Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here, we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.     

Host microRNAs exhibit differential propensity to interact with SARS-CoV-2 and variants of concern

A significant number of SARS-CoV-2-infected individuals naturally overcome viral infection, suggesting the existence of a potent endogenous antiviral mechanism. As an innate defense mechanism, microRNA (miRNA) pathways in mammals have evolved to restrict viruses, besides regulating endogenous mRNAs. In this study, we systematically examined the complete repertoire of human miRNAs for potential binding sites on SARS-CoV-2 Wuhan-Hu-1, Beta, Delta, and Omicron. Human miRNA and viral genome interaction were analyzed using RNAhybrid 2.2 with stringent parameters to identify highly bonafide miRNA targets. Using publicly available data, we filtered for miRNAs expressed in lung epithelial cells/tissue and oral keratinocytes, concentrating on the miRNAs that target SARS-CoV-2 S protein mRNAs. Our results show a significant loss of human miRNA and SARS-CoV-2 interactions in Omicron (130 miRNAs) compared to Wuhan-Hu-1 (271 miRNAs), Beta (279 miRNAs), and Delta (275 miRNAs). In particular, hsa-miR-3150b-3p and hsa-miR-4784 show binding affinity for S protein of Wuhan strain but not Beta, Delta, and Omicron. Loss of miRNA binding sites on N protein was also observed for Omicron. Through Ingenuity Pathway Analysis (IPA), we examined the experimentally validated and highly predicted functional role of these miRNAs. We found that hsa-miR-3150b-3p and hsa-miR-4784 have several experimentally validated or highly predicted target genes in the Toll-like receptor, IL-17, Th1, Th2, interferon, and coronavirus pathogenesis pathways. Focusing on the coronavirus pathogenesis pathway, we found that hsa-miR-3150b-3p and hsa-miR-4784 are highly predicted to target MAPK13. Exploring miRNAs to manipulate viral genome/gene expression can provide a promising strategy with successful outcomes by targeting specific VOCs.     

Comparative microarray analysis of microRNA expression profiles in primary cutaneous malignant melanoma, cutaneous malignant melanoma metastases, and benign melanocytic nevi

Perturbations in microRNA (miRNA) expression profiles have been reported for cutaneous malignant melanoma (CMM) predominantly when examined in cell lines. Despite the rapidly growing number of newly discovered human miRNA sequences, the availability of up-to-date miRNA expression profiles for clinical samples of primary cutaneous malignant melanoma (PCMM), cutaneous malignant melanoma metastases (CMMM), and benign melanocytic nevi (BMN) is limited. Specimens excised from the center of tumors (lesional) from patients with PCMM (n=9), CMMM (n=4), or BMN (n=8) were obtained during surgery. An exploratory microarray analysis was performed by miRNA expression profiling based on Agilent platform screening for 1205 human miRNAs. The results from the microarray analysis were validated by TaqMan quantitative real-time polymerase chain reaction. In addition to several miRNAs previously known to be associated with CMM, 19 unidentified miRNA candidates were found to be dysregulated in CMM patient samples. Among the 19 novel miRNA candidates, the genes hsa-miR-22, hsa-miR-130b, hsa-miR-146b-5p, hsa-miR-223, hsa-miR-301a, hsa-miR-484, hsa-miR-663, hsa-miR-720, hsa-miR-1260, hsa-miR-1274a, hsa-miR-1274b, hsa-miR-3663-3p, hsa-miR-4281, and hsa-miR-4286 were upregulated, and the genes hsa-miR-24-1*, hsa-miR-26a, hsa-miR-4291, hsa-miR-4317, and hsa-miR-4324 were downregulated. The results of this study partially confirm previous CMM miRNA profiling studies identifying miRNAs that are dysregulated in CMM. However, we report several novel miRNA candidates in CMM tumors; these miRNA sequences require further validation and functional analysis to evaluate whether they play a role in the pathogenesis of CMM.     

Microarray analysis of microRNA expression in liver cancer tissues and normal control

Background

Recently, many studies have focused on microRNAs (miRNAs) expression profiling in liver cancer, due to the ability of these small RNAs to potently influence cellular behavior. In this study, to further investigate the relationship between them, the miRNA expression profiling of the cancer liver tissues and normal liver tissues were compared.

Methods

The datasets of miRNAs microarray in liver cancer and normal control were downloaded from Gene Expression Omnibus. Then the SOAP analysis was performed to identify the differentially expressed miRNAs.

Results

A total of 221 differentially expressed miRNAs were found. Five of them (including hsa-miR-15b, hsa-miR-1975, hsa-miR-199a-3p, hsa-miR-199b-3p and hsa-miR-421) were determined by t-test and may be involved in the pathogenesis of liver cancer.

Conclusion

There differentially expressed miRNAs may be potential molecular markers for liver cancer screening.     

coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data

Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-seq data and sc-methylation data, usually have different powers in identifying the unknown cell types through clustering. So, methods that integrate multiple datasets can potentially lead to a better clustering performance. Here we propose coupleCoC+ for the integrative analysis of single-cell genomic data. coupleCoC+ is a transfer learning method based on the information-theoretic co-clustering framework. In coupleCoC+, we utilize the information in one dataset, the source data, to facilitate the analysis of another dataset, the target data. coupleCoC+ uses the linked features in the two datasets for effective knowledge transfer, and it also uses the information of the features in the target data that are unlinked with the source data. In addition, coupleCoC+ matches similar cell types across the source data and the target data. By applying coupleCoC+ to the integrative clustering of mouse cortex scATAC-seq data and scRNA-seq data, mouse and human scRNA-seq data, mouse cortex sc-methylation and scRNA-seq data, and human blood dendritic cells scRNA-seq data from two batches, we demonstrate that coupleCoC+ improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. coupleCoC+ has fast convergence and it is computationally efficient. The software is available at https://github.com/cuhklinlab/coupleCoC_plus.     

Methods for predicting single-cell miRNA in breast cancer

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.     

A novel vector-based method for exclusive overexpression of star-form microRNAs

The roles of microRNAs (miRNAs) as important regulators of gene expression have been studied intensively. Although most of these investigations have involved the highly expressed form of the two mature miRNA species, increasing evidence points to essential roles for star-form microRNAs (miRNA*), which are usually expressed at much lower levels. Owing to the nature of miRNA biogenesis, it is challenging to use plasmids containing miRNA coding sequences for gain-of-function experiments concerning the roles of microRNA* species. Synthetic microRNA mimics could introduce specific miRNA* species into cells, but this transient overexpression system has many shortcomings. Here, we report that specific miRNA* species can be overexpressed by introducing artificially designed stem-loop sequences into short hairpin RNA (shRNA) overexpression vectors. By our prototypic plasmid, designed to overexpress hsa-miR-146b-3p, we successfully expressed high levels of hsa-miR-146b-3p without detectable change of hsa-miR-146b-5p. Functional analysis involving luciferase reporter assays showed that, like natural miRNAs, the overexpressed hsa-miR-146b-3p inhibited target gene expression by 3'UTR seed pairing. Our demonstration that this method could overexpress two other miRNAs suggests that the approach should be broadly applicable. Our novel strategy opens the way for exclusively stable overexpression of miRNA* species and analyzing their unique functions both in vitro and in vivo.     

A Ten-MicroRNA Signature Identified from a Genome-Wide MicroRNA Expression Profiling in Human Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most common gynecologic malignancy. To identify the micro-ribonucleic acids (miRNAs) expression profile in EOC tissues that may serve as a novel diagnostic biomarker for EOC detection, the expression of 1722 miRNAs from 15 normal ovarian tissue samples and 48 ovarian cancer samples was profiled by using a quantitative real-time polymerase chain reaction (qRT-PCR) assay. A ten-microRNA signature (hsa-miR-1271-5p, hsa-miR-574-3p, hsa-miR-182-5p, hsa-miR-183-5p, hsa-miR-96-5p, hsa-miR-15b-5p, hsa-miR-182-3p, hsa-miR-141-5p, hsa-miR-130b-5p, and hsa-miR-135b-3p) was identified to be able to distinguish human ovarian cancer tissues from normal tissues with 97% sensitivity and 92% specificity. Two miRNA clusters of miR183-96-183 (miR-96-5p, and miR-182, miR183) and miR200 (miR-141-5p, miR200a, b, c and miR429) are significantly up-regulated in ovarian cancer tissue samples compared to those of normal tissue samples, suggesting theses miRNAs may be involved in ovarian cancer development.     

Iliski,a software for robust calculation of transfer functions

Understanding the relationships between biological processes is paramount to unravel pathophysiological mechanisms. These relationships can be modeled with Transfer Functions (TFs), with no need of a priori hypotheses as to the shape of the transfer function. Here we present Iliski, a software dedicated to TFs computation between two signals. It includes different pre-treatment routines and TF computation processes: deconvolution, deterministic and non-deterministic optimization algorithms that are adapted to disparate datasets. We apply Iliski to data on neurovascular coupling, an ensemble of cellular mechanisms that link neuronal activity to local changes of blood flow, highlighting the software benefits and caveats in the computation and evaluation of TFs. We also propose a workflow that will help users to choose the best computation according to the dataset. Iliski is available under the open-source license CC BY 4.0 on GitHub (https://github.com/alike-aydin/Iliski) and can be used on the most common operating systems, either within the MATLAB environment, or as a standalone application.     

miRNA在人骨髓来源间充质干细胞软骨诱导分化过程中的表达

目的：研究miRNAs在人骨髓来源间充质干细胞软骨诱导分化过程中的表达情况。方法：以从骨髓中分离培养的MSCs及软骨诱导培养后的细胞为实验对象,利用基因芯片检测miRNAs的表达情况,由SAM分析得到MSCs较其诱导培养细胞中差异表达的miRNAs,再进行生物信息学分析。结果：①分离培养出的MSCs经软骨诱导培养21天后,已具有软骨细胞特性,经芯片检测并SAM分析,软骨诱导培养的细胞较MSCs高表达的miRNAs有6个：hsa-miR-572、hsa-miR-130b、hsa-miR-193b、hsa-miR-28、hsa-miR-152、hsa-miR-560;软骨诱导培养的细胞较MSCs低表达的miRNAs有2个：hsa-miR-424、hsa-miR-122a。②利用TargetScan预测其靶基因,并行生物信息学分析,其中hsa-miR-130b、hsa-miR-193b、hsa-miR-152及hsa-miR-424的预测靶基因中多为参与细胞分化、骨形成、软骨形成及干细胞表型相关的基因。结论：hsa-miR-130b、hsa-miR-193b、hsa-miR-152和hsa-miR-424等对人骨髓来源间充质干细胞的软骨分化起着重要调控作用。     

SPOROS: A pipeline to analyze DISE/6mer seed toxicity

microRNAs (miRNAs) are (18-22nt long) noncoding short (s)RNAs that suppress gene expression by targeting the 3’ untranslated region of target mRNAs. This occurs through the seed sequence located in position 2-7/8 of the miRNA guide strand, once it is loaded into the RNA induced silencing complex (RISC). G-rich 6mer seed sequences can kill cells by targeting C-rich 6mer seed matches located in genes that are critical for cell survival. This results in induction of Death Induced by Survival gene Elimination (DISE), through a mechanism we have called 6mer seed toxicity. miRNAs are often quantified in cells by aligning the reads from small (sm)RNA sequencing to the genome. However, the analysis of any smRNA Seq data set for predicted 6mer seed toxicity requires an alternative workflow, solely based on the exact position 2–7 of any short (s)RNA that can enter the RISC. Therefore, we developed SPOROS, a semi-automated pipeline that produces multiple useful outputs to predict and compare 6mer seed toxicity of cellular sRNAs, regardless of their nature, between different samples. We provide two examples to illustrate the capabilities of SPOROS: Example one involves the analysis of RISC-bound sRNAs in a cancer cell line (either wild-type or two mutant lines unable to produce most miRNAs). Example two is based on a publicly available smRNA Seq data set from postmortem brains (either from normal or Alzheimer’s patients). Our methods (found at https://github.com/ebartom/SPOROS and at Code Ocean: https://doi.org/10.24433/CO.1732496.v1) are designed to be used to analyze a variety of smRNA Seq data in various normal and disease settings.     

Ultra-large alignments using phylogeny-aware profiles

Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, a multiple sequence alignment method that uses a new machine learning technique, the ensemble of hidden Markov models, which we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0688-z) contains supplementary material, which is available to authorized users.     

Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.     

MicroRNA miR-21 overexpression in human breast cancer is associated with advanced clinical stage, lymph node metastasis and patient poor prognosis

To investigate the global expression profile of miRNAs in primary breast cancer (BC) and normal adjacent tumor tissues (NATs) and its potential relevance to clinicopathological characteristics and patient survival, the genome-wide expression profiling of miRNAs in BC was investigated using a microarray containing 435 mature human miRNA oligonucleotide probes. Nine miRNAs of hsa-miR-21, hsa-miR-365, hsa-miR-181b, hsa-let-7f, hsa-miR-155, hsa-miR-29b, hsa-miR-181d, hsa-miR-98, and hsa-miR-29c were observed to be up-regulated greater than twofold in BC compared with NAT, whereas seven miRNAs of hsa-miR-497, hsa-miR-31, hsa-miR-355, hsa-miR-320, rno-mir-140, hsa-miR-127 and hsa-miR-30a-3p were observed to be down-regulated greater than twofold. The most significantly up-regulated miRNAs, hsa-mir-21 (miR-21), was quantitatively analyzed by TaqMan real-time PCR in 113 BC tumors. Interestingly, among the 113 BC cases, high level expression of miR-21 was significantly correlated with advanced clinical stage (P = 0.006, Fisher's exact text), lymph node metastasis (P = 0.007, Fisher's exact text), and shortened survival of the patients (hazard ratio [HR]=5.476, P < 0.001). Multivariate Cox regression analysis revealed this prognostic impact (HR=4.133, P = 0.001) to be independent of disease stage (HR=2.226, P = 0.013) and histological grade (HR=3.681, P = 0.033). This study could identify the differentiated miRNAs expression profile in BC and reveal that miR-21 overexpression was correlated with specific breast cancer biopathologic features, such as advanced tumor stage, lymph node metastasis, and poor survival of the patients, indicating that miR-21 may serve as a molecular prognostic marker for BC and disease progression.     

Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine

Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations.Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones.We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees.The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.