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LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm.DICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 ORL IMAGES (EXAMPLES.ORL_IMAGES) Parameters: V ( numpy.matrix) – The ORL faces data matrix. nimfa.examples.orl_images.read () ¶. Read face image data from the ORL database. The matrix’s shape is 2576 (pixels) x 400 (faces). Step through each subject and each image. Reduce the size of theimages
WELCOME TO NIMFA
Welcome to Nimfa¶. Nimfa is a Python library for nonnegative matrix factorization. It includes implementations of several factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. BMF (METHODS.FACTORIZATION.BMF) Bmf (. methods.factorization.bmf. ) ¶. Binary Matrix Factorization (BMF) . BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of the target matrix. Common methodologies include penalty NMF (METHODS.FACTORIZATION.NMF) Nmf (methods.factorization.nmf)¶Standard Nonnegative Matrix Factorization (NMF), . Based on Kullback-Leibler divergence, it uses simple multiplicative updates , , enhanced to avoid numerical underflow .Based on Euclidean distance, it uses simple multiplicative updates .Different objective functions can be used, namely Euclidean distance, divergence or connectivity matrix convergence. NMF_STD (MODELS.NMF_STD) Nmf_std (models.nmf_std)¶class nimfa.models.nmf_std.Nmf_std(params)¶. Bases: nimfa.models.nmf.Nmf Implementation of the standard model to manage factorizations that follow standard NMF model. It is the underlying model of matrix factorization and provides a general structure of standard NMF model. NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, one NMF_NS (MODELS.NMF_NS) Nmf_ns (models.nmf_ns)¶class nimfa.models.nmf_ns.Nmf_ns(params)¶. Bases: nimfa.models.nmf.Nmf Implementation of the alternative model to manage factorizations that follow nonstandard NMF model. This modification is required by the Nonsmooth NMF algorithm (NSNMF) .The Nonsmooth NMF algorithm is a modification of the standard divergence based NMF methods. SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm.DICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 ORL IMAGES (EXAMPLES.ORL_IMAGES) Parameters: V ( numpy.matrix) – The ORL faces data matrix. nimfa.examples.orl_images.read () ¶. Read face image data from the ORL database. The matrix’s shape is 2576 (pixels) x 400 (faces). Step through each subject and each image. Reduce the size of theimages
WELCOME TO NIMFA
Welcome to Nimfa¶. Nimfa is a Python library for nonnegative matrix factorization. It includes implementations of several factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. BMF (METHODS.FACTORIZATION.BMF) Bmf (. methods.factorization.bmf. ) ¶. Binary Matrix Factorization (BMF) . BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of the target matrix. Common methodologies include penalty NMF (METHODS.FACTORIZATION.NMF) Nmf (methods.factorization.nmf)¶Standard Nonnegative Matrix Factorization (NMF), . Based on Kullback-Leibler divergence, it uses simple multiplicative updates , , enhanced to avoid numerical underflow .Based on Euclidean distance, it uses simple multiplicative updates .Different objective functions can be used, namely Euclidean distance, divergence or connectivity matrix convergence. NMF_STD (MODELS.NMF_STD) Nmf_std (models.nmf_std)¶class nimfa.models.nmf_std.Nmf_std(params)¶. Bases: nimfa.models.nmf.Nmf Implementation of the standard model to manage factorizations that follow standard NMF model. It is the underlying model of matrix factorization and provides a general structure of standard NMF model. NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, one NMF_NS (MODELS.NMF_NS) Nmf_ns (models.nmf_ns)¶class nimfa.models.nmf_ns.Nmf_ns(params)¶. Bases: nimfa.models.nmf.Nmf Implementation of the alternative model to manage factorizations that follow nonstandard NMF model. This modification is required by the Nonsmooth NMF algorithm (NSNMF) .The Nonsmooth NMF algorithm is a modification of the standard divergence based NMF methods.DICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 NMF_STD (MODELS.NMF_STD) Nmf_std (models.nmf_std)¶class nimfa.models.nmf_std.Nmf_std(params)¶. Bases: nimfa.models.nmf.Nmf Implementation of the standard model to manage factorizations that follow standard NMF model. It is the underlying model of matrix factorization and provides a general structure of standard NMF model.NMF (MODELS.NMF)
Nmf (models.nmf)¶class nimfa.models.nmf.Nmf(params)¶. Bases: object This class defines a common interface / model to handle NMF models in a generic way. It contains definitions of the minimum set of generic methods that are used in common computations and matrixfactorizations.
NSNMF (METHODS.FACTORIZATION.NSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. DOCUMENTS (EXAMPLES.DOCUMENTS) autolysis of bacillus subtilis by glucose depletion . in cultures in minimal medium, rapid lysis of cells of bacillus subtilis was observed as soon as the carbon source, e.g. glucose, had been completely consumed . the cells died and ultraviolet-absorbing material was excreted in the medium . the results suggest that the cells lyse because of the presence of autolytic enzymes . in the presenceMODELS (MODELS)
Models (. models. ) ¶. This package contains factorization models used in the library nimfa. Specifically, it contains the following: Generic factorization model for handling common computations and assessing quality and performance measures in NMF. Generic factorization model for handling standard MF. NMF_NS (MODELS.NMF_NS) Nmf_ns (models.nmf_ns)¶class nimfa.models.nmf_ns.Nmf_ns(params)¶. Bases: nimfa.models.nmf.Nmf Implementation of the alternative model to manage factorizations that follow nonstandard NMF model. This modification is required by the Nonsmooth NMF algorithm (NSNMF) .The Nonsmooth NMF algorithm is a modification of the standard divergence based NMF methods. MF_FIT (MODELS.MF_FIT) Mf_fit (models.mf_fit)¶class nimfa.models.mf_fit.Mf_fit(fit)¶. Base class for storing MF results. It contains generic functions and structure for handling the results of MF algorithms. It contains a slot with the fitted MF model and data about parameters and methods used for factorization. MF_TRACK (MODELS.MF_TRACK) track_factor(run, **track_model)¶. Add matrix factorization factors (and method specific model data) after one factorization run. ALL AML LEUKEMIA (EXAMPLES.AML_ALL) This example is inspired by .In authors applied NMF to the leukemia data set. With rank, rank = 2, NMF recovered the AML-ALL biological distinction with high accuracy and robustness. Higher ranks revealed further partitioning of the samples. SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning DICTYEXPRESSDOCUMENTATIONDATARUN Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. SCORANGE – DOWNLOAD SINGLE CELL ANALYSIS Download Orange add-on. Orange3-SingleCell can be installed directly from Orange add-on manager. You can find add-on manager in Optionsmenu. Anaconda
NNDSVD (METHODS.SEEDING.NNDSVD) Parameters: V (scipy.sparse of format csr, csc, coo, bsr, dok, lil, dia or numpy.matrix) – Target matrix, the matrix for MF method to estimate.Data instances to be clustered. rank (int) – Factorization rank.; options (dict) – . Specify the algorithm and model specific options (e.g. initialization of extra matrix factor, seedingparameters).
SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS General Preprocessing Loads the data, annotates it and filters it. The Single Cell Preprocesses widget normalises and log-transforms the samples, additionally it can be used to select the most variable genes or to standardize them. SCORANGE – KEGG PATHWAYS SINGLE CELL ANALYSIS KEGG Pathways. Diagrams of molecular interactions, reactions, and relations. Inputs - Data: Data set. - Reference: Referential data set. Outputs - Selected Data: Data BMF (METHODS.FACTORIZATION.BMF) Bmf (methods.factorization.bmf)¶Binary Matrix Factorization (BMF). BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of thetarget matrix.
DATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecularDOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated. SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning DICTYEXPRESSDOCUMENTATIONDATARUN Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. SCORANGE – DOWNLOAD SINGLE CELL ANALYSIS Download Orange add-on. Orange3-SingleCell can be installed directly from Orange add-on manager. You can find add-on manager in Optionsmenu. Anaconda
NNDSVD (METHODS.SEEDING.NNDSVD) Parameters: V (scipy.sparse of format csr, csc, coo, bsr, dok, lil, dia or numpy.matrix) – Target matrix, the matrix for MF method to estimate.Data instances to be clustered. rank (int) – Factorization rank.; options (dict) – . Specify the algorithm and model specific options (e.g. initialization of extra matrix factor, seedingparameters).
SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS General Preprocessing Loads the data, annotates it and filters it. The Single Cell Preprocesses widget normalises and log-transforms the samples, additionally it can be used to select the most variable genes or to standardize them. SCORANGE – KEGG PATHWAYS SINGLE CELL ANALYSIS KEGG Pathways. Diagrams of molecular interactions, reactions, and relations. Inputs - Data: Data set. - Reference: Referential data set. Outputs - Selected Data: Data BMF (METHODS.FACTORIZATION.BMF) Bmf (methods.factorization.bmf)¶Binary Matrix Factorization (BMF). BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of thetarget matrix.
DATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecularDOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated. ICLIPRO: READ OVERLAP TESTING Overview and how to cite¶. iCLIPro is a Python package that can be used to control for systematic misassignments in iCLIP data. If you use iCLIPro in your research, please cite this paper (submitted forSNPSYN.BIOLAB.SI
What is SNPsyn? SNPsyn is an exploratory gene interaction analytics application. It helps you discover and quantify the synergy of SNP-SNP interactions in case SCORANGE – DISCOVER NEW MARKER GENES THAT DISTINGUISH CELL In scOrange, we can find a cluster of cells and then characterize it with a set of differentially expressed genes. If there is a cell type that is prevailing in our cluster, this technique can be used to find candidates for gene markers. SCORANGE – BLOG SINGLE CELL ANALYSIS By: Iva Černoša, Aug 19, 2019. Automatic Embedding of New Cells Onto an Existing Landscape. Learn how to quickly embed new cells onto an existing tSNE projection using the new widget the Annotator on the dataset of a healthy individual and AML patient undergoing chemotherapy gathered SCORANGE – LICENSE SINGLE CELL ANALYSIS Orange Data Mining Toolbox. Orange is a comprehensive, component-based software suite for machine learning and data mining, developed at Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia, together with open sourcecommunity.
SCORANGE – EMBEDDING NEW CELLS ONTO AN EXISTING T-SNE Note: This blog is in many ways a continuation of our previous blog Identification of Cell Populations in Healthy Bone Marrow , therefore it is advisable to read it before tackling this one. Batch effects can be a real nuisance when producing t-SNE plots of multiple data sets. In today’s blog we will show how to bypass them in scOrange by projecting single cell data set from an AML patient SCORANGE – EXPLORING THE FRUIT FLY OLFACTORY SYSTEM SINGLE Single-cell RNA sequencing is all about exploring cell subpopulations and their distinguishing properties. In this blog, you will learn how to automatically select subset of cells based on different criteria, including generating own scores.DOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated.DOWNLOAD.BIOLAB.SI
Load data on Iris ("iris.tab") from preloaded documentation datasets. Any change in selection of the tree node changes the rendering in thescatter plot.
NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm.DICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
SCORANGE – DOWNLOAD SINGLE CELL ANALYSIS Anaconda. If you are using python provided by Anaconda distribution, you are almost ready to go. Add conda-forge to the list of channels you can install packages from. conda config --add channels conda-forge. and run. conda install orange3-singlecell. SCORANGE – KEGG PATHWAYS SINGLE CELL ANALYSIS KEGG Pathways widget displays diagrams of molecular interactions, reactions and relations from the KEGG Pathways Database. It takes data on gene expression as an input, matches the genes to the biological processes and displays a list of corresponding pathways. To explore the pathway, the user can click on any process from the list orarrange
BMF (METHODS.FACTORIZATION.BMF) Bmf (. methods.factorization.bmf. ) ¶. Binary Matrix Factorization (BMF) . BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of the target matrix. Common methodologies include penalty NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, oneDATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecularDOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated. SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm.DICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
SCORANGE – DOWNLOAD SINGLE CELL ANALYSIS Anaconda. If you are using python provided by Anaconda distribution, you are almost ready to go. Add conda-forge to the list of channels you can install packages from. conda config --add channels conda-forge. and run. conda install orange3-singlecell. SCORANGE – KEGG PATHWAYS SINGLE CELL ANALYSIS KEGG Pathways widget displays diagrams of molecular interactions, reactions and relations from the KEGG Pathways Database. It takes data on gene expression as an input, matches the genes to the biological processes and displays a list of corresponding pathways. To explore the pathway, the user can click on any process from the list orarrange
BMF (METHODS.FACTORIZATION.BMF) Bmf (. methods.factorization.bmf. ) ¶. Binary Matrix Factorization (BMF) . BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of the target matrix. Common methodologies include penalty NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, oneDATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecularDOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated. ICLIPRO: READ OVERLAP TESTING Overview and how to cite¶. iCLIPro is a Python package that can be used to control for systematic misassignments in iCLIP data. If you use iCLIPro in your research, please cite this paper (submitted for SCORANGE – DISCOVER NEW MARKER GENES THAT DISTINGUISH CELL Discover New Marker Genes that Distinguish Cell Types. In scOrange, we can find a cluster of cells and then characterize it with a set of differentially expressed genes. If there is a cell type that is prevailing in our cluster, this technique can be used to find candidates for gene markers. In other words, we can consider a dataset, score
SCORANGE – BLOG SINGLE CELL ANALYSIS We analyse cell population changes in the course of chemotherapy by recreating a part of a study from Galen et al. (Cell, 2019) and projecting single cell data set from an AML patient undergoing treatment onto a t-SNE of a healthy individual. Categories: ApplyDomain tSNE
SCORANGE – LICENSE SINGLE CELL ANALYSIS License. Orange is a comprehensive, component-based software suite for machine learning and data mining, developed at Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia, together with open source community. Orange is free software; you can redistribute it and/or modify it under theterms of
WELCOME TO NIMFA
Welcome to Nimfa¶. Nimfa is a Python library for nonnegative matrix factorization. It includes implementations of several factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. SCORANGE – EMBEDDING NEW CELLS ONTO AN EXISTING T-SNE This is our projection of the single cell data from the AML patient before the chemotherapy onto the t-SNE of the cells from a healthy person. The projected cells are displayed as grey crosses. By using the dataset sampled on 15 th day after the patient has first undergone chemotherapy, we can explore how the therapy affects cell populations. SCORANGE – EXPLORING THE FRUIT FLY OLFACTORY SYSTEM SINGLE The data set comes with scOrange via Single Cell Datasets widget. The data set contains more than 1800 cells labelled with different drivers, that mark different neuronal subtypes, as seen in the Distributions widget. Here, we will focus on cells that are driven by GH146-GAL4, marking olfactory neurons - the neurons related to thesense of smell.
DOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated.DOWNLOAD.BIOLAB.SI
Load data on Iris ("iris.tab") from preloaded documentation datasets. Any change in selection of the tree node changes the rendering in thescatter plot.
NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
SNPSYN.BIOLAB.SI
What is SNPsyn? SNPsyn is an exploratory gene interaction analytics application. It helps you discover and quantify the synergy of SNP-SNP interactions in caseDICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90WELCOME TO NIMFA
Welcome to Nimfa. ¶. Nimfa is a Python library for nonnegative matrix factorization. It includes implementations of several factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. Nimfa is distributed under the BSD license. The sample script using Nimfa on LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
ORL IMAGES (EXAMPLES.ORL_IMAGES) Parameters: V ( numpy.matrix) – The ORL faces data matrix. nimfa.examples.orl_images.read () ¶. Read face image data from the ORL database. The matrix’s shape is 2576 (pixels) x 400 (faces). Step through each subject and each image. Reduce the size of theimages
DATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecular NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, one NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
SNPSYN.BIOLAB.SI
What is SNPsyn? SNPsyn is an exploratory gene interaction analytics application. It helps you discover and quantify the synergy of SNP-SNP interactions in caseDICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90WELCOME TO NIMFA
Welcome to Nimfa. ¶. Nimfa is a Python library for nonnegative matrix factorization. It includes implementations of several factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. Nimfa is distributed under the BSD license. The sample script using Nimfa on LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
ORL IMAGES (EXAMPLES.ORL_IMAGES) Parameters: V ( numpy.matrix) – The ORL faces data matrix. nimfa.examples.orl_images.read () ¶. Read face image data from the ORL database. The matrix’s shape is 2576 (pixels) x 400 (faces). Step through each subject and each image. Reduce the size of theimages
DATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecular NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, one NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
SNPSYN.BIOLAB.SI
What is SNPsyn? SNPsyn is an exploratory gene interaction analytics application. It helps you discover and quantify the synergy of SNP-SNP interactions in case SCORANGE – DOWNLOAD SINGLE CELL ANALYSIS Anaconda. If you are using python provided by Anaconda distribution, you are almost ready to go. Add conda-forge to the list of channels you can install packages from. conda config --add channels conda-forge. and run. conda install orange3-singlecell. NSNMF (METHODS.FACTORIZATION.NSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. BMF (METHODS.FACTORIZATION.BMF) Bmf (. methods.factorization.bmf. ) ¶. Binary Matrix Factorization (BMF) . BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of the target matrix. Common methodologies include penalty SCORANGE – LICENSE SINGLE CELL ANALYSIS License. Orange is a comprehensive, component-based software suite for machine learning and data mining, developed at Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia, together with open source community. Orange is free software; you can redistribute it and/or modify it under theterms of
SCORANGE – SCREENSHOTS SINGLE CELL ANALYSIS Screenshots. Landscape of cells in t-SNE plot. Analysis of cluster cell type. Cells with high expression of marker genes. K-means clustering and silhouette scoring. Gene scoring and selection. Cross-validation of cell type prediction. GO enrichment of candidatemarker genes.
NMF (MODELS.NMF)
Nmf (models.nmf)¶class nimfa.models.nmf.Nmf(params)¶. Bases: object This class defines a common interface / model to handle NMF models in a generic way. It contains definitions of the minimum set of generic methods that are used in common computations and matrixfactorizations.
SEPNMF (METHODS.FACTORIZATION.SEPNMF) Parameters: V (Instance of the scipy.sparse sparse matrices types, numpy.ndarray, numpy.matrix or tuple of instances of the latter classes.) – The target matrix to estimate. rank (int) – The factorization rank to achieve.Default is 30. n_run (int) – It specifies the number of runs of the algorithm.Default is 1. If multiple runs are performed, fitted factorization model with thelowest
ALL AML LEUKEMIA (EXAMPLES.AML_ALL) This example is inspired by .In authors applied NMF to the leukemia data set. With rank, rank = 2, NMF recovered the AML-ALL biological distinction with high accuracy and robustness. Higher ranks revealed further partitioning of the samples. SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
DICTYEXPRESSDOCUMENTATIONDATARUN Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90SNPSYN.BIOLAB.SI
What is SNPsyn? SNPsyn is an exploratory gene interaction analytics application. It helps you discover and quantify the synergy of SNP-SNP interactions in caseWELCOME TO NIMFA
Welcome to Nimfa. ¶. Nimfa is a Python library for nonnegative matrix factorization. It includes implementations of several factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. Nimfa is distributed under the BSD license. The sample script using Nimfa on LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
ORL IMAGES (EXAMPLES.ORL_IMAGES) Parameters: V ( numpy.matrix) – The ORL faces data matrix. nimfa.examples.orl_images.read () ¶. Read face image data from the ORL database. The matrix’s shape is 2576 (pixels) x 400 (faces). Step through each subject and each image. Reduce the size of theimages
DATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecular NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, one NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
DICTYEXPRESSDOCUMENTATIONDATARUN Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90SNPSYN.BIOLAB.SI
What is SNPsyn? SNPsyn is an exploratory gene interaction analytics application. It helps you discover and quantify the synergy of SNP-SNP interactions in caseWELCOME TO NIMFA
Welcome to Nimfa. ¶. Nimfa is a Python library for nonnegative matrix factorization. It includes implementations of several factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported. Nimfa is distributed under the BSD license. The sample script using Nimfa on LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm. SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
ORL IMAGES (EXAMPLES.ORL_IMAGES) Parameters: V ( numpy.matrix) – The ORL faces data matrix. nimfa.examples.orl_images.read () ¶. Read face image data from the ORL database. The matrix’s shape is 2576 (pixels) x 400 (faces). Step through each subject and each image. Reduce the size of theimages
DATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecular NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, one NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si LoadingSNPSYN.BIOLAB.SI
What is SNPsyn? SNPsyn is an exploratory gene interaction analytics application. It helps you discover and quantify the synergy of SNP-SNP interactions in case SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
SCORANGE – DOWNLOAD SINGLE CELL ANALYSIS Anaconda. If you are using python provided by Anaconda distribution, you are almost ready to go. Add conda-forge to the list of channels you can install packages from. conda config --add channels conda-forge. and run. conda install orange3-singlecell. BMF (METHODS.FACTORIZATION.BMF) Bmf (methods.factorization.bmf)¶Binary Matrix Factorization (BMF). BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of thetarget matrix.
SEPNMF (METHODS.FACTORIZATION.SEPNMF) Parameters: V (Instance of the scipy.sparse sparse matrices types, numpy.ndarray, numpy.matrix or tuple of instances of the latter classes.) – The target matrix to estimate. rank (int) – The factorization rank to achieve.Default is 30. n_run (int) – It specifies the number of runs of the algorithm.Default is 1. If multiple runs are performed, fitted factorization model with thelowest
NMF (MODELS.NMF)
Nmf (models.nmf)¶class nimfa.models.nmf.Nmf(params)¶. Bases: object This class defines a common interface / model to handle NMF models in a generic way. It contains definitions of the minimum set of generic methods that are used in common computations and matrixfactorizations.
SCORANGE – LICENSE SINGLE CELL ANALYSIS License. Orange is a comprehensive, component-based software suite for machine learning and data mining, developed at Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia, together with open source community. Orange is free software; you can redistribute it and/or modify it under theterms of
ALL AML LEUKEMIA (EXAMPLES.AML_ALL) This example is inspired by .In authors applied NMF to the leukemia data set. With rank, rank = 2, NMF recovered the AML-ALL biological distinction with high accuracy and robustness. Higher ranks revealed further partitioning of the samples. MF_FIT (MODELS.MF_FIT) Mf_fit (models.mf_fit)¶class nimfa.models.mf_fit.Mf_fit(fit)¶. Base class for storing MF results. It contains generic functions and structure for handling the results of MF algorithms. It contains a slot with the fitted MF model and data about parameters and methods used for factorization. NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm.DICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
BMF (METHODS.FACTORIZATION.BMF) Bmf (. methods.factorization.bmf. ) ¶. Binary Matrix Factorization (BMF) . BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of the target matrix. Common methodologies include penalty SCORANGE – KEGG PATHWAYS SINGLE CELL ANALYSIS KEGG Pathways widget displays diagrams of molecular interactions, reactions and relations from the KEGG Pathways Database. It takes data on gene expression as an input, matches the genes to the biological processes and displays a list of corresponding pathways. To explore the pathway, the user can click on any process from the list orarrange
NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, oneDATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecularDOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated. NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading SCORANGE – SINGLE CELL ANALYSIS SINGLE CELL ANALYSIS Single Cell Analysis. for Everyone. Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data. Learn how to effortlessly group and identify cell types in your dataset with a newwidget: the
LSNMF (METHODS.FACTORIZATION.LSNMF) Parameters: max_iter (int) – Maximum number of factorization iterations.Note that the number of iterations depends on the speed of method convergence. Default is 30. min_residuals (float) – Minimal required improvement of the residuals from the previous iteration.They are computed between the target matrix and its MF estimate using the objective function associated to the MF algorithm.DICTYEXPRESS
Credits. Dictyostelium gene expression data provided by dictyExpress are from the Functional Genomics Project at Baylor College of Medicine. Founding was provided by the National Institute of Health (P01-HD39691), Slovenian Research Agency (P2-0209, J2-9699, L2-1112), and Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grants 1 T90 DA022885 and 1 R90 SCORANGE – WORKFLOWS SINGLE CELL ANALYSIS Data Visualization with t-SNE. Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. To see the raw data, double click on the Data Table widget. Double click on the t-SNE widget to display the celllandscape. Tags: t
BMF (METHODS.FACTORIZATION.BMF) Bmf (. methods.factorization.bmf. ) ¶. Binary Matrix Factorization (BMF) . BMF extends standard NMF to binary matrices. Given a binary target matrix (V), we want to factorize it into binary basis and mixture matrices, thus conserving the most important integer property of the target matrix. Common methodologies include penalty SCORANGE – KEGG PATHWAYS SINGLE CELL ANALYSIS KEGG Pathways widget displays diagrams of molecular interactions, reactions and relations from the KEGG Pathways Database. It takes data on gene expression as an input, matches the genes to the biological processes and displays a list of corresponding pathways. To explore the pathway, the user can click on any process from the list orarrange
NNDSVD (METHODS.SEEDING.NNDSVD) Nndsvd ( methods.seeding.nndsvd) ¶. Nndsvd (. methods.seeding.nndsvd. ) ¶. Nonnegative Double Singular Value Decomposition (NNDSVD) is a new method designed to enhance the initialization stage of the nonnegative matrix factorization. The basic algorithm contains no randomization and is based on two SVD processes, oneDATA MINING
Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecularDOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated. NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si Loading ICLIPRO: READ OVERLAP TESTING Overview and how to cite¶. iCLIPro is a Python package that can be used to control for systematic misassignments in iCLIP data. If you use iCLIPro in your research, please cite this paper (submitted for SCORANGE – EXPLORE THE DIVERSITY OF CELLS WITHIN YOUR In the workflow below, a component called Single Cell Datasets displays a list of ready-to-use single cell data example. We chose the data on bone marrow cells, and feed it into t-SNE component that plots the data in two-dimensional space. Visualizations in scOrange are interactive. The data we are using here comes from the bone marrow ofa
SCORANGE – BLOG SINGLE CELL ANALYSIS We analyse cell population changes in the course of chemotherapy by recreating a part of a study from Galen et al. (Cell, 2019) and projecting single cell data set from an AML patient undergoing treatment onto a t-SNE of a healthy individual. Categories: ApplyDomain tSNE
SCORANGE – DISCOVER NEW MARKER GENES THAT DISTINGUISH CELL Discover New Marker Genes that Distinguish Cell Types. In scOrange, we can find a cluster of cells and then characterize it with a set of differentially expressed genes. If there is a cell type that is prevailing in our cluster, this technique can be used to find candidates for gene markers. In other words, we can consider a dataset, score
DOWNLOAD.BIOLAB.SI
Read the data. Try this schema with the "brown-selected" data (from datasets that come with Orange). Visualize the data distances in aheat map.
SCORANGE – EXPLORING THE FRUIT FLY OLFACTORY SYSTEM SINGLE The data set comes with scOrange via Single Cell Datasets widget. The data set contains more than 1800 cells labelled with different drivers, that mark different neuronal subtypes, as seen in the Distributions widget. Here, we will focus on cells that are driven by GH146-GAL4, marking olfactory neurons - the neurons related to thesense of smell.
SCORANGE – FAQ SINGLE CELL ANALYSIS Orange Data Mining Toolbox. I am having trouble installing Orange. Make sure you are downloading the latest version.DOWNLOAD.BIOLAB.SI
Open to see the scree diagram and interactively select the number of components. Choose two best principal components and check if the classes from the input dataset are well separated.DOWNLOAD.BIOLAB.SI
Load data on Iris ("iris.tab") from preloaded documentation datasets. Any change in selection of the tree node changes the rendering in thescatter plot.
NOVNC - DOCKER.BIOLAB.SI noVNC - docker.biolab.si LoadingYoutube
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Home > Research > Laboratories > Bioinformatics Laboratory Bioinformatics Laboratory Location: R3.20/R3.22 BIOINFORMATICS LABORATORY IS A LARGE RESEARCH GROUP AT THE FACULTY OF COMPUTER AND INFORMATION SCIENCE, WHICH PERFORMS RESEARCH IN DATA SCIENCE. WE LIKE TO COMBINE MACHINE LEARNING AND DATA VISUALIZATION, AND CRAFT TECHNIQUES FOR EXPLORATORY DATA ANALYSIS AND EXPLAINABLE ARTIFICIAL INTELLIGENCE. Our aim is to design intuitive, useful, and visually appealing approaches for both data scientists and domain experts. We apply these methods to diverse application areas that include: * molecular biology and biomedicine, * anthropology, psychology* spectroscopy,
* image and network analytics, * business analytics. Our partners come from renowned institutions, such as Baylor College of Medicine in Houston, Harvard University from Boston, SOLEIL from Paris, University of Pavia, Higher School of Economics in Moscow, and Francis Crick Institute from London. With a range of computational servers, GPUs, and disk arrays, the group is well-equipped for data- and computation-intensive tasks. The lab develops Orange (GitHub repository ), a comprehensive machine learning and data visualization suite that also offers most of the methods we have invented in an easy-to-use visual programming environment. With over several thousand weekly users, Orange has a vibrant user base, and is one of the largest open source visualization-driven data science environments. (Check out Orange videos for tutorials and overview!) We also collaborate in the development of cool interactive web-based data exploration platforms like dictyExpress . Recently, the lab has set up a database and analytics infrastructure for COVID-19 research . We take pride in teaching excellence. We regularly teach courses from introductory data mining to deeper and more advanced topics, at our home faculty and at prestigious institutions like Baylor College of Medicine, Houston, and Higher School of Economics, Moscow. We also offer seminars and workshops to diverse groups from education for state institutions to in-company training. We are active in the promotion of general computer science to the lay public, and participate in all kinds of activities, from science festivals to summer schools and code weeks.* Lab members
* Blaž Zupan, Head of Laboratory* Tomaž Curk
* Andrej Čopar
* Janez Demšar
* Aleš Erjavec
* Rok Gomišček
* Tomaž Hočevar
* Ajda Pretnar
* Marko Toplak
* Lan Žagar
* Vesna Tanko
* Karin Hrovatin
* Amra Omanović
* Projects
* Collaboration Agreement for the continuation of the QUSAR/ORANGEproject
11.12.2019 - 31.12.2020 * Interaktivne vizualizacije za analizo genskih izrazov posameznihcelic
Bilateral projects01.10.2019 - 30.09.2021 * Uporaba malega proteinabakteriofaga v boju proti razvoju odpornosti proti antibiotikom pri bakteriji Staphylococcus aureus Research projects ARRS01.07.2019 - 30.06.2022 * Nilpotent orbits and commuting matrices Bilateral projects15.01.2019 - 31.12.2020 * ITzaSKP - Design of information-technology solutions in support of data-based implementation of Common agricultural policy of the EU Research projects ARRS(ITzaSKP), 01.11.2018 - 31.10.2020 * OMANOVIĆ AMRA - AMRA OMANOVIĆ - Young Researcher Research projects ARRS(OMANOVIĆ AMRA), 01.11.2018 - 31.10.2022 * Computational and data visualisation approaches to mining of large-scale data in single-cell genomics Bilateral projects01.09.2018 - 31.08.2020 * TURIZEM 4.0 - Tourism 4.0 – Enriched Tourist Experience Projects Structural Funds(TURIZEM 4.0), 01.09.2018 - 31.08.2021 * Pathogenic role of paraspeckle-like nuclear bodies in neurodegenerative diseases ALS and FTD Research projects ARRS01.07.2018 - 30.06.2021 * JRC CDP - Joint Research Centre - Collaborative Doctoral Partnerships (JRC CDP) (JRC CDP), 01.09.2017 - 31.08.2020 * BioPharm.si - BioPharm.SI:Next Generation of Biologics Projects Structural Funds(BioPharm.si), 07.07.2016 - 30.06.2020 * Artificial intelligence and inteligent systems ARRS research programmes01.01.2015 - 31.12.2020Past:
* Pogodba za izdelavo študije o možnostih uporabe podatkovagencije
26.10.2017 - 28.02.2018 * Izdelava gradiva in izvedba usposabljanj 26.09.2017 - 26.09.2018 * DNA sampling II: A method for identification of directly bound proteins at specific loci on bacterial chromosomes Research projects ARRS01.05.2017 - 30.04.2020 * Development of an open-source platform for multivariate analysisof FTIR data
Bilateral projects01.01.2017 - 31.12.2018 * Deep Models for Image Embedding in Systems Biology of a Social Amoeba Dictyostelium Bilateral projects01.01.2017 - 31.12.2018 * RICERCANDO - MONROE - Rapid Interpretation and Cross-Experiment Root-Cause Analysis in Network Data with Orange European projects(RICERCANDO - MONROE),01.07.2016 - 30.06.2018 * Conformal prediction methods in Orange 01.04.2016 - 05.09.2016 * Data Fusion in Systems Biology of a Social Amoeba Dictyostelium Bilateral projects01.01.2015 - 31.12.2016 * PKP 1 - GenExpress Projects Structural Funds(PKP 1),01.04.2014 - 30.09.2014 * Posttranscriptional regulatory networks in neurodegenerativediseases
Research projects ARRS01.08.2013 - 31.07.2016 * Conquering the Curse of Dimensionality by Using BackgroundKnowledge
Research projects ARRS01.08.2013 - 31.07.2016 * Epidemiology and Biodiversity Studies of Plant Pathogens Research projects ARRS01.08.2013 - 31.07.2016 * Internacionalizacija UL Projects Structural Funds26.07.2013 - 30.06.2015 * Computational approaches for identification of bacterial resistance pathways in Dictyostelium Bilateral projects01.01.2013 - 31.12.2014 * AXLE - Advanced Analytics for Extremely Large European Databases European projects(AXLE),01.11.2012 - 31.10.2015 * Growth and defense trade-offs in multitrophic interaction between potato and its two major pests Research projects ARRS01.07.2011 - 30.06.2014 * Functional genomics of potato-PVY interactions Research projects ARRS01.07.2011 - 30.06.2014 * Functional genomics of cholesterol homeostasis: the role of lanosterol 14alpha-demethylase in development of metabolic disorders Research projects ARRS01.07.2011 - 30.06.2014 * Combination of next generation sequencing and metagenomic analysis in the diagnostics of severe hop stunting Research projects ARRS01.07.2011 - 30.06.2014 * Evaluation of neuro-muscular trunk stabilization functions and development of exercise programs for lower back pain prevention Research projects ARRS01.07.2011 - 30.06.2014 * Development of estimation of classification reliability 24.05.2011 - 18.03.2013 * A next-generation analytics toolbox for integrated high-throughput genomic data analysis Bilateral projects01.01.2011 - 31.12.2012 * FightingDrugFailure - Priorities and standards in pharmacogenomic research: Opportunities for a safer and more efficient pharmacotherapy European projects(FightingDrugFailure),01.10.2010 - 30.09.2013 * Modeling the transcriptome Research projects ARRS01.05.2010 - 30.04.2012 * CARE-MI - Cardio repair european multidisciplinary initiative European projects(CARE-MI),01.04.2010 - 31.03.2015 * CLIP - Mapping functional protein-RNA interactions to identify new targets for oligonucleotide-based therapy European projects(CLIP ),01.04.2010 - 31.08.2013 * Data and knowledge integration methods for network systems biology Research projects ARRS01.05.2009 - 30.04.2012 * Qualitative modeling from data Research projects ARRS01.05.2009 - 30.04.2012 * Artificial intelligence and inteligent systems ARRS research programmes01.01.2009 - 31.12.2014 * Knowledge technology approaches in drug discovery: analysis and experimental planning in high-throughput genetics Research projects ARRS01.02.2008 - 31.01.2011 * X-MEDIA - Large Scale Knowledge Sharing and Reuse Across Media European projects(X-MEDIA),01.03.2006 - 30.09.2009Research
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