Single-cell RNA sequencing (scRNA-seq) enables comprehensive investigation of cellular heterogeneity at the transcriptomic level. Nevertheless, the high dimensionality, sparsity, and technical noise inherent to scRNA-seq data pose significant challenges for precise cell type identification. Traditional clustering methods struggle to capture the nonlinear structures of such data, while existing deep learning approaches commonly suffer from inadequate disentanglement of semantic features from technical noise. To mitigate these limitations, we present scDiffAE, a new clustering approach built upon a diffusion autoencoder. It leverages a semantic encoder to extract low-dimensional semantic embeddings, and incorporates a conditional diffusion model that conditions on both semantic and random encoding features to progressively reconstruct the original data, effectively decoupling semantic features from technical noise. To further promote disentanglement and preserve semantic integrity, an auxiliary decoder is introduced to directly reconstruct the input from the semantic embeddings. Evaluations on 11 real-world scRNAseq datasets demonstrate that scDiffAE consistently surpasses seven leading methods across multiple metrics, including ARI, AMI, and NMI. Further differential gene expression analysis validates the reliability of the clustering results, providing a solid foundation for downstream research.

Medical image segmentation tasks commonly suf-fer from high annotation costs and limited training samples. To address this issue, we propose a diffusion autoencoder-based data augmentation (DADA) method to simultaneously generate se-mantically consistent image-mask pairs. The proposed approach extracts semantic features from the original image as conditional input to guide the diffusion model in generating structurally aligned image-mask pairs, effectively modeling the structural relationship between images and masks. We evaluate our method on the DRIVE and GlaS datasets. Experimental results demon-strate that our approach not only outperforms existing methods in terms of image quality across three generation tasks but also significantly improves segmentation performance when applied to eight mainstream medical image segmentation models. These findings highlight the broad potential of the proposed method for small-sample medical image generation and segmentation tasks.

Single-cell multi-modal analysis has made significant progress in recent years. However, the translation and generation of data across modalities remain challenging due to differences in data types, measurement scales, and inherent noise. In this study, we propose a novel method for single-cell cross-modality data generation based on a feature-decoupling conditional diffusion framework. Specifically, the method decouples modality-unique and common features using modality-specific and shared encoders, and then leverages two conditional diffusion models to enable accurate bidirectional translation between modalities without requiring prior cell-type annotations. Experimental results on four real datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in cross-modality translation tasks, achieving higher Pearson correlation coefficients and AUROC scores for RNA and ATAC generation. The proposed framework offers a promising solution for cross-modality analysis in single-cell research.

Single-cell multi-omics technologies enable the exploration of relationships between different omics layers and the cell-type-specific molecular regulatory mechanisms. However, these technologies face challenges such as data acquisition difficulty, high costs, and limited throughput. Existing cross-modal translation methods also face limitations in handling unpaired data, preserving modality heterogeneity, and modeling complex mapping relationships. To address these issues, we propose scOTD-a bidirectional cross-modal translation framework for unpaired single-cell data, based on optimal transport (OT) and conditional diffusion models. This method overcomes the dependency on paired data or prior knowledge by modeling independent latent spaces, thus preserving the unique features of each modality. It further establishes probabilistic coupling mechanisms using OT to implicitly model cross-modal associations and employs dynamic weight injection into a conditional diffusion model for mutual translation across modalities. Experimental results on four scRNA-seq and scATAC-seq datasets show that scOTD outperforms existing state-of-the-art methods across multiple evaluation metrics, providing a robust and efficient solution for single-cell cross-modal translation.

Single-cell multi-omics technologies enable comprehensive molecular profiling, offering insights into cellular heterogeneity and biological mechanisms. However, current cross-modality translation methods struggle with high-dimensional, noisy, and sparse single-cell data. We propose single-cell Diffusion models for Cross-modality Translation (scDCT), a probabilistic framework for bidirectional cross-modality translation in single-cell data, including single-cell RNA sequencing, single-cell assay for transposase-accessible chromatin sequencing, and protein expression. scDCT integrates modality-specific autoencoders with conditional denoising diffusion probabilistic models to map inputs to latent spaces and perform probabilistic translation across modalities. This design captures cell-type heterogeneity, accounts for data sparsity, and models uncertainty during translation. Extensive experiments on eight benchmark datasets demonstrate that scDCT outperforms state-of-the-art methods across paired, unpaired, cross-type, and cross-tissue settings, offering a robust and interpretable solution for single-cell multi-omics integration.

Accurately inferring clonal mutational profiles is essential for understanding intra-tumor heterogeneity and clonal selection during tumor evolution. Single-cell multi-modal genomic data, such as copy numbers and point mutations, can be integrated to deliver multiple views of the clonal mutational patterns. Despite of the fact that integration of single-cell multi-modal data has been extensively explored in existing studies, computational methods specifically developed to integrate copy number and point mutation data of single cells are still highly needed. We introduce a deep joint representation learning framework called scCMP, to accurately identify clonal mutational profiles. scCMP employs hybrid Transformer-CNN architectures and graph convolutional networks to integrate single-cell copy number and point mutation data. By fusing individual and commonality information among the two modalities, it generates meaningful cell embeddings for identifying clonal clusters. We comprehensively evaluate the effectiveness of scCMP on five real single-cell DNA sequencing datasets, and further showcase its good scalability on datasets generated from other omics technologies. The results show scCMP accurately aggregates the cells with similar mutational profiles into a same cluster, and surpasses the state-of-the-art methods, indicating its advantage in integrating single-cell genomic data.

Dropout events and technical noise are pervasive challenges in single-cell RNA sequencing (scRNA-seq) data, often obscuring true gene expression profiles and undermining the reliability of downstream analyses. Existing imputation and denoising methods offer partial relief but frequently struggle with over-smoothing and fail to fully capture the complex heterogeneity of cellular states. To address these limitations, we introduce scSTD, a novel imputation and denoising framework that uniquely combines the Swin Transformer (SwinT) architecture with a latent diffusion model. In scSTD, a deep autoencoder first encodes each cell into a compact latent embedding, which is then modeled via a SwinT-based latent diffusion process designed to learn the rich, multimodal distribution of scRNA-seq data. This integration enables scSTD to accurately recover gene expression profiles while preserving subtle biological variation. By synthesizing realistic latent neighbors for each cell and aggregating their decoded outputs, scSTD achieves high-fidelity imputation and denoising. Comprehensive evaluations on both synthetic and real scRNA-seq datasets demonstrate that scSTD significantly outperforms existing methods in recovering true gene expression profiles and maintaining the topological integrity of cellular landscapes.

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

Single-cell DNA sequencing (scDNA-seq) has been widely used to unmask tumor copy number alterations (CNAs) at single-cell resolution. Despite that arm-level CNAs can be accurately detected from single-cell read counts, it is difficult to precisely identify focal CNAs as the read counts are featured with high dimensionality, high sparsity and low signal-to-noise ratio. This gives rise to a desperate demand for reconstructing high-quality scDNA-seq data. We develop a new method called scTCA for imputation and denoising of single-cell read counts, thus aiding in downstream analysis of both arm-level and focal CNAs. scTCA employs hybrid Transformer-CNN architectures to identify local and non-local correlations between genes for precise recovery of the read counts. Unlike conventional Transformers, the Transformer block in scTCA is a two-stage attention module containing a stepwise self-attention layer and a window Transformer, and can efficiently deal with the high-dimensional read counts data. We showcase the superior performance of scTCA through comparison with the state-of-the-arts on both synthetic and real datasets. The results indicate it is highly effective in imputation and denoising of scDNA-seq data.

Intra-tumor heterogeneity (ITH) is one of the major confounding factors that result in cancer relapse, and deciphering ITH is essential for personalized therapy. Single-cell DNA sequencing (scDNA-seq) now enables profiling of single-cell copy number alterations (CNAs) and thus aids in high-resolution inference of ITH. Here, we introduce an integrated framework called rcCAE to accurately infer cell subpopulations and single-cell CNAs from scDNA-seq data. A convolutional autoencoder (CAE) is employed in rcCAE to learn latent representation of the cells as well as distill copy number information from noisy read counts data. This unsupervised representation learning via the CAE model makes it convenient to accurately cluster cells over the low-dimensional latent space, and detect single-cell CNAs from enhanced read counts data. Extensive performance evaluations on simulated datasets show that rcCAE outperforms the existing CNA calling methods, and is highly effective in inferring clonal architecture. Furthermore, evaluations of rcCAE on two real datasets demonstrate that it is able to provide a more refined clonal structure, of which some details are lost in clonal inference based on integer copy numbers.

MOTIVATION: Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. RESULTS: We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. AVAILABILITY AND IMPLEMENTATION: bmVAE is freely available at https://github.com/zhyu-lab/bmvae. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clustering tumor single-cell mutation data has formed an important paradigm for deciphering tumor subclones and evolutionary history. This type of data may often be heavily complicated by incompleteness, false positives and false negatives errors. Despite to the fact that several computational methods have been developed for clustering binary mutation data, their applications still suffer from degraded accuracy on large datasets or datasets with high sparsity. Therefore, more effective methods are sorely required. Here, we propose a novel method called CBM for reliably Clustering Binary Mutation data. CBM formulates the binary mutation data under a probabilistic framework through parameterizing false positive errors, false negative errors, presence probability distribution of subclones and their binary mutation profiles. To cope with the difficulty of optimizing discrete parameters, Gibbs sampling for mixtures is employed to iteratively sample cell-to-cluster assignments and cluster centers from the posterior. Extensive evaluations on simulated and real datasets demonstrate CBM outperforms the state-of-the-art tools in different performance metrics such as ARI for clustering and accuracy for genotyping. CBM can be integrated into the pipeline of reconstructing tumor evolutionary tree, and detecting subclones using CBM can be employed as a pre-text task of tumor subclonal tree inference, which will significantly improve computational efficiency of phylogenetic analysis especially on large datasets. CBM software is freely available at https://github.com/zhyu-lab/cbm.

SUMMARY: Single-cell DNA sequencing (scDNA-seq) now enables high-resolution profiles of intra-tumor heterogeneity. Existing methods for phylogenetic inference from scDNA-seq data perform acceptably well on small datasets but suffer from low computational efficiency and/or degraded accuracy on large datasets. Motivated by the fact that mutations sharing common states over single cells can be grouped together, we introduce a new software called AMC (accurate mutation clustering) to accurately cluster mutations, thus improve the efficiency of phylogenetic inference. AMC first employs principal component analysis followed by K-means clustering to find mutation clusters, then infers the maximum likelihood estimates of the genotypes of each cluster. The inferred genotypes can subsequently be used to reconstruct the phylogenetic tree with high efficiency. Comprehensive evaluations on various simulated datasets demonstrate AMC is particularly useful to efficiently reason the mutation clusters on large scDNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: AMC is freely available at https://github.com/qasimyu/amc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Next-generation sequencing (NGS) promises highresolution landscapes of cancer genomes, especially the genomewide copy number alterations (CNAs). Detecting CNAs from tumor NGS data often encounters several critical issues that heavily affect the accuracy of the results. For instance, the over-dispersed distribution of read depth signals gives rise to a challenge of designing appropriate statistical model to effectively explain the data; aneuploidy of tumor genome often causes ambiguity in interpreting copy number status for a genomic fragment. We introduce a new bioinformatics tool called EBHMM to accurately infer CNAs from single tumor sample. EBHMM employs a novel hidden Markov model (HMM) to jointly analyze read depth and read counts signals derived from tumor NGS data. To improve CNA detection robustness against severe signal fluctuation and distribution shift of read counts induced by aneuploidy, EBHMM exploits more appropriate emission models in the HMM and prior knowledge to enable accurate inference. Experimental results on real data suggest the proposed method is highly effective in detecting CNAs as well as genome ploidy, and achieves comparable performance to the state-of-the-art methods. The EBHMM software is freely available at https://github.com/qasimyu/ebhmm.

In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos. Unlike existing fully supervised methods which rely on numerous annotations by hand, our learner exploits large scale unlabeled videos plus available labeled data to generate auxiliary plausible training annotations. Motivated by the fact that neighbouring facial landmarks are usually correlated and coherent across consecutive frames, our approach automatically reasons about discriminative spatial-temporal relationships among landmarks for stable face tracking. Specifically, we carefully develop an interpretable and efficient network module, which disentangles facial geometry relationship for every static frame and simultaneously enforces the bi-directional cycle-consistency across adjacent frames, thus allowing the modeling of intrinsic spatial-temporal relations from raw face sequences. Extensive experimental results demonstrate that our approach surpasses the performance of most fully supervised state-of-the-arts.

In this paper, we propose to learn a neighborhood-reasoning label distribution (NRLD) for facial age estimation. Unlike conventional label distribution methods with fixed-structural aging patterns, in this work, our NRLD aims to reason about more resilient and adaptive label distribution by disentangling the graph of face neighbors. In particular, our model holds the assumption on that the sample-specific age label distribution is principally influenced by a mixture of interpretable and meaningful factors, which typically cause plausible edges connected to the anchors. Under the scenario of each factor, we specifically collect the subset of graph edges and then convolute them with face samples to regress a mean-variance label distribution. During the training process, the mixture hyperparameters of our label distribution are iteratively optimized by following the Expectation-Maximization schema. Extensive experimental results on three challenging widely-evaluated datasets indicate the superiority in comparisons with most state of the arts.

In this paper, we introduce global compression learning (GCL) for finding reduced network architecture from a pre-trained network by removing both intra-layer and inter-layer structural redundancy. To accomplish this, we first derive architecture features from a binary representation of the network structure that effectively characterize the relationships between different layers. We then leverage reinforcement learning to iteratively compress the network via deep deterministic policy gradient based on the learned architecture features. To void extensive exploration of the huge space of network architectures, we bound feasible solutions within a small subspace by following a strict accuracy loss tolerance. Benchmarking tests show GCL outperforms the state-of-the-art models. On CIFAR-10 dataset, our model reduces 60.5% FLOPs and 93.3% parameters on VGG-16 without hurting the network accuracy, and yields a significantly compressed architecture for ResNet-110 by reductions of 71.92% FLOPs and 79.62% parameters with the cost of only 0.11% accuracy loss.

MOTIVATION: Allele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency. AVAILABILITY AND IMPLEMENTATION: SCSsim is freely available at https://github.com/qasimyu/scssim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: A number of simulators have been developed for emulating next-generation sequencing data by incorporating known errors such as base substitutions and indels. However, their practicality may be degraded by functional and runtime limitations. Particularly, the positional and genomic contextual information is not effectively utilized for reliably characterizing base substitution patterns, as well as the positional and contextual difference of Phred quality scores is not fully investigated. Thus, a more effective and efficient bioinformatics tool is sorely required. RESULTS: Here, we introduce a novel tool, SimuSCoP, to reliably emulate complex DNA sequencing data. The base substitution patterns and the statistical behavior of quality scores in Illumina sequencing data are fully explored and integrated into the simulation model for reliably emulating datasets for different applications. In addition, an integrated and easy-to-use pipeline is employed in SimuSCoP to facilitate end-to-end simulation of complex samples, and high runtime efficiency is achieved by implementing the tool to run in multithreading with low memory consumption. These features enable SimuSCoP to gets substantial improvements in reliability, functionality, practicality and runtime efficiency. The tool is comprehensively evaluated in multiple aspects including consistency of profiles, simulation of genomic variations and complex tumor samples, and the results demonstrate the advantages of SimuSCoP over existing tools. CONCLUSIONS: SimuSCoP, a new bioinformatics tool is developed to learn informative profiles from real sequencing data and reliably mimic complex data by introducing various genomic variations. We believe that the presented work will catalyse new development of downstream bioinformatics methods for analyzing sequencing data.

In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making full access to the limited training samples which likely leads to bias age prediction, our SADAL aims to seek batches of unobserved hard-negative samples based on existing training samples, which typically reinforces the discriminativeness of the learned feature representation for facial ages. Motivated by the fact that age labels are usually correlated in real-world scenarios, we carefully develop a similarity-aware function to well measure the distance of each face pair based on the age value gaps. Consequently, the age-difference information is exploited in the synthetic feature space for robust age estimation. During the learning process, we jointly optimize both procedures of generating hard negatives and learning discriminative age ranker via a sequence of adversarial-game iterations. Another major issue lies on that existing methods only enforce the indiscriminativeness within each class, which is probably trapped into model overfitting and thus the generation capacity is limited particularly on unseen age classes with many individuals. To circumvent this problem, we propose a variational deep adversarial learning (VDAL) paradigm, which learns to encode each face sample in two factorized parts, i.e., the intra-class variance distribution and the intra-class invariant class center. Moreover, our VDAL principally optimizes the variational confidence lower bound on the variational factorized feature representation. To better enhance the discriminativeness of the age representation, our VDAL further learns to encode the ordinal relationship among age labels in the reconstructed subspace. Experimental results on folds of widely-evaluated benchmarking datasets demonstrate that our approach achieves promising performance in contrast to most state-of-the-art age estimation methods.

In this paper, we propose a deformable hourglass networks (DHGN) approach to investigate the problem of face alignment, especially in such challenging cases when faces undergo large variations including severe poses, diverse expressions and partial occlusions in unconstrained environments. Unlike conventional feature extractions which cannot explicitly exploit irregular geometric structures for facial shapes, our DHGN learns a deformable mask to reduce the variances of facial deformation and extract attentional facial regions for robust feature representation. To achieve this, we carefully design a differential module, dubbed the deformable transformer, which typically incorporates with a regression sub-net to predict a set of offsets and a masking operator to filter the semantic facial parts for feature representation learning. To further reinforce the alignment performance, we integrate our designed modules in the paradigm of stacked hourglass networks and jointly optimize the network parameters in an end-to-end manner. Extensive experimental results demonstrate very compelling performance in comparisons to most state-of-the-art methods.

In this paper, we propose a multi-agent deep collaboration learning method (MADCL) for simultaneously detecting 2D facial landmarks and 3D facial landmarks projected from 3D to 2D, which aims at distinguishing the ambiguity caused by different perspectives. Above two facial annotations, there are a large number of public semantic areas and some very important private semantic areas. Our single agent captures and memorizes private features for iterations and multiple agents collaborate to learn public features. To achieve this, we design a collaboration learning mechanism to capture, memorize and share semantic information for enhancing the feature representation. Moreover, the input of traditional cascade regression methods is cropped directly from the raw facial image via the shape-indexed manner, which leads that the poor initial shapes likely bring about the predicted results getting worse and worse. We introduce the Markov decision process (MDP) to reason a better position of the initial shape by a reward function that reflects the shape quality. Authentic experimental results indicate that our MADCL consistently outperforms most state-of-the-art methods on two widely-evaluated challenging datasets.

In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making access to limited training samples which likely leads to sub-optima, our SADAL seeks sets of unobserved and plausible hard-examples based on existing training samples, which typically reinforces the discriminativeness of the learned feature descriptor for ages. Motivated by the fact that age labels are usually correlated in the real-world applications, we carefully develop a similarity-aware function in our approach, which dynamically measures each face pair with different weights based on different age value gaps. During the learning process, we jointly optimize both procedures of generating hard-examples and learning age estimator via a sequence of adversarial-game iterations. As a result, the smoothing aging pattern is exploited in the reconstructed hard-example space for robust age estimation. Experimental results on two standard benchmarking datasets show that our approach achieves superior performance compared with most state-of-the-art age estimation methods.

BACKGROUND: Copy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression. Accurate detection of copy number alterations is necessary for discovering cancer-causing genes. Whole-exome sequencing has become a widely used technology in the last decade for detecting various types of genomic aberrations in cancer genomes. However, there are several major issues encountered in these detection problems, including normal cell contamination, tumor aneuploidy, and intra-tumor heterogeneity. Especially, deciphering the intra-tumor heterogeneity is imperative for identifying clonal and subclonal copy number alterations. RESULTS: We introduce CloneCNA, a novel bioinformatics tool for efficiently addressing these issues and automatically detecting clonal and subclonal somatic copy number alterations from heterogeneous tumor samples. CloneCNA fully explores the log ratio of read counts between paired tumor-normal samples and tumor B allele frequency of germline heterozygous SNP positions, further employs efficient statistical models to quantitatively represent copy number status of tumor sample containing multiple clones. We examine CloneCNA on simulated heterogeneous and real tumor samples, and the results demonstrate that CloneCNA has higher power to detect copy number alterations than existing methods. CONCLUSIONS: CloneCNA, a novel algorithm is developed to efficiently and accurately identify somatic copy number alterations from heterogeneous tumor samples. We demonstrate the statistical framework of CloneCNA represents a remarkable advance for tumor whole-exome sequencing data. We expect that CloneCNA will promote cancer-focused studies for investigating the role of clonal evolution and elucidating critical events benefiting tumor tumourigenesis and progression.

MOTIVATION: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore, efficient computational methods are required to address these issues. RESULTS: We introduce CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors), a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B-allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples. AVAILABILITY AND IMPLEMENTATION: The CLImAT software package can be freely downloaded at http://bioinformatics.ustc.edu.cn/CLImAT/.