Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data

2020 ◽  
pp. 223-232 ◽  
Author(s):  
Gregor Sturm ◽  
Francesca Finotello ◽  
Markus List
Keyword(s):  
Rna Sequencing ◽  
Computational Methods ◽  
Immune Cell ◽  
R Package ◽  
Sequencing Data ◽  
Cell Fractions

scholarly journals IMMU-35. TRANSCRIPTIONALLY DEFINED IMMUNE CONTEXTURE IN HUMAN GLIOMAS AT SINGLE-CELL RESOLUTION

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii112-ii112
Author(s):  
Pravesh Gupta ◽  
Minghao Dang ◽  
Krishna Bojja ◽  
Tuan Tran M ◽  
Huma Shehwana ◽  
...  
Keyword(s):  
Dendritic Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Wild Type ◽  
Sequencing Data ◽  
Human Gliomas ◽  
Cell Clusters ◽  
Recurrent Gliomas ◽  
Antigen Presenting

Abstract The brain tumor immune microenvironment (TIME) continuously evolves during glioma progression and a comprehensive understanding of the glioma-centric immune cell repertoire beyond a priori cell types and/or states is uncharted. Consequently, we performed single-cell RNA-sequencing on ~123,000 tumor-derived immune cells from 17-pathologically stratified, IDH (isocitrate dehydrogenase)-differential primary, recurrent human gliomas, and non-glioma brains. Our analysis delineated predominant 34-myeloid cell clusters (~75%) over 28-lymphoid cell clusters (~25%) reflecting enormous heterogeneity within and across gliomas. The glioma immune diversity spanned functionally imprinted phagocytic, antigen-presenting, hypoxia, angiogenesis and, tumoricidal myeloid to classical cytotoxic lymphoid subpopulations. Specifically, IDH-mutant gliomas were enriched for brain-resident microglial subpopulations in contrast to enhanced bone barrow-derived infiltrates in IDH-wild type, especially in a recurrent setting. Microglia attrition in IDH-wild type -primary and -recurrent gliomas were concomitant with invading monocyte-derived cells with semblance to dendritic cell and macrophage/microglia like transcriptomic features. Additionally, microglial functional diversification was noted with disease severity and mostly converged to inflammatory states in IDH-wild type recurrent gliomas. Beyond dendritic cells, multiple antigen-presenting cellular states expanded with glioma severity especially in IDH-wild type primary and recurrent- gliomas. Furthermore, we noted differential microglia and dendritic cell inherent antigen presentation axis viz, osteopontin, and classical HLAs in IDH subtypes and, glioma-wide non-PD1 checkpoints associations in T cells like Galectin9 and Tim-3. As a general utility, our immune cell deconvolution approach with single-cell-matched bulk RNA sequencing data faithfully resolved 58-cell states which provides glioma specific immune reference for digital cytometry application to genomics datasets. Resultantly, we identified prognosticator immune cell-signatures from TCGA cohorts as one of many potential immune responsiveness applications of the curated signatures for basic and translational immune-genomics efforts. Thus, we not only provide an unprecedented insight of glioma TIME but also present an immune data resource that can be exploited to guide pragmatic glioma immunotherapy designs.

scholarly journals The Crosstalk Between Malignant Cells and Tumor-Promoting Immune Cells Relevant to Immunotherapy in Pancreatic Ductal Adenocarcinoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Xuefei Liu ◽  
Ziwei Luo ◽  
Xuechen Ren ◽  
Zhihang Chen ◽  
Xiaoqiong Bao ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Immune Cell ◽  
Immune Checkpoint Blockade ◽  
Ductal Adenocarcinoma ◽  
Malignant Cells ◽  
Sequencing Data ◽  
Immunosuppressive Microenvironment

Background: Pancreatic ductal adenocarcinoma (PDAC) is dominated by an immunosuppressive microenvironment, which makes immune checkpoint blockade (ICB) often non-responsive. Understanding the mechanisms by which PDAC forms an immunosuppressive microenvironment is important for the development of new effective immunotherapy strategies.Methods: This study comprehensively evaluated the cell-cell communications between malignant cells and immune cells by integrative analyses of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC. A Malignant-Immune cell crosstalk (MIT) score was constructed to predict survival and therapy response in PDAC patients. Immunological characteristics, enriched pathways, and mutations were evaluated in high- and low MIT groups.Results: We found that PDAC had high level of immune cell infiltrations, mainly were tumor-promoting immune cells. Frequent communication between malignant cells and tumor-promoting immune cells were observed. 15 ligand-receptor pairs between malignant cells and tumor-promoting immune cells were identified. We selected genes highly expressed on malignant cells to construct a Malignant-Immune Crosstalk (MIT) score. MIT score was positively correlated with tumor-promoting immune infiltrations. PDAC patients with high MIT score usually had a worse response to immune checkpoint blockade (ICB) immunotherapy.Conclusion: The ligand-receptor pairs identified in this study may provide potential targets for the development of new immunotherapy strategy. MIT score was established to measure tumor-promoting immunocyte infiltration. It can serve as a prognostic indicator for long-term survival of PDAC, and a predictor to ICB immunotherapy response.

schex avoids overplotting for large single-cell RNA-sequencing datasets

2019 ◽  
Vol 36 (7) ◽  
pp. 2291-2292 ◽  
Author(s):  
Saskia Freytag ◽  
Ryan Lister
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

2019 ◽  
Vol 35 (22) ◽  
pp. 4827-4829 ◽  
Author(s):  
Xiao-Fei Zhang ◽  
Le Ou-Yang ◽  
Shuo Yang ◽  
Xing-Ming Zhao ◽  
Xiaohua Hu ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Ensemble Learning ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
The Individual ◽  
Downstream Analysis ◽  
Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals scds: computational annotation of doublets in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Abha S Bais ◽  
Dennis Kostka
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Binary Classification ◽  
Single Cells ◽  
Computational Cost ◽  
Original Data ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study’s conclusions, and therefore computational strategies for the identification of doublets are needed. Results With scds, we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scds presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. Availability and implementation scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deletion of Lats 1 and 2 in mouse pancreatic acinar cells induces pancreatic fibrosis and inflammation

2019 ◽  
Author(s):  
Ming Gao ◽  
Jun Liu ◽  
Michael Nipper ◽  
Francis E. Sharkey ◽  
Randy L. Johnson ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Acinar Cell ◽  
Immune Cell ◽  
Stellate Cell ◽  
Rapid Development ◽  
Critical Role ◽  
Acinar Cells ◽  
Pancreatic Fibrosis ◽  
Pancreatic Acinar Cells ◽  
Sequencing Data

AbstractObjectiveThe Hippo signaling pathway is known for regulating proliferation, differentiation, organ size, and tumorigenesis. Large tumor suppressor kinase 1 and 2 (LATS1&2) are the core kinases of this pathway, whose functions in both the normal pancreas and pancreatic diseases are unclear. We studied the function of LATS1&2 specifically in pancreatic acinar cells of adult mice.DesignWe generated mice with adult pancreatic acinar cell–specific deletion of Lats1&2 genes by using CreER/LoxP. Pancreata were analyzed by histological examination, immunostaining, western blot, and RNA-sequencing.ResultsDeletion of Lats1&2 genes in adult pancreatic acinar cells resulted in rapid development of pancreatic inflammation and fibrosis. Loss of Lats1&2 did not directly induce acinar cell proliferation or apoptosis, but resulted in pancreatic stellate cell (PSC) activation followed by immune cell infiltration and acinar-to-ductal metaplasia. These effects were mediated by the Hippo downstream effectors YAP1/TAZ. By using neutralizing antibody to block CTGF, a YAP1/TAZ target, the inflammation and fibrosis were reduced. Our RNA-sequencing data identified upregulation of fibroinflammatory genes in Lats1&2 null pancreata, which may play important roles in stimulating PSC activation and promoting pancreatic fibrosis, as well as inflammation.ConclusionsDeletion of the Lats1&2 genes from adult acinar cells leads to the YAP1/TAZ dependent upregulation of a fibroinflammatory program. Our results emphasize the critical role of Lats1&2 in regulating PSC activation. Our findings identify new strategies for controlling pancreatic inflammation and fibrosis in diseases such as pancreatitis and pancreatic cancer.

scholarly journals FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D. Aevermann ◽  
Trygve E. Bakken ◽  
Jeremy A. Miller ◽  
Rebecca D. Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

AbstractSingle cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method – FR-Match – that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8192 ◽  
Author(s):  
Gökhan Karakülah ◽  
Nazmiye Arslan ◽  
Cihangir Yandım ◽  
Aslı Suner
Keyword(s):  
Gene Expression ◽  
Linear Regression ◽  
Transposable Elements ◽  
Regression Model ◽  
Rna Sequencing ◽  
Linear Regression Model ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sequencing Data ◽  
Data Set

Introduction Recent studies highlight the crucial regulatory roles of transposable elements (TEs) on proximal gene expression in distinct biological contexts such as disease and development. However, computational tools extracting potential TE –proximal gene expression associations from RNA-sequencing data are still missing. Implementation Herein, we developed a novel R package, using a linear regression model, for studying the potential influence of TE species on proximal gene expression from a given RNA-sequencing data set. Our R package, namely TEffectR, makes use of publicly available RepeatMasker TE and Ensembl gene annotations as well as several functions of other R-packages. It calculates total read counts of TEs from sorted and indexed genome aligned BAM files provided by the user, and determines statistically significant relations between TE expression and the transcription of nearby genes under diverse biological conditions. Availability TEffectR is freely available at https://github.com/karakulahg/TEffectR along with a handy tutorial as exemplified by the analysis of RNA-sequencing data including normal and tumour tissue specimens obtained from breast cancer patients.

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