MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features

In a single-cell RNA-seq (scRNA-seq) data set, a high proportion of missing values (or an excessive number of zeroes) are frequently observed. For the related follow-up tasks, such as clustering analysis and differential expression analysis, a data set without missing values is generally required. Many imputation approaches have been proposed for this purpose. Multiple imputation (MI) is a well-established approach to address possible biases in a follow-up analysis result based on one-time imputed data. There is a lack of investigation on this in the analysis of scRNA-seq data. In this study, we have investigated how to efficiently apply the MI approach to the clustering analysis and the differential expression analysis of scRNA-seq data. We proposed an MI procedure for clustering analysis and an MI procedure for differential expression analysis. To demonstrate the improvements achieved by MI in clustering analysis and differential expression analysis of scRNA-seq data, we analyzed three well-known scRNA-seq data sets. scIGANs, an scRNA-seq imputation method based on the generative adversarial networks (GANs), has been recently proposed for scRNA-seq data imputation. Multiple randomly imputed data sets can be conveniently generated by this method. We implemented our MI procedures based on scIGANs. We demonstrated that MI yielded improved performances on the clustering analysis and differential expression analysis results. Our applications to experimental scRNA-seq data illustrated the advantages of MI over one-time imputation of missing values in scRNA-seq data.

The excessive amount of zeros in single-cell RNA-seq (scRNA-seq) data includes 'real' zeros due to the on-off nature of gene transcription in single cells and 'dropout' zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect three types of DE genes in scRNA-seq data with higher accuracy. The R package DEsingle is freely available at Bioconductor (https://bioconductor.org/packages/DEsingle). Supplementary data are available at Bioinformatics online.

Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single-cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq. Recently, a Bayesian correlation scheme that assigns low similarity to genes that have low confidence expression estimates has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering three ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression. We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

<p>Table_S1. Study cohort. Table_S2. The sample composition of each dataset. Table_S3. Patient information. Table_S4. Whole genome sequencing (WGS) datasets. 1,411 UC whole genomes. Table_S5. Whole exome sequencing (WES) datasets. Table_S6. RNA sequencing (RNA-seq) datasets. Table_S7. Circle-seq datasets. Table_S8. Single-cell RNA-seq (scRNA-seq) datasets. Table_S9. Amplicon classification. Table_S10. Amplicon similarity scores. Table_S11. ecDNA prevalence in human cancers. Table_S12. Genomic and sequence features of amplicons. Table_S13. Oncogene list. Table_S14. Genes encoded on all focal amplifications. Table_S15. ecDNA status and CNV ITH in tumor multi-region sampling cohort. Table_S16. Diver gene alterations in ecDNA- and ecDNA+ tumors. Table_S17. Summary of Single-cell RNA-seq (scRNA-seq) data. Table_S18. IHC evaluation of CD45 in 19 tumors with scRNA-seq data. Table_S19. Signature genes of 50 robust NMF programs. Table_S20. Pathway enriched in each NMF program. Table_S21. Top 30 signature genes of tumor meta-program. Table_S22. Differential pathways of malignant cells between ecDNA- and ecDNA+ tumors. Table_S23. Gene signature matrix for deconvolution analysis of bulk RNA-seq data. Table_S24. Software.</p>

Recently, single-cell RNA-seq (scRNA-seq) data have been used to study cellular communication. Most bioinformatics methods infer only the intercellular signaling between groups of cells, mainly exploiting ligand-receptor expression levels. Only few methods consider the entire intercellular + intracellular signaling, mainly inferring lists/networks of signaling involved genes. Here, we present scSeqComm, a computational method to identify and quantify the evidence of ongoing intercellular and intracellular signaling from scRNA-seq data, and at the same time providing a functional characterization of the inferred cellular communication. The possibility to quantify the evidence of ongoing communication assists the prioritization of the results, while the combined evidence of both intercellular and intracellular signaling increase the reliability of inferred communication. The application to a scRNA-seq dataset of tumor microenvironment, the agreement with independent bioinformatics analysis, the validation using spatial transcriptomics data and the comparison with state-of-the-art intercellular scoring schemes confirmed the robustness and reliability of the proposed method. scSeqComm R package is freely available at https://gitlab.com/sysbiobig/scseqcomm and https://sysbiobig.dei.unipd.it/software/#scSeqComm. Submitted software version and test data are available in Zenodo, at https://dx.doi.org/10.5281/zenodo.5833298. Supplementary data are available at Bioinformatics online.

BackgroundCancer-associated fibroblasts (CAFs) are involved in tumor growth, angiogenesis, metastasis, and resistance to therapy. We sought to explore the CAFs characteristics in hepatocellular carcinoma (HCC) and establish a CAF-based risk signature for predicting the prognosis of HCC patients.MethodsThe signal-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of HCC were obtained from the TCGA and GEO databases respectively. Seurat R package was applied to process scRNA-seq data and identify CAF clusters according to the CAF markers. Differential expression analysis was performed to screen differentially expressed genes (DEGs) between normal and tumor samples in TCGA dataset. Then Pearson correlation analysis was used to determine the DEGs associated with CAF clusters, followed by the univariate Cox regression analysis to identify CAF-related prognostic genes. Lasso regression was implemented to construct a risk signature based on CAF-related prognostic genes. Finally, a nomogram model based on the risk signature and clinicopathological characteristics was developed.ResultsBased on scRNA-seq data, we identified 4 CAF clusters in HCC, 3 of which were associated with prognosis in HCC. A total of 423 genes were identified from 2811 DEGs to be significantly correlated with CAF clusters, and were narrowed down to generate a risk signature with 6 genes. These six genes were primarily connected with 39 pathways, such as angiogenesis, apoptosis, and hypoxia. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for HCC, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of HCC.ConclusionCAF-based risk signatures can effectively predict the prognosis of HCC, and comprehensive characterization of the CAF signature of HCC may help to interpret the response of HCC to immunotherapy and provide new strategies for cancer treatment.

High-throughput sequencing advancements have shifted genomic project bottlenecks from data generation to computational storage and analysis. Single-cell RNA-seq (scRNA-seq) data exhibits unique structural features, including extensive labeled sequence identifiers, which conventional compression tools fail to optimize. This study proposes ScBlkCom, a specialized compression scheme for scRNA-seq data. The method partitions sequencing data into distinct blocks and applies tailored compression strategies: differential encoding for numerical attributes, Huffman coding for categorical labels, and context-adaptive encoding for sequence identifiers. Experiments demonstrate ScBlkCom achieves 84.29% higher compression gain compared to single-module approaches and outperforms generic tools (e.g., GZIP, BZIP2) by 6.44% in compression ratio, while maintaining stable processing speeds. This block-wise adaptive framework effectively addresses scRNA-seq data redundancy, offering enhanced storage efficiency for large-scale single-cell studies.

BackgroundSingle-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the technical biases in experiments, the raw scRNA-seq data usually includes large noise and makes the downstream analyses complicated. Although many methods have been proposed to impute the noisy scRNA-seq data in recent years, few of them take into account the prior associations across genes in imputation and integrate multiple types of imputation data to identify cell types.ResultsWe present a new framework, NetImpute, towards the identification of cell types from scRNA-seq data by integrating multiple types of biological networks. We employ a statistic method to detect the noise data items in scRNA-seq data and develop a new imputation model to estimate the real values of data noise by integrating the PPI network and gene pathways. Meanwhile, based on the data imputed by multiple types of biological networks, we propose an integrated approach to identify cell types from scRNA-seq data. Comprehensive experiments demonstrate that the proposed network-based imputation model can estimate the real values of noise data items accurately and integrating the imputation data based on multiple types of biological networks can improve the identification of cell types from scRNA-seq data.ConclusionsIncorporating the prior gene associations in biological networks can potentially help to improve the imputation of noisy scRNA-seq data and integrating multiple types of network-based imputation data can enhance the identification of cell types. The proposed NetImpute provides an open framework for incorporating multiple types of biological network data to identify cell types from scRNA-seq data.

Background: Cancer stem cells (CSCs) play a key role in tumor initiation, progression, and resistance to conventional therapies. These cells possess the ability to self-renew and differentiate, contributing to tumor heterogeneity and recurrence. Preclinical data indicate that targeting cancer stem cell-specific pathways could lead to more effective treatments and prevent relapse, thereby improving outcomes. However, there is limited clinical data to support this, in part due to the challenges of measuring CSCs in the clinical setting. Single-cell RNA sequencing (scRNAseq) data in combination with stemness gene expression signatures were investigated as a novel approach to detect and assess changes in CSC numbers in response to treatment. Methods: Single-cell RNAseq datasets from two clinical trials were interrogated – OPPORTUNE, a window-of-opportunity trial evaluating anastrozole vs anastrozole plus pictilisib (PI3K inhibitor) in 75 patients with ER+ HER2- early breast cancer, and FELINE, a neoadjuvant trial which compared letrozole plus placebo with letrozole plus ribociclib (CDK4/6 inhibitor) in 120 patients with ER+ HER2- early breast cancer. The primary endpoint for OPPORTUNE was the inhibition of tumor cell proliferation as measured by Ki67. The primary endpoint for FELINE was the rate of preoperative endocrine prognostic index (PEPI) score 0 after neoadjuvant endocrine therapy. Thirty-four patients from the FELINE study had available scRNAseq data for analysis; this number was 62 patients for the OPPORTUNE study. Stemness gene expression signatures with published evidence of selectivity for breast CSCs were identified from the literature and their utility in detecting CSCs compared in the datasets. Subsequently, changes in CSC fraction with treatment was assessed. Results: Eight different stemness gene signatures were identified from the published literature. Among these, four (Kim_myc, benporath_es2, Bhattacharya_hESC, and Shats_consensus) exhibited selective expression in a minority of tumor cells, with the Shats consensus signature demonstrating the highest specificity to tumor cells over non-malignant cells. Utilizing a Gaussian mixture model, we estimated that approximately 4.5% of cells within a tumor are likely stem cells. In the OPPORTUNE study, higher stemness scores were observed in luminal B compared to luminal A tumors, but there was no association with PIK3CA mutation status. Changes to Ki67 were inversely associated with the CSC fraction – tumors with higher stemness scores were less likely to achieve a complete cell cycle arrest versus endocrine-sensitive tumors. Similarly, in the FELINE trial, non-responders in the letrozole arm showed a trend towards elevated stemness scores. Conclusion: The use of stemness gene expression signatures in scRNAseq data is a feasible method to assess changes in putative CSC fraction with treatment in breast cancer. Increased stemness scores were associated with more aggressive subtypes and resistance to treatment. Citation Format: Peter Hall,Alejandro Chibly, Peter Schmid, Sarah E Pinder, Arnie Purushotham, Alastair Thompson, Steven Gendreau. Evaluation of breast cancer stem cell gene expression signatures in single-cell RNA sequencing (scRNAseq) data from the OPPORTUNE and FELINE trials, and the association with treatment resistance [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS13-01.

BackgroundLiver cancer (LC) is a heterogeneous tumor with unfavorable prognosis. T cells play key roles in tumor surveillance and cancer immunotherapy. The aim of this study is to explore the...

Joint learning of multiple gene networks from single-cell gene expression data

Introduction. The interaction between malignant cells (MCs), stromal cells, tumor-associated lymphocytes (TILs), and tumor-associated macrophages (TAMs) is relevant for non-small cell lung carcinoma (NSCLC) progression. The spatial distribution of those cells may affect the prognosis and can be related to genetic intra-tumor heterogeneity (ITH). The aim of this study was to characterize the immunologic ITH and the spatial distribution of immune cells to MCs in primary NSCLC tumors at early stages using multiplex immunofluorescence (mIF) and image analysis approaches. Material and methods. We studied 33 surgically resected NSCLC cases (adenocarcinomas=23; squamous-cell carcinomas=10) with a history of recurrence in a follow-up of at least 60 months (recurrence, N=13; non-recurrence, N=15). Consecutive FFPE tissue sections were stained with two mIF panels (panel 1: cytokeratin (AE1/AE3), PD-L1, PD-1, CD3, CD8, and CD68; panel 2: AE1/AE3, CD3, CD8, granzyme-B, CD45RO, and FOXP3). Three intra-tumor regions (3mm2 each) per case were selected after gridding the whole tumor section. A total of 99 intratumor regions were scanned and analyzed using Vectra Multispectral-Microscope and InForm-software. From each intratumor region, TILs and TAMs densities, as well as the coefficient of variation, were evaluated. The median distance and the G-Cross area under the curve (AUC) for specific radial distances (10µm, 20µm, and 40µm) were obtained between TILs and TAMs phenotypes to MCs. Results. Recurrence was associated with higher MCs density and TAMs/TILs ratio, and lower TIL densities. A high ITH of cytotoxic T-cells (CTLs) PD-L1+ was associated with worse survival. The distance of TAMs PD-L1+ to MCs PD-L1 negative (60µm vs 25µm) or to MCs PD-L1 positive (25µm vs 13µm) was higher in the non-recurrence group than in recurrence group. Close TAMs PD-L1+ to MCs was associated with worst survival. In a radial distance of 10µm, 20µm, and 40µm, a higher infiltration of CTLs PD-1+, was observed in the group of recurrence than non-recurrence group, surrounding MCs PD-L1 negative (AUC 0.49, 3.80, and 20.03; vs AUC 0.01, 0.16, and 1.29, respectively), and MCs PD-L1 positive (AUC 0.60, 4.35, and 19.90; vs AUC 0.01, 0.20, and 2.20, respectively). A high infiltration of CTLs PD-1+ surrounding MCs, with or without expression of PD-L1, was associated with worse survival. All the differences were statistically significant (P<0.05). Conclusion. Close spatial proximity of antigen-experienced CTLs and TAMs PD-L1+ to MCs are associated with recurrence and poor survival in early stages of NSCLC. We determined that ITH of immune cell densities is associated with recurrence of surgically resected NSCLC. Tumor-immune cell spatial modeling offers a deep understanding of tumor microenvironment that impacts on clinical outcomes. Supported by CPRITRP160668 and UT Lung SPORE grants Citation Format: Alejandro Francisco-Cruz, Edwin R. Parra, Santhoshi N. Krishnan, Souptik Barua, Mei Jiang, Junya Fujimoto, Christine B. Peterson, Priyam Das, Chi-Wan Chow, Jaime Rodriguez-Canales, Carmen Behrens, Neda Kalhor, Annikka Weissferdt, John Heymach, Stephen Swisher, Boris Sepesi, Arvind Rao, J. Jack Lee, Cesar Moran, Andrew Futreal, Jianjun Zhang, Ignacio I. Wistuba. Impact of the spatial analysis of tumor-associated lymphocytes and tumor-associated macrophages on recurrence at early stage of non-small cell lung carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1180.

The spatial distribution of immune and non-immune cells within tissues and the expression of cell-specific markers provides essential information on cell function in situ. Human skin is a highly complex organ with defined compartments comprising different cells with diverse phenotypes. By using multi-fluorescence labeling, distinct skin cell populations can be determined and further characterized. However, currently, the availability of non-commercial tools for image analysis of skin samples that allow in silico segmentation of cells specifically within the epidermis or the dermis at a single-cell resolution is limited. We provide here a step-by-step protocol for immunofluorescence staining of skin sections, confocal microscopy, and image analysis using our freely available tool SCAnED (Skin Compartment Analysis of Epidermis and Dermis). The SCAnED macro allows the precise identification and classification of cells and nuclei in both the epidermal and dermal compartments of human skin. We provide guidelines on how to determine average intensity levels of maker expression in different cells and cellular compartments, such as the cytoplasm and the nucleus, and how to quantify cells expressing varying amounts of these markers with an accompanying Python pipeline provided as a ready-to-use Jupyter notebook executed in Google Colab. This protocol will allow inexperienced users to determine cell-specific expression profiles within skin tissue and provide insights into the spatial distribution of cells within the epidermal and dermal compartments, allowing a deeper understanding of the intricate tissue structure and cell composition of human skin.

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR.