Abstract
The advent of single-cell sequencing has revolutionized the study of
cellular dynamics, providing unprecedented resolution into the
molecular states and heterogeneity of individual cells. However, the
rich potential of exon-level information and junction reads within
single cells remains underutilized. Conventional gene-count methods
overlook critical exon and junction data, limiting the quality of cell
representation and downstream analyses such as subpopulation
identification and alternative splicing detection. We introduce
DOLPHIN, a deep learning method that integrates exon-level and junction
read data, representing genes as graph structures. These graphs are
processed by a variational graph autoencoder to improve cell
embeddings. DOLPHIN not only demonstrates superior performance in cell
clustering, biomarker discovery, and alternative splicing detection but
also provides a distinct capability to detect subtle transcriptomic
differences at the exon level that are often masked in gene-level
analyses. By examining cellular dynamics with enhanced resolution,
DOLPHIN provides new insights into disease mechanisms and potential
therapeutic targets.
Subject terms: Machine learning, Computational models, Software, Data
processing
__________________________________________________________________
Single-cell RNA-seq analysis is conventionally limited to gene-level
quantification, missing transcript diversity. Here, authors present
DOLPHIN, a deep learning method that enables exon- and junction-level
analysis to improve cell representation and detect alternative
splicing.
Introduction
Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomics
by enabling the profiling of gene expression at the level of individual
cells, a major advance in studying cellular diversity within complex
tissues^[34]1. This technology has driven significant progress across
fields such as developmental biology^[35]2,[36]3,
immunology^[37]4,[38]5, and cancer research^[39]6,[40]7, revealing
intricate cellular landscapes, elucidating developmental pathways, and
identifying previously uncharacterized cell types linked to disease
states^[41]8,[42]9. By enabling high-resolution dissection of cellular
states and dynamics, scRNA-seq provides insights that bridge basic
biological understanding with therapeutic applications, reshaping both
basic and translational research.
Despite these advancements, conventional scRNA-seq analyses are
predominantly gene-level, relying on gene count tables for cell
representation learning and downstream tasks such as cell clustering,
differential gene expression, and pseudotime trajectory
inference^[43]10. Numerous computational tools, including
SCANPY^[44]11, seurat^[45]12, scVI^[46]13, scGPT^[47]14,
geneFormer^[48]15, scBERT^[49]16, scSemiProfiler^[50]17, and
Cellar^[51]18 are designed to analyze this gene-level data. However,
aggregating data at the gene level oversimplifies the transcriptomic
landscape, as critical biological information encoded in exon-level
reads and junction reads—reads spanning exon boundaries and capturing
exon connectivity—is often lost^[52]19,[53]20. This simplification
masks essential details, including exon-specific expression and
splicing patterns, which are crucial for accurately representing
cellular states. Consequently, gene-level aggregation may lead to an
oversimplified view of cellular characteristics, limiting insights into
cellular function and regulation and underscoring the need for
approaches that preserve this fine-grained information^[54]21.
In addition to cell representation learning, another critical task in
scRNA-seq analysis is the detection and quantification of alternative
splicing (AS) events. AS analysis at the gene level poses substantial
challenges, as gene-level quantification obscures isoform-specific and
exon-specific variations that are critical for capturing splicing
dynamics. To address this, various computational tools have been
developed for AS analysis in scRNA-seq data. Among junction read-based
approaches, Outrigger^[55]22 constructs a de novo splicing event index
by pooling junction-spanning reads across all cells and building a
splice graph to identify and quantify AS events. scQuint^[56]23 adopts
a different strategy by quantifying intron usage based on junction
reads. To improve splicing quantification under sparse conditions,
imputation-based methods such as BRIE2^[57]24 and SCASL^[58]25 have
been developed. BRIE2 employs a Bayesian hierarchical model to borrow
information across similar cells and infer more robust Percent
Spliced-In (PSI) estimates, whereas SCASL uses an iterative weighted
k-nearest neighbors (KNN) strategy to impute missing PSI values.
Despite these methodological advances, major gaps remain. Most existing
tools were developed and benchmarked primarily on full-length scRNA-seq
datasets, and their performance degrades substantially when applied to
droplet-based platforms such as 10X Genomics, where coverage is sparse
and biased toward transcript ends. Furthermore, nearly all methods
predominantly rely on junction-spanning reads for splicing
quantification. This reliance can limit sensitivity and robustness,
especially in the context of scRNA-seq, where sparse coverage and
frequent dropout render junction reads insufficient for capturing the
full extent of splicing variability. Additionally, the exclusion of
exon body reads, which represent a more abundant yet underutilized
source of information, can reduce the sensitivity of existing methods
in detecting subtle or complex splicing events that may be missed due
to the sparsity of junction reads in scRNA-seq data.
To address these foundational limitations, we introduce DOLPHIN (
[MATH: D_eep
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-
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Neural Network for
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-cell
[MATH: Representation_ :MATH]
Learning and Alternative Splicing), a deep learning framework that
advances scRNA-seq analysis beyond conventional gene-level
quantification. DOLPHIN constructs a graph for each gene, representing
exons as nodes and splice junctions as edges, to model gene
architecture at single-cell resolution. By integrating exon-level reads
and junction reads, DOLPHIN captures a richer and more detailed
transcriptional landscape compared to traditional approaches that rely
solely on gene-level counts. Built on a variational graph autoencoder
(VGAE) framework^[59]26,[60]27, DOLPHIN learns cell embeddings that
preserve fine-grained exon usage patterns and splicing information,
enabling more accurate and informative representations of cellular
states. These enhanced embeddings not only improve downstream analyses
such as cell clustering and differential gene analysis but also support
more sensitive AS detection. Specifically, DOLPHIN uses the learned
embeddings to identify neighboring cells with similar exon and splicing
profiles, aggregates junction reads across neighbors to amplify
splicing signals, and substantially enhances AS detection under the
sparse sequencing conditions typical of scRNA-seq. Following
aggregation, PSI values are calculated using the Outrigger function
from Expedition^[61]28, providing accurate and robust quantification of
splicing events across diverse cell populations.
We demonstrate DOLPHIN’s general applicability by validating its
performance on a diverse set of scRNA-seq datasets^[62]29–[63]31 that
encompass distinct sequencing technologies, including full-length and
droplet-based approaches, as well as a broad spectrum of tissue types
and biological conditions. These datasets span healthy tissues, normal
tissues from patients with cancer, and malignant tissues, thereby
representing a wide range of physiological and pathological contexts.
This systematic validation highlights DOLPHIN’s robustness and
adaptability, demonstrating its effectiveness in accurately capturing
cell heterogeneity and refining complex downstream analyses across
diverse experimental contexts. Across diverse scRNA-seq datasets and
simulated data, our model consistently outperforms traditional gene
count-based methods. By integrating exon-level and junction read
information with advanced deep learning techniques, DOLPHIN enhances
the resolution of single-cell transcriptomic analysis, improving cell
embedding quality and enabling more detailed analyses of AS and
differential gene expression. Ultimately, DOLPHIN provides an
analytical framework that addresses the limitations of gene-count-based
methods, enabling more precise insights into complex cellular processes
and facilitating the study of disease mechanisms and therapeutic
targets.
Results
Overview of DOLPHIN
DOLPHIN is a deep learning framework for exon-level analysis of
scRNA-seq data, offering higher transcriptomic resolution than
traditional gene-count methods (Fig. [64]1). Each gene is modeled as an
exon graph, where nodes represent exons and edges represent their
connections via junction reads. By integrating exon and junction data,
DOLPHIN generates integrative cell representations that support
applications like cell clustering, differential exon analysis, and AS
detection^[65]19,[66]32,[67]33.
Fig. 1. Method overview of DOLPHIN for exon-level single-cell RNA-seq data
analysis.
[68]Fig. 1
[69]Open in a new tab
a Preprocessing of single-cell RNA-seq data, including quantification
of exon-mapped reads and exon-exon junction reads. b Construction of
gene-specific exon graphs, where nodes represent exons and edges
represent junctions, aggregated to form an exon graph for each cell. c
Learning cell embeddings from exon-level quantification and junction
reads through a Variational Graph Autoencoder (VGAE). Each exon graph
is converted into feature matrices (X[i]) and normalized adjacency
matrices (AN[i]), which are processed by a Graph Attention Network
(GAT) layer to capture exon dependencies. The output (H[i]) from the
GAT layer is then passed to a Variational Autoencoder (VAE) that
projects graph representations into a latent space (Z), defined by mean
(μ) and standard deviation (σ) parameters, with a KL divergence term
weighted by a hyperparameter (β) to regularize the latent space. The
decoders reconstruct both the feature matrix (
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) and raw adjacency matrix (
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:MATH]
), with losses weighted by a hyperparameter (λ) to minimize feature and
adjacency reconstruction errors, thereby learning cell-specific
embeddings. d Construction of a K-nearest neighbor (KNN) graph in the
latent space for refining and aggregating junction reads from
neighboring cells based on consensus (majority voting), which enhances
junction coverage for downstream splicing analysis. e Calculation of
percent-splice-in (PSI) values from aggregated junction reads, enabling
accurate alternative splicing inference at the single-cell level. f
High-resolution cell embeddings generated by DOLPHIN improve the
characterization of cellular heterogeneity compared to conventional
gene count-based methods. g Detection of exon-specific markers and
identification of biological pathways that are often missed in
gene-level analyses. Exon-level biomarkers were identified through
differential expression analysis using MAST. h Extensive alternative
splicing analysis enabled by DOLPHIN across diverse cellular
populations. By default, PSI values and splicing modalities were
quantified using Expedition. However, DOLPHIN can be adapted to work
with other alternative splicing quantification tools.
The method operates in three main steps. First, DOLPHIN constructs an
exon graph for each gene by capturing gene-specific exon connectivity
from junction reads (Fig. [70]1a, b)^[71]19,[72]33. Raw scRNA-seq reads
are aligned to a reference genome to identify exon reads and junction
reads, which are then used to build exon graphs. Each exon graph has
nodes representing exons annotated with their read counts and
directional edges weighted by normalized junction read counts. This
setup forms a cell-level structure comprising exon graphs for each
gene. Second, these cell-level exon graphs are processed through a
VGAE^[73]26 to produce informative cell embeddings (Fig. [74]1c). Each
cell-level exon graph is converted into adjacency and feature matrices,
which are processed by a graph attention (GAT) layer^[75]34. The GAT
layer dynamically assigns weights to neighboring exons, emphasizing
biologically relevant exon connections informed by junction reads. The
variational autoencoder (VAE) encoder then learns a latent
representation Z that captures critical exon-junction relationships,
optimized through a composite loss function that balances
reconstruction of both exon-level features and adjacency
matrices^[76]35,[77]36. Third, DOLPHIN addresses the limited detection
of junction reads in scRNA-seq by aggregating junction reads from
similar cells in the junction-aware latent space (Fig. [78]1d)^[79]37.
Using a KNN approach, cells with similar exon and junction patterns are
identified, and junction reads from those neighboring cells are
aggregated based on majority voting. This aggregation step,
schematically illustrated in Fig. [80]1e, enriches each cell’s profile
with junction reads from consistent neighboring cells, enhancing
detection sensitivity without introducing noise.
With these enhanced cell embeddings, DOLPHIN supports exon-level
analyses such as refined cell clustering (Fig. [81]1f), differential
gene analysis at the exon level (Fig. [82]1g), and AS detection
(Fig. [83]1h). By integrating these embeddings with splicing detection
tools like Outrigger from the Expedition suite, DOLPHIN can compute PSI
values, providing detailed insights into exon usage and cell-specific
splicing patterns^[84]22,[85]28.
DOLPHIN enhances cell embeddings across diverse single-cell scenarios through
graph-based exon and junction read integration
DOLPHIN enhances cell embeddings through the graph-based integration of
exon-level and junction read quantification, leveraging both read types
to improve the quality of cell embeddings and the accuracy of scRNA-seq
clustering compared to traditional gene count methods. To demonstrate
the general applicability of DOLPHIN, we validated its performance
across diverse scRNA-seq datasets spanning different platforms, tissue
types, and biological conditions. These included a full-length dataset
from human peripheral blood mononuclear cells (PBMCs)^[86]29 and two
10X Genomics Chromium Single Cell 3′ v2 datasets from normal epithelial
colon and rectum tissues from gastrointestinal cancer patients^[87]30.
For each dataset, we processed four inputs through the VAE framework–an
exon feature matrix, a junction-based adjacency matrix, a gene count
table, and the integrated feature and adjacency matrices from DOLPHIN.
These components were assessed individually to evaluate their
contributions and the enhancement achieved through integration.
Clustering outcomes were compared to ground truth labels using Uniform
Manifold Approximation and Projection (UMAP) visualizations^[88]38,
with the ground truth annotations taken from the original publications,
as shown in Fig. [89]2a–d and Supplementary Fig. [90]S1. Additionally,
Adjusted Rand Index (ARI)^[91]39 and Normalized Mutual Information
(NMI)^[92]40 scores were used for quantitative evaluation, as presented
in Fig. [93]2e–g. DOLPHIN’s integrated embeddings consistently
outperformed individual matrices and gene count tables, capturing cell
type-specific information at finer resolution and achieving higher ARI
and NMI scores, as demonstrated in Fig. [94]2.
Fig. 2. DOLPHIN enhances cell embedding quality through exon and junction
read integration.
[95]Fig. 2
[96]Open in a new tab
a–d UMAP plots comparing the quality of cell embeddings generated by
DOLPHIN, which integrates both exon and junction read counts, against
conventional gene count-based methods across multiple single-cell
RNA-seq datasets. Improved clustering and separation of distinct cell
populations define higher-quality embeddings. Top panels: Human
peripheral blood mononuclear cells (PBMCs) analyzed using full-length
single-cell RNA-seq. Middle panels: Human colon cells analyzed using
10X Genomics. Bottom panels: Human rectum cells analyzed using 10X
Genomics. For each dataset, the following inputs are compared: a
DOLPHIN integrating both exon and junction read counts, producing the
most integrative and biologically informative embeddings. b DOLPHIN
framework using gene count tables, reflecting a conventional gene-level
analysis. c DOLPHIN using only exon read counts (feature matrix). d
DOLPHIN using only junction read counts (adjacency matrix). e–g Box
plots of Adjusted Rand Index (ARI) and Normalized Mutual Information
(NMI) scores comparing embedding quality across three different
datasets. DOLPHIN, through the integration of exon and junction read
counts, achieves significantly higher scores than approaches using exon
or junction data alone or conventional gene-count methods. These
metrics confirm DOLPHIN's superior clustering accuracy and alignment
with known biological cell types, highlighting its performance
advantage. Each score is based on N = 50 bootstrapping replicates using
different random seeds (technical replicates). Boxes indicate the
interquartile range (IQR, 25th to 75th percentile), with the line
inside each box representing the median. Whiskers extend to the most
extreme data points within 1.5 times the IQR from the quartiles. P
values from one-sided Student’s t-tests: *P < 0.05, **P < 0.01,
***P < 0.001, ****P < 0.0001; n.s. not significant. Exact P values are
provided in the source data. Source data are provided as a [97]Source
Data file.
In the PBMC dataset, UMAP visualizations illustrate that DOLPHIN
distinctly delineates cell clusters closely matching ground truth cell
types (Fig. [98]2a, top panel). In contrast, gene count tables yield
denser clusters, obscuring subpopulations, particularly within T cell
subsets (Fig. [99]2b, top panel). The feature matrix and adjacency
matrix are each able to resolve specific cell types, including
monocytes (Mono), B cells, and natural killer cells, into distinct and
well-defined clusters (Fig. [100]2c, d, top panels). This suggests that
both matrices effectively capture biologically relevant variations,
facilitating the accurate identification of cell populations.
Furthermore, their integration in DOLPHIN provides the most refined
results. Supplementary Fig. [101]S2a highlights the abundance of exon
and junction reads in full-length data, sufficient for constructing
robust exon-level graphs for cell representation learning. Quantitative
analysis with ARI and NMI metrics (Fig. [102]2e) shows that DOLPHIN
achieves median ARI scores 0.11 higher than gene count methods, with
statistical significance (P = 1.98 × 10^−4).
For UMI-based platforms with limited gene coverage, DOLPHIN was applied
to two 10X Genomics datasets. In the human colon dataset, UMAP plots
show that DOLPHIN mitigates batch effects and produces well-defined
clusters for Paneth-like, Goblet, and transient amplifying (TA) cells
(Fig. [103]2a, middle panel). By contrast, the gene count table
exhibits batch effects, blurring cell type boundaries (Fig. [104]2b,
middle panel). The batch effect was evaluated in Supplementary
Fig. [105]S3a, b, where we show that the DOLPHIN method exhibits
significantly less batch effect compared to the gene count table
approach. Notably, the integration LISI (iLISI) score showed the most
substantial improvement, increasing from 0.01 with the gene count table
to 0.82 with DOLPHIN, with P = 1.38 × 10^−23. Across multiple
evaluation metrics, DOLPHIN demonstrated superior performance in
reducing batch effects relative to the gene count table method. The
feature matrix delineates Goblet and Paneth-like cells, while the
adjacency matrix captures broader cell-type patterns with slightly
diffuse boundaries (Fig. [106]2c, d, middle panel). DOLPHIN’s
integrated embeddings achieve the best clustering accuracy, with ARI
and NMI improvements of 0.10 and 0.08, respectively, over gene count
tables (Fig. [107]2f). These results were statistically significant
(P = 4.56 × 10^−25 and P = 4.85 × 10^−42, respectively), highlighting
DOLPHIN’s robustness for low-coverage datasets.
Similarly, in the 10X rectum dataset, DOLPHIN improved clustering
performance, effectively resolving Enterocyte and Goblet cell
populations, as seen in UMAP plots (Fig. [108]2a–d, bottom panel). ARI
and NMI metrics further confirmed its advantage, with improvements of
0.11 (P = 1.98 × 10^−44) and 0.09 (P = 1.05 × 10^−33), respectively,
compared to gene count tables (Fig. [109]2g). We also compared the
batch effect between the gene count table and DOLPHIN, as shown in
Supplementary Fig. [110]S3c, d, where the iLISI score increased from
0.06 with the gene count table to 0.40 with DOLPHIN with
P = 4.91 × 10^−13. These findings demonstrate DOLPHIN’s adaptability to
diverse datasets and its ability to detect biologically meaningful
patterns even under the 10X tag-based platforms, where exon and
junction reads are much less abundant, as shown by their distribution
in Supplementary Fig. [111]S2b, c.
Robustness of cell embeddings against batch effects is critical for
accurately capturing biological variation in scRNA-seq data. We
evaluated the robustness of DOLPHIN embeddings by conducting two
complementary analyses. First, we assessed DOLPHIN’s default embeddings
without applying any external batch correction to the input features.
As shown in Supplementary Fig. [112]S3, DOLPHIN’s exon-level modeling
inherently mitigates batch-driven separation, resulting in robust cell
embeddings even under uncorrected conditions. To further strengthen
this evaluation, we compared DOLPHIN embeddings against standard batch
correction methods. Specifically, we applied Harmony^[113]41 and scVI
to perform batch correction on the gene count matrix, and separately
applied scVI to correct batch effects in the exon-level feature matrix
prior to DOLPHIN embedding. In contrast, Harmony operates only on
low-dimensional embeddings and is not compatible with exon-level
feature correction before DOLPHIN. As shown in Supplementary
Fig. [114]S4a–c, while all approaches reduced batch-driven separation,
DOLPHIN embeddings derived from scVI-corrected exon inputs achieved the
best batch mixing. This observation is further supported by
quantitative metrics in Supplementary Fig. [115]S4d, which assess both
biological conservation (ARI, NMI) and batch correction performance
(batch average silhouette width (ASW), graph
connectivity)^[116]42,[117]43. Notably, applying Harmony to gene-level
embeddings improved the median ARI from 0.26 to 0.41
(P = 1.71 × 10^−7), whereas DOLPHIN with batch-corrected exon inputs
achieved a higher ARI of 0.49 compared to the Harmony-corrected gene
count matrix (P = 2.09 × 10^−4), indicating superior preservation of
biological structure. DOLPHIN embeddings also exhibited the highest
median Batch ASW and comparable graph connectivity to Harmony,
reflecting strong batch mixing while maintaining biological relevance.
Together, these results demonstrate that DOLPHIN’s exon-level
embeddings are inherently robust against batch effects and can achieve
even greater performance when built upon batch-corrected exon-level
inputs.
Beyond clustering, DOLPHIN’s exon-level embeddings enable de novo cell
type annotation by capturing transcriptomic differences often missed at
the gene level. To systematically assess this, we compared gene-,
isoform-, and exon-level expression across annotated cell types in
three datasets. For each dataset, one well-established marker gene per
cell type was selected^[118]30,[119]44–[120]46, and UMAP expression
patterns were visualized for their corresponding isoforms
(Supplementary Figs. [121]S5–[122]S7). While isoform expression
generally resembled gene-level patterns, several isoforms revealed
finer subcluster structures. For example, in the 10X colon dataset
(Supplementary Fig. [123]S6a), among five isoforms of the enterocyte
marker SLC26A3^[124]47, ENST00000453332 exhibited strong, localized
expression, distinguishing subpopulations within enterocytes. Building
on these observations, we emphasized exon-level features underlying
DOLPHIN’s embeddings (Supplementary Figs. [125]S8–[126]S10). Exon-level
expression further refined cell type-specific patterns beyond both
gene- and isoform-level analyses. In the PBMC dataset, while CUX1 gene
and isoform expressions broadly marked monocytes (Supplementary
Fig. [127]S11), specific exons (e.g., exons 19 and 20) localized to the
CD16 monocyte subcluster^[128]48 (Supplementary Fig. [129]S8b). These
results demonstrate that DOLPHIN’s exon-level embeddings facilitate
precise de novo annotation of cell types and subtypes, capturing
biologically meaningful heterogeneity overlooked by conventional
approaches.
To explore the broader applicability of DOLPHIN for cell representation
learning beyond short-read scRNA-seq data, we further applied it to
single-cell long-read RNA-seq datasets^[130]49. In this analysis, we
generated isoform-level counts and subsequently analyzed them with
SCANPY and scVI to establish isoform-based baselines. In parallel,
DOLPHIN was applied directly to exon-informed features to learn cell
embeddings. As shown in Supplementary Fig. [131]S12, DOLPHIN
consistently outperformed isoform-based approaches, achieving ARI
improvements of 0.27 over SCANPY (P = 4.04 × 10^−18) and 0.31 over scVI
(P = 4.17 × 10^−7). These results demonstrate that DOLPHIN can deliver
enhanced clustering resolution even when applied to long-read datasets.
DOLPHIN outperforms conventional gene count tables in detecting
cancer-related marker genes
The DOLPHIN framework leverages exon-level quantification in scRNA-seq
to capture finer-grained transcriptomic details that conventional
gene-level count methods often overlook. This approach enhances cell
clustering accuracy and enables more insightful downstream analyses. We
applied DOLPHIN to identify exon-level differentially expressed genes
(EDEGs) in a pancreatic ductal adenocarcinoma (PDAC) dataset generated
using the 10X Genomics Chromium Single Cell 3′ v2 chemistry^[132]31 and
compared these findings to those obtained with conventional gene count
tables, where differential genes are identified as differentially
expressed genes (DEGs). Our analysis reveals significant improvements
in sensitivity and biological relevance with DOLPHIN.
Using a 10X PDAC dataset with cells from cancer and control
conditions^[133]31, we first leveraged the latent cell embeddings from
DOLPHIN, which integrate exon-level quantification and junction reads,
for cell clustering. As shown in Fig. [134]3a, the clustering results
closely aligned with cell-type annotations from the original study,
reflecting DOLPHIN’s ability to capture distinct cellular identities.
Focusing on cells within Leiden cluster 2, we performed differential
gene expression analysis between cancer and control groups. For
comparability, we applied the same cluster selection to the
conventional gene count table approach to identify DEGs, ensuring that
observed differences could be attributed to the method rather than
clustering inconsistencies.
Fig. 3. DOLPHIN identifies exon-level differential genes undetectable by
gene-level analysis.
[135]Fig. 3
[136]Open in a new tab
a Clustering of the PDAC dataset using DOLPHIN, with clusters labeled
by subject condition, Leiden clusters, and cell type. Leiden cluster 2,
highlighted, is used as an example for subsequent analyses comparing
cancer and control groups. b Enrichment analysis reveals that
exon-level differentially expressed genes (EDEGs) identified by DOLPHIN
are significantly enriched in pancreatic cancer-related terms with
lower adjusted P-values compared to differentially expressed genes
(DEGs) identified by conventional gene count-based methods. This
indicates deeper biological insights. Term marked as “n.s.” indicate no
significant enrichment. The P values comparing DOLPHIN and conventional
methods were calculated using a one-sided Wilcoxon test. c A Venn
diagram shows that DOLPHIN identifies significantly more EDEGs than
DEGs detected by conventional gene-level methods, highlighting its
enhanced sensitivity in detecting biologically meaningful changes. d
Heatmap of differentially expressed exons uniquely identified by
DOLPHIN across cancer and control groups, alongside corresponding gene
expression levels. The heatmaps illustrate that DOLPHIN captures subtle
transcriptomic changes that remain undetectable at the gene level.
P-values for cancer versus control comparisons were calculated using a
two-sided Wilcoxon test. e Enrichment analysis of the 896 DOLPHIN-only
EDEGs shows significant associations with pancreatic cancer-related
terms. In contrast, 483 DEGs identified by conventional gene count-only
methods, but not at the exon level, showed no significant enrichment in
these terms. Adjusted P values for each enrichment term were calculated
using one-sided hypergeometric tests, followed by multiple testing
correction using the Benjamini–Hochberg method. f Volcano plot
highlighting pancreatic cancer-related EDEGs identified by DOLPHIN,
specifically from the disease term highlighted in part e. These EDEGs
are not detected as DEGs by conventional gene count methods,
demonstrating DOLPHIN's ability to uncover biologically important
exon-level differential genes missed by traditional approaches.
Non-significant differences are shaded in gray. P values were derived
using MAST, which fits a hurdle model accounting for both detection
rate and expression level, and were adjusted for multiple testing using
the Benjamini–Hochberg method. See the “Methods” section for details.
Source data are provided as a [137]Source Data file.
In Fig. [138]3b, we present the results of disease and pathway
enrichment analysis^[139]50 on EDEGs identified by DOLPHIN compared to
DEGs identified using the gene count table. Here, pancreatic
cancer-related terms show strong enrichment and lower adjusted P values
when using EDEGs detected by DOLPHIN, underscoring the method’s
sensitivity to relevant pathways and diseases; terms labeled “n.s.”
(not significant) in the DEG analysis highlight the limited detection
capacity of the conventional approach^[140]51,[141]52.
A Venn diagram in Fig. [142]3c illustrates the overlap between EDEGs
identified by DOLPHIN and DEGs detected using the conventional gene
count table, revealing 896 unique EDEGs exclusively identified by
DOLPHIN. These EDEGs correspond to genes that exhibit significant
exon-level differential expression, which remain undetected when
analyzed solely at the gene level using conventional methods. This
highlights DOLPHIN’s enhanced sensitivity in capturing subtle,
exon-specific variations that are otherwise masked in gene-level
analyses. To further explore the biological significance of these
uniquely identified EDEGs, we specifically examined the exons that
contributed to their detection. From the 896 EDEGs, we selected exons
that displayed differential expression, while their corresponding genes
showed no significant differential expression at the gene level. The
heatmap in Fig. [143]3d visualizes this subset, demonstrating that
these exons exhibit robust differential expression when analyzed with
DOLPHIN, yet are overlooked by the conventional gene count table
approach. This underscores DOLPHIN’s ability to uncover exon-level
regulatory changes that are critical but often missed by traditional
gene-centric analyses.
Further exploration of EDEGs unique to DOLPHIN is shown in
Fig. [144]3e, where disease and pathway enrichment analysis reveals
significant enrichment of pancreatic cancer-related terms. To
illustrate the specific gene-level differences, a volcano plot in
Fig. [145]3f shows log2 fold changes and adjusted P values for key
PDAC-associated genes identified as EDEGs by DOLPHIN but missed as DEGs
by the gene count table. The selection of these genes was guided by the
top highlighted pancreatic cancer term in Fig. [146]3e. Several of
these genes have well-established roles in PDAC progression and therapy
response, including SMAD4, a canonical tumor suppressor gene frequently
mutated or lost in PDAC and associated with poor prognosis and
treatment resistance^[147]53–[148]55; ERCC1, a marker implicated in
chemotherapy response and DNA repair deficiency in
PDAC^[149]56,[150]57; TGFBR2, a key component of TGF-beta signaling,
which plays a dual role in tumor suppression and progression in
pancreatic cancer^[151]58,[152]59; and ATM, a DNA damage response
kinase frequently mutated in PDAC, where its loss impairs double-strand
break repair and confers increased sensitivity to DNA-damaging agents
and PARP inhibitors^[153]60,[154]61. The identification of these genes
through exon- and junction-level resolution suggests that DOLPHIN can
recover biologically and clinically meaningful signals that remain
undetected by conventional pipelines, with potential implications for
both diagnostic biomarker discovery and therapeutic targeting. The
distribution of these genes underscores DOLPHIN’s enhanced sensitivity,
with many exhibiting exon-level differential expression that does not
translate to gene-level differences, making them undetectable by
conventional methods.
To assess the clinical relevance of the 896 DOLPHIN-unique EDEGs
identified in this PDAC dataset, we conducted a Kaplan-Meier survival
analysis using real patient survival data from The Cancer Genome Atlas
(TCGA) PDAC cohort^[155]62, stratifying patients based on the
expression of DOLPHIN-unique EDEGs. Given that pseudo-bulk expression
profiles derived from single-cell data may introduce biases into
downstream analyses, particularly due to dropout events and limited
coverage of lowly expressed genes^[156]63, we instead validated the
clinical relevance of our findings using matched bulk RNA-seq data to
ensure more reliable interpretation of survival associations. This
strategy moves beyond pseudo-bulk approximations and leverages
orthogonal, external bulk datasets to provide a more robust assessment
of the prognostic value of the identified genes. As shown in
Supplementary Fig. [157]S13a, we stratified patients into high-risk and
low-risk groups based on the expression of the top 100 and all 896
EDEGs, where the genes were ranked by increasing adjusted P from our
DOLPHIN-based differential analysis. Across all subsets, the separation
between risk groups was statistically significant, with the strongest
prognostic signal observed when using the full set of 896 EDEGs
(P = 2.22 × 10^−39, log-rank-sum test^[158]64). To characterize how the
prognostic signal accumulates with increasing numbers of EDEGs, we
plotted the association P values across ranked gene sets (Supplementary
Fig. [159]S13b). The resulting curve demonstrates a consistent and
monotonic strengthening of survival association as more top-ranked
EDEGs are included. These analyses collectively demonstrate that the
EDEGs uniquely identified by DOLPHIN not only capture biologically
relevant information missed by gene-level approaches but also exhibit
strong clinical relevance when validated against independent datasets.
Additionally, we conducted a similar analysis using the junction count
table to identify junction-level differentially expressed genes
(JDEGs), as shown in Supplementary Fig. [160]S14b–d. This analysis
further reinforces DOLPHIN’s capability in capturing transcriptomic
variations beyond gene-level limitations, particularly in exon and
junction reads usage.
In addition to the results observed in Cluster 2, which contains a
balanced number of cells between disease and control groups, we further
examined other disease-relevant clusters to assess the robustness and
generalizability of DOLPHIN under realistic group size imbalances.
Given the biological relevance of ductal cells to PDAC, which
originates from the epithelial lining of the pancreatic ducts, we
additionally included Ductal Type 1 and Type 2 cell clusters in the
EDEG and DEG comparison. Unlike Cluster 2, the ductal clusters exhibit
pronounced imbalance in group sizes, reflecting a common feature of
real-world single-cell datasets where cell-type abundance may vary
across conditions. Specifically, this ductal cluster contains 1067
cells, including 891 from cancer samples and 176 from healthy controls,
providing a challenging and biologically meaningful setting to evaluate
the robustness of differential analysis. Although downsampling has been
proposed as a strategy to address group imbalance^[161]65, we did not
employ it in this study, as doing so would further reduce the already
limited number of cells in biologically relevant populations and
diminish statistical power. Results are shown in Supplementary
Fig. [162]S15. DOLPHIN identified 445 more significant genes than the
conventional gene count-based method, as shown in Supplementary
Fig. [163]S15b. Enrichment analysis Supplementary Fig. [164]S15c
further demonstrates the biological relevance of these additional
genes: the EDEGs identified by DOLPHIN yielded stronger enrichment for
pancreatic-related terms compared to DEGs. Notably, the 1491 EDEGs
uniquely identified by DOLPHIN were significantly enriched in the
pancreatic cancer-related term, whereas the 1046 DEGs identified only
by gene count-based analysis did not yield any enrichment for such term
(Supplementary Fig. [165]S15d). These results highlight the added
biological signal gained through exon-level analysis. We also analyzed
JDEGs based on DOLPHIN’s junction reads. As shown in Supplementary
Fig. [166]S15f, the 2867 JDEGs were significantly enriched for
pancreatic disease-related terms. Furthermore, even when considering
only the 1583 JDEGs that did not overlap with DEGs, enrichment analysis
still revealed pancreatic cancer-related terms Supplementary
Fig. [167]S15g. These findings emphasize the additional biological
resolution provided by junction-level modeling and demonstrate that
DOLPHIN’s splicing-aware framework captures disease-relevant signals
that are often missed by conventional gene expression analyses.
DOLPHIN effectively detects alternative splicing events through junction read
aggregation
DOLPHIN integrates exon reads and junction reads to aggregate cells
based on exon-junction read patterns, making it well-suited for AS
analysis at the single-cell level. To evaluate its performance, we
selected Outrigger as a baseline, as it is one of the most widely used
tools for AS event detection in transcriptomics^[168]65–[169]67. This
comparison underscores the advantages of DOLPHIN’s junction-read-aware
aggregation, a key feature that enhances sensitivity and accuracy in
detecting AS events at the single-cell level. Notably, DOLPHIN’s
aggregation approach can be adapted to work with other AS tools (see
benchmarking sections), showcasing its versatility.
In the full-length PBMC dataset, DOLPHIN shows marked improvements over
Outrigger in detecting AS events. The top panel of Fig. [170]4a
illustrates the number of Exon Skipping (ES) and Mutually Exclusive
Exon (MXE) events detected per cell using Outrigger with single-cell
input versus aggregated cell input generated by DOLPHIN. In this
context, “single-cell input” refers to the original, unaggregated
scRNA-seq reads, which were supplied directly to Outrigger without any
aggregation. This configuration reflects the baseline setting used to
evaluate the impact of DOLPHIN’s read aggregation strategy. The results
demonstrate a substantial increase in the number of detected events
using DOLPHIN, with the median count for ES rising from 183 to 1215,
and for MXE increasing from 4 to 22, indicating a marked enhancement in
sensitivity. We next assessed whether DOLPHIN effectively enhances
single-cell splicing detection by examining AS events jointly detected
by both approaches (Fig. [171]4a). While Fig. [172]4a summarizes the
total number of events per cell, it does not capture how consistently
each shared event is detected across cells by the two methods. To
address this, we analyzed the cell-level detection patterns of
overlapping AS events (Supplementary Fig. [173]S16). On one hand,
DOLPHIN robustly preserves the detection of AS events originally
identified by the single-cell input. In Supplementary
Fig. [174]S16a, we present paired heatmaps for the full-length PBMC
dataset, showing the detection patterns for each event across cells. We
found that 97.8% of AS events detected by the single-cell input were
also detected by DOLPHIN, demonstrating strong consistency. On the
other hand, DOLPHIN identifies substantially more AS events beyond
those captured by the single-cell input. In Supplementary Fig.
[175]S16b, we quantify this relationship by plotting the distribution
of Pearson correlation coefficients between the detection patterns of
the two methods for each AS event. Across cells, DOLPHIN detected ~4.8
times more events than the single-cell method alone. Together, these
results demonstrate that DOLPHIN not only preserves the fidelity of
single-cell AS detection but also enhances sensitivity by recovering a
more complete landscape of splicing events across cells.
Fig. 4. DOLPHIN enhances alternative splicing detection and analysis.
[176]Fig. 4
[177]Open in a new tab
a–c Detection of alternative splicing (AS) events across three
datasets: Top: full-length PBMC, Middle: 10X colon, and Bottom: 10X
rectum. a DOLPHIN identifies significantly more AS events, including
exon skipping (ES) and mutually exclusive exons (MXE), compared to the
baseline Outrigger tool, demonstrating superior sensitivity in
detecting splicing variations. b Scatter plots of Percent Spliced-In
(PSI) values show that DOLPHIN achieves higher correlation with
pseudo-bulk data (used as a proxy ground truth), indicating more
accurate AS quantification than conventional approaches. c UMAP plots
based on PSI values reveal that DOLPHIN captures distinct
cell-type-specific splicing patterns with greater clarity and
biological relevance, improving resolution of splicing events missed by
baseline methods. d Sashimi plots for the AS event HsaEX0051104 in the
full-length PBMC dataset show stronger junction read signals after
DOLPHIN aggregation, enabling detection of splicing events overlooked
by conventional methods. e Similarly, for the AS event HsaEX0013878 in
the 10X colon dataset, DOLPHIN enhances junction read signals,
uncovering AS events missed by the baseline approaches. P values from
one-sided Student’s t-tests: *P < 0.05, **P < 0.01, ***P < 0.001,
****P < 0.0001. Exact P values are provided in the source data. Source
data are provided as a [178]Source Data file.
To further demonstrate DOLPHIN’s capability, we compared PSI values
between pseudo-bulk and single-cell samples (top panel of
Fig. [179]4b), using pseudo-bulk PSI values as a proxy ground truth, a
strategy commonly employed for AS validation^[180]65,[181]67,[182]68.
Each point represents the PSI value for a specific AS event, with a
higher density of points along the diagonal in DOLPHIN indicating
stronger concordance with pseudo-bulk data. The Pearson correlation
increases by 0.06 (P = 6.37 × 10^−242), indicating that the additional
AS events detected by DOLPHIN exhibit comparable, if not stronger,
correlation with pseudo-bulk results. This improvement reflects
DOLPHIN’s enhanced detection capabilities and greater precision in
capturing splicing patterns. In the scatter plot, we observed a higher
density of AS events along the diagonal, reflecting a broader
improvement across the entire PSI spectrum. AS in most cell populations
predominantly yields near-complete exon inclusion or exclusion, with
intermediate splicing states being relatively rare and technically
challenging to detect^[183]22,[184]65. Building on this observation, we
analyzed the distribution of detected AS events across different PSI
ranges and assessed the corresponding junction read support to
characterize DOLPHIN’s aggregation-enhanced detectability. As shown in
Supplementary Fig. [185]S17a (upper panel), DOLPHIN-enhanced input
increased the total number of detected exon-skipping events across all
three PSI categories (PSI = 0, 0 < PSI < 1, and PSI = 1) in the
full-length PBMC dataset. The most pronounced gain was observed for
PSI = 1, with 431,406 additional events detected, although noticeable
improvements were also seen in the other PSI ranges. We further
examined the junction read support across the full PSI spectrum
(Supplementary Fig. [186]S17b, upper panel). In the single-cell input,
events with intermediate PSI values (e.g., between 0.4 and 0.6)
exhibited substantially lower read counts, with a mean of 66 reads.
After DOLPHIN enhancement, the mean read count increased to 168, a
statistically significant difference (one-sided Mann–Whitney U test,
P < 10^−300). These results demonstrate that DOLPHIN improves the
detection of AS events across the PSI spectrum, including low-coverage
events with intermediate splicing levels.
To evaluate whether the PSI values reflected biologically meaningful
splicing regulation, we assessed their ability to capture
cell-type-specific splicing patterns. Specifically, we used PSI values
as input features for cell representation and clustering analyses. The
UMAP plots in the top panel of Fig. [187]4c show that DOLPHIN-inferred
PSIs yield sharper boundaries between cell types compared to
single-cell PSI values alone. This improvement is quantitatively
supported by a 0.38 increase in ARI (P = 2.70 × 10^−121). These results
indicate that DOLPHIN more effectively captures splicing signals that
distinguish cell types, suggesting higher biological relevance and
improved splicing quantification accuracy.
Beyond full-length single-cell data, we extended our evaluation to the
common tag-based 10X Genomics scRNA-seq data from human colon samples
to demonstrate its general applicability, where DOLPHIN showed robust
performance even with limited transcriptome coverage. The middle panel
of Fig. [188]4a shows that the distribution of detected events by
DOLPHIN is shifted towards higher counts compared to single-cell data
alone (without aggregation), with the median number of detected ES
increasing from 58 to 224, and the maximum number of MXE detected per
cell rising from 2 to 8. This underscores DOLPHIN’s sensitivity to data
with partial coverage. The concordance heatmap shown in the middle part
of Supplementary Fig. [189]S16a further illustrates that DOLPHIN
consistently preserves the original single-cell detection signals while
detecting additional AS events across cells. The scatter plot between
pseudo-bulk and single-cell PSI values (middle panel of Fig. [190]4b)
demonstrates an improvement in Pearson correlation by 0.02
(P = 1.19 × 10^−208) with DOLPHIN, further validating its accuracy. In
addition to the correlation improvement, we observed a higher density
of AS events along the diagonal in the scatter plot, reflecting
DOLPHIN’s broader enhancement across the entire PSI spectrum.
Specifically, in the lower panel of Supplementary Fig. [191]S17b, AS
events with PSI = 1 showed the greatest increase, with an additional
480,987 events detected compared to the original single-cell input. The
mean junction read count supporting AS events with intermediate PSI
values (i.e., between 0.4 and 0.6) increased from 65 to 116
(P = 9.16 × 10^−3). These results confirm that DOLPHIN enhances the
detection of low-coverage AS events with intermediate PSI values even
in the sparse 10X dataset. The UMAP plots (middle panel of
Fig. [192]4c) demonstrate that DOLPHIN achieves clear separation of
specific cell types, such as TA and enterocyte cells, with an increase
in the ARI score by 0.06 (P = 3.70 × 10^−31) compared to single-cell
data, highlighting its broad applicability across various datasets. We
observed similar improvements with the tag-based 10X rectum data (the
bottom panels of Fig. [193]4a–c and Supplementary Fig. [194]S16a, b).
Specifically, the bottom panel of Fig. [195]4a reveals an increase in
the number of detected ES, with median values rising from 62 to 200,
and for MXE events, from 1 to 2. Additionally, the bottom panel of
Fig. [196]4b shows an improved correlation with pseudo-bulk PSI values,
increasing by 0.01 (P = 1.22 × 10^−187). Notably, the bottom panel of
Fig. [197]4c shows that the UMAP plot achieves clearer separation of
Enterocyte cells using DOLPHIN, further validating its robustness.
To illustrate the detailed insights DOLPHIN provides, we present
examples of exon and junction read coverage for specific AS events.
Fig. [198]4d showcases the full-length PBMC splicing event HsaEX0051104
in the naïve T cell sample “SRR18385965,” comparing single-cell data
with DOLPHIN-aggregated data. HsaEX0051104, an exon-skipping event in
the PTPRC gene that generates the CD45RA isoform, critical for T cell
function^[199]69,[200]70. HsaEX0051104 encompasses three exons (exon 4,
exon 5, and exon 6), with junction read counts of 13 between exons 4
and 5, 31 between exons 5 and 6, and 16 between exons 4 and 6. However,
in single-cell data, this splicing event is not detectable due to the
absence of junction reads spanning exons 4 and 6, which are critical
for validating the exon-skipping event. We applied an in silico
pseudo-bulk validation strategy using CD4 T cells to independently
confirm the biological existence of the AS event identified by DOLPHIN.
Specifically, we generated 20 pseudo-bulk BAM files by randomly
sampling 80% of CD4 T cells per replicate, simulating replicate-level
coverage. VALERIE^[201]71 was then applied to profile AS events based
on junction read and coverage signals across these samples. As shown in
Supplementary Fig. [202]S18a, VALERIE consistently detected the same
exon-skipping event in PTPRC (HsaEX0051104) identified by DOLPHIN, with
stable PSI distributions across replicates. We further confirmed this
event by applying VALERIE to DOLPHIN’s single-cell BAM files for CD4 T
cells (Supplementary Fig. [203]S18b), providing orthogonal evidence of
its reproducibility and biological relevance. To further support this
AS event, we visualized the pseudo-bulk read coverage using
ggsashimi^[204]72. As shown in Supplementary Fig. [205]S18c, the
sashimi plot based on full-length PBMC pseudo-bulk alignments clearly
demonstrates the exon-skipping pattern corresponding to HsaEX0051104.
In addition, DOLPHIN uncovers another splicing event (HsaEX0051102)
involving exons 1, 3, and 4. This event is supported by 25 junction
reads between exons 1 and 3, 11 reads between exons 3 and 4, and 22
reads connecting exons 1 and 4. Conversely, in this specific single
cell, this event is not detected due to the lack of junction reads
bridging exons 1 and 4, which are crucial for identifying this splicing
pattern. In Fig. [206]4e, we investigated the splicing event
HsaEX0013878 within the CD47 gene in progenitor cell
“AAGCCGCCACTACAGT-1” from the 10X colon dataset. CD47 has been
implicated in colorectal cancer progression^[207]73,[208]74. This event
involves exons 1, 2, and 3, with 52 junction reads supporting the
connection between exons 1 and 2, 30 reads between exons 2 and 3, and
70 reads between exons 1 and 3. However, this specific cell lacks the
crucial junction reads linking exons 1 and 3, thereby precluding the
detection of this event in this cell. The presence of this splicing
event was further supported by pseudo-bulk alignments of progenitor
cells (Supplementary Fig. [209]S18d). These examples underscore
DOLPHIN’s capacity to uncover complex AS patterns and demonstrate its
effectiveness in enhancing single-cell AS analyses through
junction-read-informed cell aggregation, revealing biologically
significant insights otherwise missed by standard methods.
DOLPHIN reveals biologically relevant alternative splicing events unique to
specific cell types
We assessed DOLPHIN’s capability to detect cell-type-specific AS events
by calculating PSI values for each event per cell type, enabling
differential AS analysis. Genes associated with significantly
differentially spliced events were identified as differentially spliced
genes. Fig. [210]5a, b and Supplementary Fig. [211]S19 highlight the
biological relevance of these cell-type-specific events identified by
DOLPHIN, underscoring its ability to detect distinct splicing patterns
not captured by the raw single-cell data without DOLPHIN aggregation
enhancement. Specifically, Fig. [212]5a displays dot plots of the top
differentially spliced events across cell types in the full-length PBMC
and tag-based 10X colon datasets, respectively. The labels in the plot
correspond to differentially spliced genes and event identifiers, which
provide detailed information for each splicing event provided in
Supplementary Table [213]S1 and Supplementary Table [214]S2. In
contrast, Supplementary Fig. [215]S19 and Supplementary Table [216]S3
display the top differentially spliced events identified using raw
single-cell data without aggregation. Without DOLPHIN’s aggregation,
the analysis based on raw data alone fails to capture the distinct
splicing patterns, as evidenced by the reduced separation of PSI values
across cell types. Dot colors in the plots represent the average PSI
values of an event for cells from each specific cell type, further
highlighting DOLPHIN’s capability to detect differential splicing
events that were previously missed. For example, in the full-length
PBMC dataset, unique splicing events specific to B cells were
challenging to distinguish from dendritic cells (DCs) and Other cells
using the raw single-cell data, but are now clearly identifiable after
DOLPHIN aggregation. Similarly, in the 10X colon dataset, top
differentially spliced events appear more prominently in paneth-like
cells compared to the single-cell method, illustrating DOLPHIN’s
enhanced sensitivity to cell-type-specific splicing.
Fig. 5. DOLPHIN captures biologically relevant alternative splicing events
that signify cell-type-specific differences.
[217]Fig. 5
[218]Open in a new tab
a Dot plots showing the PSI values of the top differentially spliced
events identified by DOLPHIN. b GO biological process (GOBP) enrichment
analysis of biologically significant differentially spliced genes
identified by DOLPHIN, with alternative splicing-related terms
highlighted in red. Adjusted P-values for each enrichment term were
calculated using one-sided hypergeometric tests, followed by multiple
testing correction using the Benjamini–Hochberg method. c Schematic
illustration explaining PSI distribution splicing modality
categorization. d PSI distribution for a single alternative splicing
event, categorized by splicing modality across cell types,
demonstrating that DOLPHIN provides clearer distinctions of splicing
differences that align with cell type identities. e Splicing modality
composition across single cells shows that DOLPHIN captures more
distinct and biologically relevant splicing patterns by reducing the
proportion of multimodal (null) categories, which represent PSI
distributions without clear features. This demonstrates that DOLPHIN
reduces ambiguity in alternative splicing event detection, enabling
more precise analysis. f UMAP plots of cell clusters using PSI modality
one-hot encoding demonstrate that the PSI splicing modalities
identified by DOLPHIN retain strong cell-type-specific signals. DOLPHIN
enhances the resolution of these cell-type-specific splicing patterns,
providing clearer separation and biologically meaningful clustering
compared to single-cell data alone. These biologically relevant
alternative splicing events can contribute to more accurate cell type
classification and offer insights into cellular diversity and potential
disease mechanisms. P values from one-sided Student’s t-tests:
*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; n.s. not
significant. Exact P values are provided in the source data. Source
data are provided as a [219]Source Data file.
Supplementary Fig. [220]S20a highlights the top differentially spliced
genes that could not be identified using traditional gene count-based
differential expression methods, alongside their expression values in
the PBMC and colon datasets. Unlike conventional approaches that
primarily focus on gene expression differences, DOLPHIN leverages
PSI-based differences to uncover differentially spliced genes that
remain undetectable with single-cell gene count data alone. This
capability is particularly evident in the 10X colon dataset, where
unique splicing patterns are revealed across cell types, even in the
absence of significant gene expression changes, underscoring DOLPHIN’s
distinct advantage in detecting splicing-driven heterogeneity. To
confirm the biological significance of these findings, we performed
gene ontology biological process (GOBP) enrichment analysis using
differentially spliced genes. In the upper panel of Fig. [221]5b, GOBP
terms enriched in B cells from the PBMC dataset include B cell
activation and B cell receptor signaling, reinforcing the biological
relevance of these identified splicing events^[222]75. Additionally,
GOBP terms associated with AS confirm DOLPHIN’s accuracy in detecting
spliced genes involved in splicing regulation. In the 10X colon
dataset, GOBP enrichment analysis (lower panel of Fig. [223]5b)
revealed terms critical to enterocyte function, such as metabolic
processes, aerobic respiration, and mitochondrial electron
transport—biological processes that are essential for maintaining gut
health^[224]76,[225]77. The identification of AS-related GOBP terms
reflects the adaptive role of enterocytes in modulating gene expression
in response to environmental and cellular stressors^[226]78,[227]79.
GOBP enrichment analysis for other cell types is presented in
Supplementary Fig. [228]S21, further underscoring the functional
relevance of splicing events detected by DOLPHIN. Supplementary
Fig. [229]S20b illustrates the distinction between differentially
spliced genes identified by DOLPHIN and DEGs detected using
conventional gene count methods. Supplementary Fig. [230]S20c presents
the GOBP enrichment analysis for differentially spliced genes uniquely
detected by DOLPHIN, after excluding those already identified as DEGs
by conventional gene count-based approaches. The GOBP enrichment
analysis of these remaining genes reveals critical biological processes
encoded within PSI values that cannot be detected using gene count data
alone, highlighting DOLPHIN’s unique ability to uncover
splicing-specific regulatory mechanisms. To provide a more granular
view of splicing distributions, we applied the Anchor tool from
Expedition^[231]22, categorizing PSI distributions into five splicing
modalities: excluded, bimodal, included, middle, and multimodal (null)
(Fig. [232]5c). This categorization reveals variations in PSI
distributions across cell types, facilitating detection of cell
type-specific splicing patterns. In scRNA-seq data, splicing events
often exhibit varying degrees of PSI consistency within the same cell
type^[233]22,[234]65. Some events show concentrated PSI distributions
corresponding to clear splicing modes, such as inclusion or exclusion,
whereas others display dispersed or heterogeneous PSI patterns,
classified as multimodal or null modalities. Multimodal splicing
patterns can arise from genuine biological heterogeneity, including the
co-expression of multiple isoforms and dynamic splicing regulation
across cell types^[235]22,[236]80. However, in sparse single-cell
datasets, multimodal and null modalities can also result from technical
factors such as incomplete read coverage, dropout, and measurement
noise, making the interpretation of such events more challenging. Null
modalities, in particular, indicate splicing signals lacking sufficient
consistency across cells, thereby complicating the identification of
robust, biologically meaningful splicing patterns. DOLPHIN improves
signal clarity by enhancing read coverage and exon-level resolution,
which increases the proportion of splicing events that can be
classified into more interpretable modalities.
In the upper panel of Fig. [237]5d, we examine the splicing event
HsaEX0051104 in the PBMC dataset, comparing PSI distributions from
single-cell data with DOLPHIN results. DOLPHIN identifies four distinct
splicing modes across eight cell types, whereas single-cell data alone
captures only three modes. Notably, DOLPHIN enhances the detection of
splicing variations in CD8 T cells, shifting the distribution from a
null mode to a middle mode, thereby providing a clearer and more
accurate representation of these events. We performed in silico
validation of this splicing event using a bootstrapped pseudo-bulk
strategy. Specifically, we randomly sampled 80% of CD8 T cells multiple
times to construct pseudo-bulk profiles and applied VALERIE to
visualize splicing signals at the event locus. As shown in
Supplementary Fig. [238]S22a, the consistent detection of junction
usage and read coverage patterns across replicates confirms the
presence of this ES event. In addition, we assigned splicing modality
based on the PSI values derived from these pseudo-bulk replicates. The
resulting modality, shown in Supplementary Fig. [239]S22b, consistently
falls within the middle modality, validating the splicing distribution
identified by DOLPHIN in CD8 T cells.
In the lower panel of Fig. [240]5d, the 10X colon dataset displays the
PSI distribution for the splicing event HsaEX0013878 within the CD47
gene across different cell types. The CD47 gene, known for its
involvement in tumor progression and immune evasion, is typically
upregulated in colorectal cancer tissues^[241]81. Due to its relevance
in colorectal cancer, it is anticipated that multiple transcripts of
CD47 would be detected in the colon dataset. Of the six annotated
transcripts, three contain the splicing event HsaEX0013878: transcripts
ENST00000398258 and ENST00000361309 include all three exons, while
ENST00000517766 exhibits ES. To better contextualize the PSI
distributions of this event, we incorporated pseudo-bulk transcript
quantification using kallisto^[242]82 to estimate the exon inclusion
probability across cell types for exon chr3:108049619-108049651
(HsaEX0013878). These estimates, shown as red dashed lines in the
Supplementary Fig. [243]S23a, serve as in silico reference values for
comparing single-cell and DOLPHIN-aggregated results. Notably, we
applied bootstrapping to quantify the absolute differences between
method-specific median PSI values and pseudo-bulk estimates across cell
types shown in Supplementary Fig. [244]S23b. The DOLPHIN-aggregated PSI
values showed smaller deviations from the pseudo-bulk references