cociter manual

All public documents can be downloaded instantaneously, all dimmed links are available depending on your personal settings. Please contact Power Diagnostix if you do not find what you are looking for. It automatically captures the selected text, its Internet address, its title and date of adding to the database. The program even allows you to assign your own comments and place it to a specified folder. This will be added to Web Data Extractors white paper. We tackle your challenges with an approach based on the shared values of humanity, authenticity and responsibility. Find out what progress you are making towards transition and learn what we have already done for other clients. What can be done about it. Innovation is vitally needed, but can it be green innovation. You want to change, but where do you start? How can you ensure a fruitful dialogue with your communities. How do you communicate about your progressive, sustainable activities? How can you help your partners understand the meaning of your commitments. How do you become credible actors of change? Join us there! In partnership with The Shift, Springtime has published a guide on what a junior can do to motivate their management to take more sustainable action. Request your free copy! Cociter also focuses on following the principles of collaborative management. It’s a safe bet! Will you do the same. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. La-dessus, nous sommes (quasiment) tous d’accord. Notre modele de societe a montre ses limites. Cette crise a mis en lumiere la fragilite de notre systeme economique, notre dependance par rapport au marche globalise, l’absurdite d’une consommation lowcost. En a peine deux mois, l’edifice a vacille.

• cociter manual.

Why Cogitum Co-Citer. How to use Co-Citer. Press Room User Manual Download and Installation Instruction License Agreement How to Get Business License Frequently Asked Questions (FAQ) Troubleshooting Guide Contact Us It automatically captures the selected text, its Internet address, its title and date of adding to the database. The Cogitum Co-Citer screenshot Once a text has been grabbed, you can. Discover everything Scribd has to offer, including books and audiobooks from major publishers. Start Free Trial Cancel anytime. Report this Document Download Now Save Save A a a a a a A For Later 0 ratings 0% found this document useful (0 votes) 1 views 5 pages Manual De Cociter: Cargando el programa Original Title: A a a a a a A Uploaded by Pedro De La Cruz Espinoza Description: Full description Save Save A a a a a a A For Later 0% 0% found this document useful, Mark this document as useful 0% 0% found this document not useful, Mark this document as not useful Embed Share Print Download Now Jump to Page You are on page 1 of 5 Search inside document Browse Books Site Directory Site Language: English Change Language English Change Language. Report this Document Download Now Save Save A a a a a a A For Later 0 ratings 0% found this document useful (0 votes) 0 views 5 pages Manual De Cociter: Cargando el programa Original Title: A a a a a a A Uploaded by Pedro De La Cruz Espinoza Description: Full description Save Save A a a a a a A For Later 0% 0% found this document useful, Mark this document as useful 0% 0% found this document not useful, Mark this document as not useful Embed Share Print Download Now Jump to Page You are on page 1 of 5 Search inside document Browse Books Site Directory Site Language: English Change Language English Change Language. Furthermore we post here software updates, manuals, data sheets and other files for registered customers. Please register to get full access!

Systematic validation by automated literature and data mining provides strong additional support for our predictions. Thus, these predictions serve as a valuable resource that would be useful for the broad biological community. Finally, we have built a user-friendly, interactive web portal to enable users to navigate this mouse cell network atlas. Graphical Abstract In Brief Suo et al.They predict essential regulators for all major cell types in mouse and develop an interactive web portal for query and visualization. INTRODUCTION A multi-cellular organism contains diverse cell types; each has its own functions and morphology. While it is clear the maintenance of cell identity involves the coordinated action of many regulators, transcription factors (TFs) have been long recognized to play a central role. In several cases, the activity of a small number of key TFs, also known as the master regulators, are essential for cell identity maintenance: depletion of these regulators cause significant alteration of cell identity, while forced expression of these regulators can effectively reprogram cells to a different cell type ( Han et al., 2012; Ieda et al., 2010; Riddell et al., 2014; Takahashi and Yamanaka, 2006 ). However, for most cell types, the underlying gene regulatory circuitry is incompletely understood. With the increasing diversity of gene expression programs being identified through single-cell analysis, an urgent need is to understand how these programs are established during development, and to identify the key regulators responsible for such processes. Systematic approaches for mapping gene regulatory networks (GRNs) have been well established. The most direct approach is through genome-wide occupancy analysis, using experimental assays such as chromatin immunoprecipitation sequencing (ChIP-seq), chromatin accessibility, or long-range chromatin interaction assays ( ENCODE Project Consortium, 2012 ).

Et nombreux sont ceux qui se demandentPourtant aucun basculement significatif neEt prenons le temps deL’electricite n’est pas un produit dePour beaucoup d’entre nous,Comme si tous lesEt nous sommes encourages dans ce choix par laEt tant pis pour la provenance reelle des kWh et la qualite de laC’est le Comptoir citoyen des EnergiesSe fournir enUne societe de l’apres Covid. Renouvelables (APERe). Brussels School of Economics and Management (SBS-EM) de l’ULB. Si vous continuez a utiliser ce dernier, nous considererons que vous acceptez l'utilisation des cookies. This document is a user manual for using this package. Please check following document for usage and packages for your own analysis. Here, we take gene expression in normal, inflammation and cancerBIC-SKmeans clustering, to cluster the genes based on the expression. Then BIC-SKmeans clustering outputs the Kggfile. Below gives theEntrez gene IDs, and the second column is GROUP which is the clusterBGnetworkfile contains the background PPI networks formatted asEach row contains two genes of a NPNature methods)Exprfile, which outputs the predicted immune cell contents in differentThe second column is the label ofIf you do not need the immune cell deconvolution results, just skip thisUse following lines to run the script with Python command line prompt. Stored in detector.immuneCytokines. Copyright notice This is an open access article under the CC BY license ( ). The publisher's final edited version of this article is available free at Cell Rep See other articles in PMC that cite the published article. However, for most cell types, the regulatory mechanism underlying their identity remains poorly understood. By computational analysis of the recently published mouse cell atlas data, we have identified 202 regulons whose activities are highly variable across different cell types, and more importantly, predicted a small set of essential regulators for each major cell type in mouse.

Each group contains 20 cells. We applied SCENIC to infer regulons based on the group-averaged gene expression profiles, and the RAS scores were calculated at the single-cell level as in the original study ( Aibar et al., 2017 ). By testing its performance on several representative tissues, we found that our modified approach separated cell types more effectively compared to the original implementation ( Figure S1; see STAR Methods for details). Open in a separate window Figure 1. Mapping Mouse Cell Network Atlas with Regulon Activity (A) Schematic overview of the computational approach in this study. See also Figures S1 and S2 and Tables S1, S2, and S3. We focused on a representative, well-annotated subset of MCA data ( Han et al., 2018 ), containing 61,637 cells sampled from 43 tissues. Previous analysis has identified 98 main cell types ( Han et al., 2018 ). By applying the modified SCENIC approach described above, we identified 202 significant regulons containing 8,461 genes ( Table S1 ). For example, each of four main cell types identified in liver occupies a distinct territory in the t-Distributed Stochastic Neighbor Embedding (tSNE) plot ( Figure 1C ). One question of interest is whether cells of the same type may have different regulatory circuitries across tissues. To address this question, we focused on stromal cells, which can be found in a wide variety of tissues, providing support, structural and anchoring functions. The behavior of stromal cells is well known to be highly plastic, a necessary property for supporting a diverse range of tissue development ( Lee et al., 2006 ). Indeed, we found that the stromal cells from different tissues tend to have distinct regulon activities ( Figure 1D ). While stromal cells are clustered together from the global view of t-SNE map, closer examination suggests the subpopulations from different tissues—such as uterus, mammary gland, bladder, and pancreas—are well separated.

However, this approach is not scalable to a large number of cell types, and its application is often limited by the number of cells that can be obtained in vivo. An alternative, more generalizable approach is to computationally reconstruct GRNs based on single-cell gene expression data ( Fiers et al., 2018 ), followed by more focused experimental validations. In this study, we took this latter approach to build a comprehensive mouse cell network atlas. To this end, we took advantage of the recently mapped mouse cell atlas (MCA) derived from comprehensive single-cell transcriptomic analysis ( Han et al., 2018 ), and combined with a computational algorithm to construct GRNs from single-cell transcriptomic data. Our analysis indicates that most cell types have distinct regulatory network structure and identifies regulators that are critical for cell identity. In addition, we provide an interactive web-based portal for exploring the mouse cell network atlas. RESULTS Reconstructing Gene Regulatory Networks Using the MCA To comprehensively reconstruct the gene regulatory networks for all major cell types, we applied the SCENIC pipeline ( Aibar et al., 2017 ) to analyze the MCA data. In brief, SCENIC links cis -regulatory sequence information together with single-cell RNA sequencing (RNA-seq) data. SCENIC contains three main steps, including co-expression analysis, target gene motif enrichment analysis, and regulon activity evaluation. The main outcomes contain a list of regulons (each representing a TF along with a set of co-expressed and motif significantly enriched target genes), and the regulon activity scores (RAS) for each cell ( Figure 1A ). To improve computational efficiency and robustness, we modified the original pipeline to analyze pooled data instead. Specifically, we divided the entire cell population into small groups with similar cell states. This was achieved by random, non-overlapping sampling from the same tissue and cell type.

When mapping the average activity score of each module onto tSNE map, we found that each module occupies distinct region and all highlighted regions show complementary patterns ( Figure S3C ). Module M1 contains regulators Gata1, Tal1, and Lmo2, which are essential regulators for the erythroblast. Module M5 contains regulators that are specifically activated in testicular cells, such as Sox5 ( Kiselak et al., 2010 ) and Ovol2 ( Chizaki et al., 2012 ). Regulons in M6 are highly associated with the nervous system, such as oligodendrocytes and astrocytes. The activity of M7 including Mafb, Irf2, and Nfkb1 is specifically high in different immune cell types ( Valledor et al., 1998 ), such as macrophages, microglia, dendritic cells, B cells, and T cells. A closer examination of this cell type indicates that the regulon Rorc is specifically activated in this subtype. In contrast, the T cell subtypes that are associated with M7 have distinct regulon activities and reside in other tissues. For example, T cells from clusters 3 and 15 are mainly from mammary gland tissues. In these subtypes, the regulon Batf (contained in M7) has the highest specificity score. Open in a separate window Figure 3. Identification of Combinatorial Regulon Modules (A) Identified regulon modules based on regulon connection specificity index (CSI) matrix, along with representative transcription factors, corresponding binding motifs, and associated cell types. (B) Zoomed-in view of module M7 identifies sub-module structures. (C) Different sub-modules in M7 are associated with distinct immune cell types and regulon activities. See also Figure S3. We next focused on the largest module M7, which contains 48 regulons. This module is strongly associated with immune cell types. This is perhaps not surprising, considering the complexity of the mammalian immune system. Interestingly, each smaller module is specifically associated with distinct immune cell types ( Figure 3C ).

Mapping the MCA Network Using Cell-Type-Specific Regulatory Activity The full MCA dataset contains over 800 cell types ( Table S2 ). At this resolution, many cell types share similar gene expression patterns and their biological functions are likely to be less distinct. Our complete network analysis estimated the RAS and RSS ( Table S3 ) for all these cell types. We found that related cell types share similar overall network structure. The Sankey plot ( Figure 4B ) summarizes the relationship between cell types and their top associated regulon modules. For example, M1, M7, and M8, which are immune cell-type-related regulon modules ( Figure 3A ), are highly enriched in G1, whereas modules M2 and M3 (stromal cell-related modules) ( Figure 3A ) are highly enriched in G2. Open in a separate window Figure 4. A Summary View of the Mouse Cell Network Atlas (A) Relatedness network for the 818 cell types based on similarity of regulon activities. A Web-Based Resource for Interrogating Mouse Cell Network Atlas We created a web-based portal to enable users to easily navigate this predicted mouse cell network atlas ( ). The web interface provides both regulon-centric and cell-type centric views. The regulon-centric view represents the relationship between the 202 regulons; each edge connects a pair of regulons whose cell-type-specific activity scores are highly correlated. Similarly, the cell-type centric view represents the relationship between the cell types; each edge connects a pair of cell types that share similar regulon activity patterns. The user can choose between the 98 major cell types (that are analyzed here) and 818 cell types (from the whole MCA) versions. As an example, cumulus cells are a special cell type in the ovary whose function is not well characterized. To find information about this cell type, a user could simply enter “cumulus cell” in the search box to search for related cell types ( Figure 4C, Box 1).

Similar refined structure can be found in various other cell types, such as T cells and epithelial cells ( Figure S2C ). Taken together, regulon-based activity score in different cell types provides a new avenue to investigate the potential regulatory mechanism in inter- and intra-cell type variations. Our analysis indicates that GRN differences are primarily driven by cell type differences but further modulated by tissue environment differences. Comparative Analysis Identified Essential Regulators for the Maintenance of Cell Identity Our comprehensive network analysis provides an opportunity to systematically identify critical regulators for cell identity. For each regulon, we evaluated its activities associated with each of the 98 major cell types ( Table S3 ), and defined a regulon specificity score (RSS) based on Jensen-Shannon divergence ( Cabili et al., 2011 ) Table S3; see STAR Methods ). We then selected the regulons with highest RSS values and further examined their functional properties. To test whether this approach is effective, we started with the erythroblast because its core gene regulatory network has been well characterized ( Orkin and Zon, 2008 ). Our network analysis identified Lmo2, Gata1, and Tal1 (also known as SCL), as the most specific regulons associated with erythroblast ( Figure 2A ). The x axis represents different datasets, and the y axis represents the co-expression significance of target genes in each dataset. See also Figure S2 and Table S4. The success of our approach in recapitulating critical regulators for well-characterized cell types motivated us to repeat the analysis for all other 96 cell types. To systematically evaluate the accuracy of these predictions, we used two complementary approaches: SEEK ( Zhu et al., 2015 ) and CoCiter ( Qiao et al., 2013 ), based on mining the pubic datasets and literatures, respectively.

First, SEEK analysis was done to select datasets in which the TF and its target genes within a regulon are co-expressed. We queried the titles for public mouse datasets for enrichment of cell-type-specific terms, with the assumption that functionally related genes tend to be co-expressed in the corresponding cell types. Second, CoCiter analysis was done to identify enriched co-occurrence of a gene and cell type term pair in publication abstracts, with the assumption that functionally related genes and terms should frequently appear together in the literature. To test if the above two data-mining approaches are useful validation strategies, we applied each approach to test the regulators identified for erythroblast and B cells. For erythroblast, we applied SEEK analysis to search for GEO datasets in which the genes in regulon Lmo2 are significantly co-expressed. Therefore, we applied both approaches to evaluate the relevance of predicted essential regulators for other cell types, most of which are incompletely characterized. For example, oligodendrocytes are a type of neuroglia whose main functions are to provide support and insulation to axons in the CNS. While various factors have been implicated to play a role in oligodendrocyte development ( Zuchero and Barres, 2013 ), the most important regulators remain unknown. Approximately 15% of lung cells belong to alveolar type II (AT2), which has the important functions of synthesizing and secreting surfactant ( Mason, 2006 ). To systematically characterize the combinatorial patterns, we compared the atlas-wide similarity of RAS scores of every regulon pair based on the Connection Specificity Index (CSI) ( Fuxman Bass et al., 2013 ) (see STAR Methods ). For each module, we identified several representative regulators and cell types through their average activity scores ( Figure S3B ).

Selecting this cell type would generate two main outputs. The first output is a list of regulons ranked by the degree of RSS. The most specific regulons are Foxp1, Arh, and Vdr, although a number of additional regulons, such as Foxo1, have similar specificity ( Figure 4C, Box 3). The second output is a list of cell types with similar regulatory networks. Not surprisingly, its neighboring cell types include other cumulus cell subtypes identified by MCA. Of interest, granulosa cells, which are also called cumulus granulosa cells depending on location within the ovarian follicle, are also identified as its neighbors ( Figure 4C, Box 4). Thus, biomedical investigators can identify putative regulons that are likely important during mouse ovary and oocyte development. The web portal also provides a zoom function, which enables users to interactively explore the regulon and cell-type network structures at any desired resolution. In addition, the raw data are also downloadable from the web portal to support further investigation. DISCUSSION Our knowledge of cellular heterogeneity has exploded in the past few years. In comparison, for most of the cell types identified so far, we lack mechanistic understanding how their characteristic gene expression programs are established and maintained. Neither do we understand the developmental and functional relationship between different cell types. Such information is not just of fundamental biological interest, but also can guide developing novel cell reprogramming strategies with clinical implications. Building upon the recently mapped MCA ( Han et al., 2018 ), we have comprehensively constructed the GRNs for all major cell types in mouse through computational analysis. An important consequence is the predictions of critical regulators for each cell type. While most predictions remain hypotheses, they have provided a guide for future experimental investigation.

As such, we have created a valuable resource for the broad biologist community. Each regulon is then defined as a TF and its direct target genes; (3) the RAS in each single cell is calculated through the area under the recovery curve. The original implementation of SCENIC is not scalable to large datasets and its results can be significantly affected by sequencing depth. To improve the scalability or robustness, we modified its implimentation by pooling data from every 20 cells randomly selected within each cell type and tissue and then applying SCENIC to the average gene expression profile of the pooled data. This simple modification (referred to as Avg20) effectively increases the data quality as well as reduces the computational burden. We compared the performance of our modified approach with the original version of SCENIC by analyzing the MCA data from three representative tissues: bladder, kidney and bone marrow. For performance evaluation, we calculated the Silhouette value, which is a commonly used quantitative metric for clustering consistency. A high silhouette value indicates a high degree of separation among cell types; therefore, it provides a quantitative metric for functional relevance. For each tissue, the Avg20 approach was repeated three times to estimate the variability due to random sampling, and t test was used to evaluate whether the performance of Avg20 approach is better than that of using all single cells. The results are shown in Figure S1. The consistency between three replicates was evaluated by the following approaches. First, the overlap among the TFs of regulons was evaluated by Fisher’s exact test. Second, for each Avg20 replicate, we calculated the pairwise distance of single cells in each tissue based on their RAS and then calculated Pearson correlation coefficient (PCC) to evaluate the agreement of different Avg20 replicates.

Next, we evaluate the Jensen-Shannon Divergence (JSD), which is a commonly used metric for quantifying the difference between two probability distributions, defined as. Finally, the regulon specificity score (RSS) is defined by converting JSD to a similarity score. We used the mouse version of SEEK to evaluate whether the genes in a regulon are co-expressed, and if so whether the datasets supporting the co-expression are associated with an interested cell type. If genes are significantly co-expressed in many datasets related to a certain cell type, it could be inferred that the function of this regulon is highly related to this cell type. Then we choose a p Qiao et al., 2013 ) is a text mining approach against the up-to-date Medical Literature Analysis and Retrieval System Online (MEDLINE) literature database to evaluate the co-citation impact (CI, log-transformed paper count) between a gene list and a term. Here we used the function “gene-term” in CoCiter (use default parameters but set organism as mouse, ) to check whether the genes in a regulon are significantly co-cited with a certain cell type in literatures. Regulon module analysis Regulon modules were identified based on the Connection Specificity Index (CSI) ( Fuxman Bass et al., 2013 ), which is a context-dependent measure for identifying specific associating partners. The evaluation of CSI involves two steps. First, the Pearson correlation coefficient (PCC) of activity scores is evaluated for each pair of regulons. Next, for a fixed pair of regulons, A and B, the corresponding CSI is defined as the fraction of regulons whose PCC with A and B is lower than the PCC between A and B. Hierarchical clustering with Euclidean distance was performed based on CSI matrix to identify different regulon modules. The result was visualized by Cytoscape ( Shannon et al., 2003 ). We used the same strategy to identify submodules within M7.

For each regulon module, its activity score associated with a cell type is defined as the average of the activity scores of its regulon members in all cells within this cell type. Then the top ranked cell types are identified for each module. Quantifying cell type relationship Using the gene regulatory network analysis as a guide, we quantified the relationship between different cell-types based on the similarity of the overall regulon activities, which is quantified by the Spearman correlation coefficient. The results were represented as a network, where a pair of cell types were connected if the Spearman correlation coefficient is greater than 0.8. Again, the result was visualized by using Cytoscape. Groups of related cell-types were identified by using the Markov Clustering Algorithm (MCL) ( van Dongen and Abreu-Goodger, 2012 ), as implemented in the ClusterMaker application in Cytoscape. We used the default setting except setting the inflation parameter as 2. Web service We created an interactive, web-based portal to explore the network atlas in this study (URL: ). This interactive website is constructed with some of latest technologies including JavaScript libraries jQuery 3.3, Bootstrap 4, and Leaflet 1.3. Together these libraries provide efficient client-side search, zooming functions for the large cell type network. The site is hosted on an Apache web server running the Apache Tomcat which provides the necessary back-end support for the web server. Users can zoom-in on a part of network, mouse-over, click on a cell type in the network, and browse information about the associated regulons and other most similar cell types. The website also provides a complete, downloadable list of pairwise regulon-cell type associations. QUANTIFICATION AND STATISTICAL ANALYSIS Details of the statistical tests used in this study are described briefly in the main text and more in-depth in the subsections above.

They are also summarized below:It randomly selected 1000 gene sets with the same size of tested regulon and the p value was calculated as the number of times that co-citation impact of “random” larger than “true” divided by 1000. ADDITIONAL RESOURCES We created an interactive, web-based portal for community to explore the network atlas in this study. URL. This research was supported by a Claudia Barr Award and NIH (R01HL119099 to G.-C.Y.). Footnotes SUPPLEMENTAL INFORMATION Supplemental Information includes four figures and four tables and can be found with this article online at. DECLARATION OF INTERESTS The authors declare no competing interests. The in vivo profile of transcription factors during neutrophil differentiation in human bone marrow. Blood. Open source clustering software. Bioinformatics. An integrated encyclopedia of DNA elements in the human genome. Nature. Mapping gene regulatory networks from single-cell omics data. Brief. Funct. Genomics. Cell type transcriptome atlas for the planarian Schmidtea mediterranea. Science. Using networks to measure similarity between genes: association index selection. Nat. Methods. Oncotarget. Cell Stem Cell. Direct reprogramming of fibroblasts into functional cardiomyocytes by defined factors. Cell. Biology of alveolar type II cells. Respirology. Hematopoiesis: an evolving paradigm for stem cell biology. Cell. Fine-tuning of FOXO3A in cHL as a survival mechanism and a hallmark of abortive plasma cell differentiation. Blood. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science. CoCiter: an efficient tool to infer gene function by assessing the significance of literature co-citation. PLoS ONE. The human cell atlas. eLife. Reprogramming committed murine blood cells to induced hematopoietic stem cells with defined factors. Cell. Cytoscape: a software environment for integrated models of biomolecular interaction networks.