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opencc-by-nc-sa-4.0Jun 2023View details →

Data archive: CICT for single cell RNA-seq network inference

This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions" (Shojaee and Huang, Brief in Bioinform 2023. DOI: 10.1093/bib/bbad370). The CICT code is available at the GitHub repo (https://github.com/hlab1/scRNAseqWithCICT/).The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021), SCENIC (Aibar et al., Nat Methods 2017), Inferelator 3.0 (Gibbs et al., Bioinformatics 2022), and CellOracle (Kamimoto et al., Nature 2023). The output directory names are (subdirectories within each dataset):* CICT_ewMIshrink_RFmaxdepth10_RFntrees20/: CICT for simulated data * CICT_v2/: CICT for experimental data * CELLORACLEDB/: CellOracle for experimental data * DEEPDRIM72_ewMIshrink_RFmaxdepth10_RFntrees20/: DEEPDRIM for simulated data * DEEPDRIM72_v2/: DEEPDRIM for experimental data * INFERELATOR38_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-Prior for simulated data * INFERELATOR38_v2/: Inferelator-Prior for experimental data * INFERELATOR34_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-NoPrior for experimental data * INFERELATOR34_v2/: Inferelator-NoPrior for experimental data * GENIE3/: GENIE3 * GRNBOOST2/: GRNBOST2 * LEAP/: LEAP * PIDC/: PIDC * PPCOR/: PPCOR * SCENICDB/: SCENIC for experimental data * SCNS/: SCNS * SCODE/: SCODE * SCRIBE/: SCRIBE * SINCERITIES/: SINCERITIES * SINGE/: SINGE * RANDOM/: RANDOMThe methods were benchmarked against two kinds of scRNA-seq datasets: * Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts with shape parameter k=6.5 and rate parameter q=10, 30, 50, 70, 80.  * Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, with three types of ground truth networks.     * Evaluation levels:         * L0: 500 highly varying genes plus TFs         * L1: 1000 highly varying genes plus TFs         * L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1.     * Types of ground truths:         * Cell-type-specific ChIP-seq ground truth (L0, L1, L2)         * Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns)         * Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof)The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For complete details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).

opencc-zeroJun 2024View details →

scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data

This repository contains the training data and source code to reproduce the results of our paper: scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).

opencc-by-3.0-usJul 2021View details →

ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)

We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.

opencc-by-4.0Sep 2023View details →

Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression

Supplemental Data 1 is single-cell response to rapamycin count data first sequenced in this work and deposited in GEO with accession GSE242556. It is a 173348 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Gene', 'Replicate', 'Pool', and 'Experiment') are cell-specific metadata. Supplemental Data 2 is bulk response to rapamycin count data first sequenced in this work. It is a 33 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Oligo', 'Time', 'Replicate', and 'Sample_barcode') are sample-specific metadata. Supplemental Data 3 is single-cell count data published as GSE125162 and re-analyzed with the pipeline used for single-cell quantification in this work. It is a 65068 rows × 5850 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 7 columns ('Condition', 'Sample', 'Genotype_Group', 'Genotype_Individual', 'Genotype', 'Replicate', 'Cell_Barcode') are cell-specific metadata. Supplemental Data 4 is the four deep learning models trained in this work. It is a TAR.GZ file containing the final biophysical transcription/decay model, the pre-trained decay model, the velocity prediction model, and the count prediction model. Each model file is an h5 file containing a pytorch model that can be loaded with supirfactor\_dynamical.read(). Supplemental Data 5 is the prior knowledge network used to constrain the models for TF interpretability. It is a 1574 rows × 204 columns [Genes x TFs] TSV.GZ file where the first row is a header with TF names, the first column is an index of gene names, and TF-gene interactions are indicated by non-zero values in the matrix. There are 2799 TF-gene interactions. Supplemental Table 6 is the oligonucleotide sequences used in this work. It is a TSV file with a header row. Supplemental Table 7 is the yeast strains used in this work. It is a TSV file with a header row. Supplemental Table 8 is gene metadata used in this work (e.g. Ribosomal Protein gene labels, etc). It is a TSV file with a header row. Supplemental Table 9 is FY4/5 growth curve data generated in this work. It is a 20 rows × 7 columns TSV file where the first row is a header with replicate IDs, the first column is an index of times in minutes, and values are cell densities in YPD culture, in units of 10$^6$ cells / mL. Supplemental Data 10 is a TAR.GZ file containing the yeast SacCer3 genome, modified to add UTR sequences, that was used to generate transcripts for kallisto pseudoalignment in this work.

dryad40/100

Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells

Skin-resident CD8+ T cells comprise distinct IFN-γ- (TRM1) and IL-17-producing (TRM17) subsets that differentially contribute to immune responses. However, whether these populations employ common mechanisms to establish tissue residence is unknown. Here, we show that TRM1 and TRM17 cells navigate divergent trajectories to acquire tissue residency in skin. While TRM1 cells depend on a T-bet-Hobit-IL-15 axis, TRM17 cells develop independently of these factors. Instead, c-Maf commands a tissue-resident program in TRM17 cells parallel to that induced by Hobit in TRM1 cells, with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment. Accordingly, targeting this pathway enables ablation of skin TRM17 cells without compromising their TRM1 counterparts. Thus, skin-resident T cells rely on distinct molecular circuitries, which can be exploited to strategically modulate local immunity.

opencc-zeroOct 2023View details →

opencc-by-4.0Nov 2021View details →

Classification of blood cells dynamics with convolutional and recurrent neural networks: a sickle cell disease case study

The fraction of red blood cells (RBC) adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease (SCD). Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples(out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. These videos are of different durations (from 6 to more than 100 frames). This dataset contains four adult patients with SCD. They were enrolled in the study Drepaforme (approved by the institutional review board CPP Ouest 6 under the reference n°2018A00679-46) and were sampled weekly for several months. The movies were processed using in-house routines in Matlab (Matlab, R2016a) and RBC were detected individually and tracked over time. The database provided in this repository are already pre-processed sequences of tracked and centered RBC over time, each time step image being normalized to 31x31 pixels. Within the 32 experiments, the total number of sequences (or samples) is nearly 150 000. All sequences were semi-automatically labelled into 3 classes, depending on the dynamic of the cell: tank-treading, flipping and unreliable (140 000 are unreliable). The percentage of tank-treading cells with respect to all reliable cells (tank-treading+flipping)  in every experiment is the final goal of this study. This dataset is very interesting to the community as it is a large database for cell dynamics classification: the class depends on the movement of the cell. An automatic processing of the database using a 2-stage deep learning model is available here https://github.com/icannos/redbloodcells_disease_classification For opening the data in python:   from scipy.io import loadmat   x=loadmat('BG20191003shear10s01_Export.mat')   * x['Norm_Tab'] is of size nb_samples x max_len_sequences x 31 x 31, where max_len_sequences is the length of the longest sequence of the series, typically ~150 to 180. The other sequences are padded with 31x31 zero matrices at the end in order to fill this maximal length.   * x['Labels_Num'] is the corresponding label of each sequence, of size nb_samples. Label can be:         - 0 : "tank-treading" (or healthy)         - 1 : "flipping" (or tumbling, i.e. related to a SCD)         - 2 : "unreliable"

opencc-by-4.0Jan 2022View details →

# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.

opencc-by-4.0Jan 2022View details →

Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).

opencc-by-4.0Mar 2022View details →

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease

This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.

opencc-by-4.0Aug 2023View details →

Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks

This repository contains training, validation, and testing of fluorescence image data sets with their label for spheroid cell death classification.  These data are intended to be used in the paper "Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks" currently submitted

dryad40/100

Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells

Open the record for dataset details and reuse information.

publicOct 2023View details →

opencc-by-4.0Nov 2020View details →

Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network

The use of models of stem cell differentiation to trophoblastic cells provides an effective perspective for understanding the early molecular events in the establishment and maintenance of human pregnancy. In combination with the newly developed deep learning technology, the automated identification of this process can greatly accelerate the contribution to relevant knowledge. Based on the transfer learning technique, we used a convolutional neural network to distinguish the microscopic images of Embryonic stem cells (ESCs) from differentiated trophoblasts -like cells (TBL). To tackle the problem of insufficient training data, the strategies of data augmentation were used. The results showed that the convolutional neural network could successfully recognize trophoblast cells and stem cells automatically, but could not distinguish TBL from the immortalized trophoblast cell lines in vitro (JEG-3 and HTR8-SVneo). We compare the recognition effect of the commonly used convolutional neural network, including DenseNet, VGG16, VGG19, InceptionV3, and Xception. This study extends the deep learning technique to trophoblast cell phenotype classification and paves the way for automatic bright-field microscopic image analysis of trophoblast cells in the future.

opencc-by-4.0Mar 2024View details →

Rapid and Accurate Identification of Stem Cell Differentiation Stages via SERS and Convolutional Neural Networks

<a name="OLE_LINK2"></a>Monitoring the transition of cell states during induced pluripotent stem cell (iPSC) differentiation is crucial for clinical medicine and basic research. However, both identification category and prediction accuracy need further improvement. Here, we propose a method combining Surface-Enhanced Raman spectroscopy (SERS) with convolutional neural networks (CNN) to precisely identify and distinguish cell states during stem cell differentiation. First, mitochondria-targeted probes were synthesized by combining AuNRs and mitochondrial localization signal (MLS) peptides to obtain effective and stable SERS spectra signals at various stages of cell differentiation. Then, the SERS spectra served as input datasets, and their distinctive features were learned and distinguished by CNN. As a result, rapid and accurate identification of six different cell states, including the embryoid body (EB) stage, was successfully achieved throughout the stem cell differentiation process with an impressive prediction accuracy of 98.5%. Furthermore, the impact of different spectral feature peaks on the identification results was investigated, which provides a valuable reference for selecting appropriate spectral bands to identify cell states. This is also beneficial for shortening the spectral acquisition region to enhance spectral acquisition speed. These results suggest the potential for SERS-CNN models in quality monitoring of stem cells, advancing the practical applications of stem cells.

dryad36/100

KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cells

Mechanobiology requires precise quantitative spatial information on processes taking place in specific 3D microenvironments. Connecting microscopical, molecular, biochemical and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps is particular challenging for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. Here we present raw data and processed segmented data of fluorescence-tagged keratin intermediate filaments from MDCK, HaCaT and RPE epithelial cells. The 30 datasets contain unprocessed airyscan superresolution 3D image data and numerical network data of the 3D arrangement of the keratin intermediate filament cytoskeleton. Information is provided on network organization at the subcellular level, including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature and orientation. The datasets contain data in exchange format for 3d software packages. Comparison of the resulting network parameters helps to identify similarities and differences of keratin network organization in epithelial cell types. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

opencc-zeroDec 2021View details →

opencc-by-4.0Mar 2022View details →

TestDataset for Oneat networks for detection and prediction of dividing Hela cells for different imaging modalities

Here we present the tif files to be used to test the Oneat networks for detection and prediction of division events for Hela cells imaged under different imaging modalities and presented originally here: https://zenodo.org/record/6139958#.Yjcjl3rMJD8

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A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation

B-cell activation and immune synapse (IS) formation with membrane-bound antigens are actin-dependent processes that scale positively with the strength of antigen-induced signals. Importantly, ligating the B-cell integrin, LFA-1, with ICAM-1 promotes IS formation when antigen is limiting. Whether the actin cytoskeleton plays a specific role in integrin-dependent IS formation is unknown. Here we show using super-resolution imaging of mouse primary B cells that LFA-1: ICAM-1 interactions promote the formation of an actomyosin network that dominates the B-cell IS. This network is created by the formin mDia1, organized into concentric, contractile arcs by myosin 2A, and flows inward at the same rate as B-cell receptor (BCR): antigen clusters. Consistently, individual BCR microclusters are swept inward by individual actomyosin arcs. Under conditions where integrin is required for synapse formation, inhibiting myosin impairs synapse formation, as evidenced by reduced antigen centralization, diminished BCR signaling, and defective signaling protein distribution at the synapse. Together, these results argue that a contractile actomyosin arc network plays a key role in the mechanism by which LFA-1 co-stimulation promotes B-cell activation and IS formation.

opencc-zeroMay 2022View details →

dryad36/100

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants underlying the range of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Examination of informative model features reveals novel biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

opencc-zeroSep 2022View details →