
PC2
ViP
Sat
OBC. nourenus
Olige
Part
Serpini'
VLMC
Snico
Macrop Regon
Endoslie
PC1
UMAP1
B
Celltype
Celltype
Serpinf
Sat
newrons
UMAP2
PC2
Sncg
Serpinfi
Pvalb
PC1
UMAP1
exc neurons
Macrophage
C
Pvalb
Sst
Lamp5
Serpinf1
Sncg
Vi
CR
Meis2 NP
Astro
Oligo
Endo
VLMC
Peri SMC
SMC
Peri
VLMC
Endo
Macrophage
Oligo
Astro
exc_neurons
NP
Meis2
Vip
Sncg
Serpinf1
Lamp5
Sst
Pvalb
-1.00 -0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
Figure 15: A PCA and UMAP embeddings of estimated gene expression profiles. PCA and
UMAP embeddings computed on neuronal clusters. C: Clustered matrix showing Pearson correla- tion between each pair of cell types.
32

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Under review as a conference paper at ICLR 2023
1XL13
103
98
Astro
07
83
82
83
97
speroo
spete1
spetat
Oligo
0.
32
64
0.6
66
02
08
129041
30
sptia
800
0.8
1peti]
00
1.8
1.0
1.3
exc neurons
cata0
04
0.4
64
011
0.4
km1
Cepl3
Pap2r2a
CR
Soutult
32
20019
+2
S0AALE
02
12
Prod
C2b2
Leves
Gr132205
Sx2c
108
0.3
Lamp5
64
04
402
64
16
1.0
1.0
Macrophage
6.8
0.
34
03
644
quia1
spetall
Myd16
D6
Ere
Meis2
0.5
3.
64
0.6
06
62
02
0.
34
00
0.
50
1.8
12
1.0
1.
NP
64
8A
6A
Hgdth
Pini
0a
6a
0.8
56
Peri
6-1
82
02
82
02
quta
124tal
Figure 16: Scaled expression of top five DE genes for cell types shown over the H&E tissue slide. Rows indicate cell types.
33

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Under review as a conference paper at ICLR 2023
Astro MangllaDDBAugmeenne _021
Lamp5 PanglaoDB Augmbnted _222
Astrocytes
Bergmann Gila
Neurons
Mitlerr Cells
Purkinje Neurans
Schwann
Oligodendroeytes
Olgodendrocydes
Ncuroendacrine Colls
Enteric Glia Cells
Pyremidel Cells
Setellite Cells
Glycinergic Neurons
Glfe Cells
GABAergic
Sotellite Gilal Cells
Neural Stem/Precursor Cells
Melenocytes
Dopaminergic Neurons
20
24
28
32
36
18
21
(Adjssddd P-value)
(Agj:sAdesttedd P-value)
CR
Macrophage PanglaoDB Augmented 2021
PanglaoDB Augmented 2021
Cajol-Retziur Retzius Cells
Microglie
Intereuronn
Macrophages
Neurons
Monocytes
Enteric Cells
Kupffer Cells
Neural Stem/Precursor Cells
Osteoclast Procursor Colls
Pyramidal Cells
Langerhans
Astrocytes
Dendritic Cells
Purkinje Neurons
Plesmacytoid Dendritic Cells
Tonycytos
Osteoclasts
Chondrocytes
Pulo Macrophages
6
10
16
32
48
56
(Adjuusd P-value)
logioAAdjusted P-value)
Endo
Meis2
PanglaoDB Augmented 2021
PanglaoDB Augmented 2021
Endothelial Cells
Interneurons
Endothelial Calls (Aorta)
Purkinje Neurons
Loop Hanie Cells
Neurons
Hemetopoietic Cells
GABeeiic Neurons
Melanocytes
Neuroblests
Embryonic Stem Cells
Pyramidal Cells
Mesotheliel Cells
Glycinargic Neurons
Microfolo Cells
Meural Stem/Precursor Cells
Sertoi Cells
Ratinal Ganglion Cells
Mast Cells
Retinal Progenitar Cells
12
18
30
42
2
8
10
12
16
(Adjssdd P-value)
(Adjusdd P-value)
Pvalb
Sst
Augmented 2021
PanglaoDB Augonnteddgme 2021
intereurons
Interneurons
Neurons
Neurons
Purkinje Neurons
Purkinje
Neuroendocrine Cells
Oligodendrocytess
Pyremidal Cells
Pyramidal Cells
Olggodendrocyles
Neuroendozrine Cells
GABAergic Neurons
Glycinergic Neurons
Glycinergic Neurons
GABAergic Neurons
Parietal
Retinal Ganglion Gells
Trigaminal Neurons
Stem/Frecursor Calls (Ajjssed P-value)
10
12
12 15
21
Alg.o(Adjusted P-value)
Figure 17: Plots showing gene set enrichment results of each cell type. For each cell type, the DE genes were selected with adjusted value cutoff of 0.01 and absoluted logz fold change cutoff of 1. PanglaoDB Augmented 2021 gene sets were used as background.
34

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Under review as a conference paper at ICLR 2023
NP Aurimented 2021
Serpinf1 PanglaoDB Augmented 2021
Neurons
Intomourons
Intemeurons
Nourons
Oigodendrocyles
Purcijj Neurons
Neuroendoccine Cells
Oigadondnocytos
Pyradeel Celb
Pyramidal ls
Relinal Gangion Celb
Neurzendoccire Ce
Gamme (P9) Cells
Bergman Glaa
Glytinergic Neurons
ChBtergic Neurons
Furkinje Nourons
Neural Stennrrcursor Cels
Glutmiinericc Neurons
Astrocytes
2
6
10
12
14
16
6
9
12
15
18
21
AAjjstdd P-value)
Jogu(Adjjstter P-velue)
Oligo
VLMC
PanglaoDB Auw.mmteddd 2021
PanglaoDB PanglaoDBAuugeented Augmented 2021
Olggadonnoccytrr
Firrbb asts
Schwarn Calss
Perisubuler Mynid Ce
digodennrocyt Progentor Cilss
Meningeal Ce
Miller Cels
Endothelial Co (Aerta)
Astrocytes-
Mosangial Is
Pancrestic Progenior Cels
Rencresti: Ste late Ce
Melarocytes
Pulmonary Vascular Smooth Ce
Enteric Gla Cels
Chondrocytes
Red el Gla Cels
Pericytes
Periislet Schwarn Cels
Smooth Musce e Cels
8
15
24
32
40
56
64
12
18
24
36
42
48
AAjjstddestedd P-velue)
-logu (Adjusted P-velue)
Sncg
exc neurons
PanglaoDB PanglaoDB.Augmennter Augmented 2021
PanglaoDB Augmented 2021
Intemcurons
Intereurons
Yeurons
Oligodentocyless
Purcinja Neurons
Neurons
Pyramial Cels
Neurtendocrine Gells
Entere Gla Co
G4BAergic Nourons
Puncinje Neurons
Enteric Cells
Neursendacrine Ca
Pyramidal Cells
Tanysytos
Glyinerii Neurons
Gutaminergic Neurons
Gltamineriic Neurons
Dopaminergic Neurons
10
14
16
2
10
12
(Adjsstdee P-value)
AAjjssddes P-valus)
Peri
SMC
MingllaoD-Augmenten 2021
PanglaoDB Augmented 2021
Pericytes
Smooth Muscle Cells
Wesangial Cells
Pericytes
Aciooyyt Progenizor Cells
Wesergial
Airway Smooth Muscle Cells
Adipocyte Progenitor Cells
Smooth Muscle Cells
Satellite Cells
Pancrestic Stellte Gells
Fibroblasts
Fbroblsts
Airway Smooth Mussle
Loop Hemle Cells
Peritusu Nyoid Cells
Pulmonary Vascular Smooth Muscle Coll:
Mynttasts
Hyofbrabastss
Mesothellaa Cells
16
24
28
10
20
25
30
35
(Adjuude P-value)
-log (Adjusted Pvaluoe
Figure 18: Continued from Figure 17
35

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Under review as a conference paper at ICLR 2023
Sk2767
vd2
Neurod6
Posítiva markers
7.00
5.8
5.0
5001531
6.50
48
46
6.00
Gho
Oligg
0.30
0.25
0.25
0.06
0.20
0.20
0.15
0.15
0.04
0.15
Nagaticva markers
0.02
0.10
0.05
Ctss
Gad:
Gad2
Glde5
0.12
0.6
05
0.08
Spatii
02
0.4
0.04
02
0.02
5o63all
0.00
SpatialL
0.0
SpacatL
spatatl
0.0
Figure 19: Evaluation of known positive and negative marker genes for excitatory neurons. The negative marker genes are the genes highly expressed on other cell types compared to excitatory neurons. Sox10, Oligl: Oligodendrocytes, Sst: Sst neurons, Gfap: Astrocytes, Clss: Microglia, Gadl, Gad2; Inhibitory neurons, Cldn5: Endothelial cells.
36

---

Under review as a conference paper at ICLR 2023
buurah
DuK2
Rorb
Foot2
2.5
25
URNA72
LRAFFZ
2.0
2.0
LEMF2
1.5
0.5
UMAPI
UMAP1
0.0
0.0
UIMAPT
louvain
Cux2
OCL 653 29
spatial2
Rorb
5
001 638
Fezf2
6
3.40
8
spatiall
Figure 20: Top: (From left right) Louvain clustering on estimated gene expression on excitatory neurons. log2 gene expression of Cux2, Rorb and Fez/2. Bottom left: Louvain clusters identified on estimated excitatory neurons visualized on top of H&E slide and Bottom right: Expressions of Cux2, Rorb and Fezf2 in excitatory neurons jointly visualized over H&E slide.
37

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Under review as a conference paper at ICLR 2023
Cluster: Allen Brain Atlas up
Cluster: 5
Allen Brain Atlas up
interior dinguiste area, verbri part
Prinery somatoeerssry
subloyer of CDx
Somstaeersoy sory area
Anterior cingulite aesa part,
letengalisal presiffor
Prelimbic area. layer
lsyer of (PP cortox
byar
martle zone
Artarior cingulte arso, dorsal part. layer
mentle zone PCx
Restubicuikm kye:
pareta contex
CDX
ploteimargins
layer 5 R65
Primary somelsseroory area, unessigre layer
Au5
layer 3
-ogg.IAdjastee P-valoe)
loqusAddsstedd P-value)
Cluster: 1
Cluster: Allen Brain Atlas up
Allen Brain Atlas up
Prirary so ara
intomeed aie strata
Somstosensoy
layer 1 VhP corter
laye: CCe
supertosi PDx pltbinargins
marte zone Fo
subethraid nackus
porittal
renro curtens madeus
superficiel strttum PCx icorical mazmmrrgiaat tel zonel
parvicalu uar sussontusocuue
parketal corter
mande zore of UNF-
ovi pa proceettall ractuss
PC+
layer 2 CCe
Inniral naclss
4og LAduuteee Punllae)
-ag::(Adusted Pretuc)
Cluster: Allen Brain_Atias up
Cluster: Allen Brain Atlas up
subpslium
Anterao dingu late ares, ventral part, fa
ope CCe
Prelimbic area layer Eb
Anteder ding area ventral gart,
sublayar
bed nuciecs of edteral cepule
"benia texto. darsal part, layer 4
part layer
layer
Bed nuctee of the strie terninalss, pasterfor diviion, strill exters on
lape CDk
Caudeputareen
ofS
Carebral ruda-
Dorsal padurcuaar aria layer
Nucless accumbers
Anterio cingu lato area, vertral part Sb
Stratum
lite part, laye
dogudAduutted Puvalae)
Jag:/Adjussed Pvoous))
Gene set enrichment of DE genes computed for each louvain cluster on excitatory neu- rons. Enrichr was used to perform gene enrichment of each DE gene with Allen Brain Atlas Up gene sets as background. The x-axis indicated -logı0 adjusted p-value. A higher value indicates greater significance. The y-axis lists the enriched gene sets ordered by decreasing significance.
Figure 21:
38


Reviews:
[{"review_id": "hKuZKCihL9g", "reviewer": "Reviewer_w6RR", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "Authors proposed DISSECT as a novel deconvolution framework estimating both celltype fractions and celltype specific gene expression. DISSECT leveraged both the training data with ground-truth proportions and simulated data based on scRNA-seq reference. It can be seen as an augmentation strategy which mixes the training data with synthetic data or a regularization framework that solves the domain shift problem between the training domain and reference (scRNA-seq) domain. Authors completed extensive evaluations over a large collection of datasets and compared DISSECT with other competitive methods. Authors highlighted the superior performance of DISSECT and provided a possible extension on applying DISSECT on the spatial transcriptomic data which was a new application scenario.\u0000", "summary_of_the_review": "The manuscript is not ready yet to be presented in its current form. Concerns raised above need to be addressed.\n\n\nResponse to authors\n\nThank you for the detailed response. Most of my concerns have been addressed properly. I still have two minor comments:\n1. I am confused that the consistency loss will be zero for linear MLP as I follow the definition of consistency loss in equation 6.\n\n2. Thanks for including appendix table 13 and 14. It will be great if they can be merged to table 3 and table 4 as one separate column accordingly. Also the column name of the first field in table 13 should be updated from \"Dataset\" to \"Metrics\" or something similar.\n\nI will keep my scores for now.", "strength_and_weaknesses": "The manuscript was well written and included major players in the field of celltype deconvolution to compare in the computational experiments. The shared code repository was helpful to better understand the algorithm. There are some experiment details that need to be clarified as follows.\n\nMajor Concerns\n\n1.\t(section 4.3.2) The experiments estimating celltype-specific gene expression was only conducted on the simulated bulk RNA-seq data. This is insufficient to evaluate how DISSECT behaves in practice. One possible source of verification data can be imaging-based spatial transcriptomics data [1], as authors have tried applying DISSECT on the spatial transcriptomic data.\n\n2.\t(section 3) Authors constructed the Linear MLPs removing the consistency loss which may be not a fair comparison. I am wondering how Linear MLPs would behave if the consistency loss was incorporated.\n\n3.\t(section 2.2.1) The first training phase was purely based on the simulated examples. I am curious to see what the performance of DISSECT is right after this simulation-based training phase. This can be seen as an ablation test to check how mixing the training data with training data helps improve the deconvolution performance.\n\n4.\tI didn’t find descriptions on how the training/validation/test sets were split for each dataset. Could you elaborate on this?\n\nMinor Concerns\n\n1.\tScaden [2] included both SDY67 (called PBMC1) and Monaco (named PBMC2) for comparison in their manuscript. Their reported correlations were different compared to those reported in Table 3. I am wondering what could possibly lead to this difference.\n\n\n[1] Cable, Dylan M., et al. \"Cell type-specific inference of differential expression in spatial transcriptomics.\" Nature methods 19.9 (2022): 1076-1087.\n\n[2] Menden, Kevin, et al. \"Deep learning–based cell composition analysis from tissue expression profiles.\" Science advances 6.30 (2020): eaba2619.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "LuEwhHMzt5", "reviewer": "Reviewer_qKLA", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors present DISSECT, a method for estimating cell fraction and gene expression from bulk RNA sequencing data. The primary innovation in DISSECT is the introduction of a regularization term during training that encourages similar expression profiles between simulated bulk data (drawn from scRNA-seq data) and real bulk RNA-seq data. A multilayer perceptron is used to estimate cell-type proportions and an autoencoder is used to estimate cell-type specific gene expression profiles. The authors evaluate their method on several real bulk RNA-seq datasets of PBMCs.", "summary_of_the_review": "The current state of the evaluation and its limitations, as mentioned above, are the primary weaknesses of the work in its current state. A more extensive evaluation, following the analysis done in these other works in deconvolution, would greatly strengthen the paper. But in its current state, it is not ready for publication.", "strength_and_weaknesses": "I believe the authors are pursuing a good research direction within the field of RNA-seq deconvolution, with the incorporation of a consistency loss into training, and this problem is important for biological studies, but this field has been studied extensively and to make a contribution to it, a more thorough investigation is needed. \n\nStrengths:\n- The problem of RNA-seq deconvolution is of great importance for many biological studies.\n- Adding a consistency loss is a good insight for addressing the issue of differing characteristics between scRNA-seq and bulk RNA-seq.\n- The authors are aware of other primary competing methods in the field of cell-type deconvolution.\n- The authors have clearly laid out the logic of their reasoning about the consistency loss and the math details are explained clearly and are easy to follow.\n- Overall, the paper is well-written.\n\nWeaknesses:\n- As mentioned, there has been extensive work toward solving the problem of deconvolution. The authors have only applied their method to datasets of PMBCs, and if I understand their training procedure correctly for cell-type estimation, only used the PBMC8k dataset as the single-cell reference (or did they use all 4 from Table 1?). Even so, these datasets are from one individual each (per the description in Table 1), thus the datasets are limited in their ability to capture possible person-to-person or technical variation. Thus, it could be that their advantage comes largely from this limitation. In comparison, MuSiC compared to several different tissue types and used scRNA-seq data of 10-20 individuals. To make a compelling case, the authors need to apply to more tissue types and use a more diverse set of reference single-cell datasets. (The authors include results for some other types of data in the Appendix, but need to provide quantitative comparisons and an explanation of the results.)\n- In particular, the authors should consider doing an evaluation that exactly compares to some of those reported in MuSiC or (Avila Cobos et al., 2020); this would give greater confidence that their evaluation matches what is reported in those works.\n- Furthermore, as was shown in the benchmarking study of (Avila Cobos et al., 2020), the normalization procedure can have dramatic effects on the results of deconvolution. The authors comment in the Appendix C.3 that their procedure is standard for Scaden, but given the potential dramatic impact, more reasoning should be provided for the other methods as well, especially in light of the analysis of (Avila Cobos et al., 2020).\n- The incorporation of the consistency regularization, while sensible and a good consideration, is not in itself a significant algorithmic advance, at least for the scope of ICLR.\n- There are other methods that are not referenced: Bisque (Jew et al., Nature Communications 2020) and DWLS (Tsoucas et al., Nature Communications 2019). The authors are correct that MuSiC and Cibersort are the main established methods, but still some reference should be made to these methods to distinguish theirs and a comparison against them would strengthen their work.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "EMv5lMfYMK", "reviewer": "Reviewer_W86N", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposed DISSECT, a regularization-based deep learning approach for bulk RNA-seq deconvolution that decomposes the bulk RNA-seq matrix into a cell type fraction matrix $X$ and an expression profile $S$. The authors introduced consistency regularization which utilizes a mixture of the true bulk RNA-seq and simulated RNA-seq data. The proposed learning paradigm includes two deep networks, MLP and VAE, estimating X and S, respectively. The authors reported the model performance for multiple bulk RNA-seq datasets and showed DISSECT outperforms the competing approaches in both cell fraction and gene expression estimation tasks. \n\n\n**Update after rebuttal:** I have read all reviewers' comments and the authors' responses. I still think the paper lacks enough contribution as a machine learning paper and my score remains unchanged.", "summary_of_the_review": "I think the paper has limited contributions to the machine learning field and the manuscript is not sufficient for a ML focused venue like ICLR. ", "strength_and_weaknesses": "Strength:\n\n- The authors suggested a simple regularization loss that factorizes bulk RAN-seq and estimate cell type-specific expression with limited number of cells.\n\n- The authors reported their model performance for several RNA-seq data and demonstrated the outperformance of their regulation-based deconvolution in compared with other deconvolution methods, \n\n\nWeaknesses / Concerns / Comments:\n\n- The paper does not have sufficient contributions. There is no justification for the consistency term and it is not clear to me that why such a simple linear formulation can improve cell type fraction approximation.\n\n- Is there sample-to-sample correspondence between $B_i$ and $B_i^{sim}$?\n\n- While bulk measurements and simulated dataset (based on single-cell measurements) consist of different sources of noise and biological variabilities, can you justify why a linear mixture of these two cell measurements can enhance cell deconvolution? \n\n- Is there any equation missing after the third equation, Section 2 (page 2)?\n\n- In Section 2, 4th equation, why does the signature matrix which is a $c$ x $n$ matrix is a function of three indices, including sample id? Does not $S$ represent the expression profiles across cell types? \n\n- Section 2.1.2, Eq. 4 should be written as $x_i s_i$, not $s_i x_i$.\n\n- Is the number of cell types given to the model apriori? How does the deconvolution performance change in the absence of having the true number of cell types in the bulk RNA-seq data?\n\n- In Figure 1.a, I think the first “+” operation should be “=”.\n\n- Why do you use a variational approach to learn signature matrix? Why not just an MLP, similar to $X$ matrix estimation? Is there any plan to use the latent factors? \n\n- How many genes are profiled in datasets in Tables 1 & 2? Are you using the same subset of genes in Eq. 3?\n\n- I think reporting MSE (which is reported in the supplement) is more informative rather than reporting correlation between estimated variables and true ones. My suggestion is to report MSE results in the main text.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "7qnHuYcGJha", "reviewer": "Reviewer_fXFv", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors propose a new model for cell deconvolution task by introducing consistency regularization. \n", "summary_of_the_review": "The authors propose a new model for cell deconvolution task by introducing consistency regularization. The paper really focusing on a particular task in cell biology thus more specific venue is suitable.\n", "strength_and_weaknesses": "Strength\n\n- The proposed model was extensively tested against existing models using various datasets.\n- The proposed model showed superior preformance among compared.\n\nWeaknesses\n\n- Whole contents of this paper is focusing on a particular task in cell biology. Very domain specific.\n- It is unclear how the model was trained. Which dataset was used?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject