Scanpy umap

Wrappers to external functionality are found in scanpy. Filtering of highly-variable genes, batch-effect correction, per-cell normalization, preprocessing recipes. Any transformation of the data matrix that is not a tool. Annotate highly variable genes [Satija15] [Zheng17] [Stuart19]. Principal component analysis [Pedregosa11]. Normalization and filtering as of [Zheng17]. Normalization and filtering as of [Weinreb17]. Normalization and filtering as of Seurat [Satija15].

Also see Data integration. Note that a simple batch correction method is available via pp. Checkout scanpy. ComBat function for batch effect correction [Johnson07] [Leek12] [Pedersen12]. Compute a neighborhood graph of observations [McInnes18].

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Any transformation of the data matrix that is not preprocessing. In contrast to a preprocessing function, a tool usually adds an easily interpretable annotation to the data matrix, which can then be visualized with a corresponding plotting function. Force-directed graph drawing [Islam11] [Jacomy14] [Chippada18]. Diffusion Maps [Coifman05] [Haghverdi15] [Wolf18]. Cluster cells into subgroups [Traag18]. Cluster cells into subgroups [Blondel08] [Levine15] [Traag17].

scanpy umap

Computes a hierarchical clustering for the given groupby categories. Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf19]. Mapping out the coarse-grained connectivity structures of complex manifolds [Wolf19]. Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories. Score a set of genes [Satija15]. Score cell cycle genes [Satija15]. Simulate dynamic gene expression data [Wittmann09] [Wolf18].

The plotting module scanpy. For reading annotation use pandas.

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AnnData object. The following read functions are intended for the numeric data in the data matrix X. Read file and return AnnData object. Read 10x formatted hdf5 files and directories containing. Read other formats using functions borrowed from anndata.

The module sc. AnnData is reexported from anndata. A convenience function for setting some default matplotlib. An instance of the ScanpyConfig is available as scanpy. Influence the global behavior of plotting functions.Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata.


It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells. Tom White : distributed computing. Discuss usage on Discourse and development on GitHub. Get started by browsing tutorialsusage principles or the main API.

Follow changes in the release notes. Consider citing Genome Biology along with original references. Read up more on the format. New plotting classes can be accessed directly e. Added ax parameter which allows embedding the plot in other images.

Return a dictionary of axes for further manipulation. This includes the main plot, legend and dendrogram to totals. The groupby param can take a list of categories, e. PR Added title for colorbar and positioned as in dotplot for matrixplot. Improved the colorbar and size legend for dotplots. They also align at the bottom of the image and do not shrink if the dotplot image is smaller. A new style was added in which the dots are replaced by an empty circle and the square behind the circle is colored like in matrixplots.

Removed the tics for the y-axis as they tend to overlap with each other. Using the style method they can be displayed if needed. PR I Virshup. Added CellRank to scanpy ecosystem PR giovp.

Fix diffmap issue G Eraslan. Fix default size of dot in spatial plots PR issue giovp. This includes the main plot, legend and dendrogram to totals Legends can be removed. PR Added title for colorbar and positioned as in dotplot for matrixplot. Violin colors can be colored based on average gene expression as in dotplots. The linewidth of the violin plots is thinner.

Read the Docs v: stable Versions latest stable 1.One of the main strengths of the Bioconductor project lies in the use of a common data infrastructure that powers interoperability across packages. Users should be able to analyze their data using functions from different Bioconductor packages without the need to convert between formats.

This class implements a data structure that stores all aspects of our single-cell data - gene-by-cell expression data, per-cell metadata and per-gene annotation Figure 4. Figure 4. Each row of the assays corresponds to a row of the rowData pink shadingwhile each column of the assays corresponds to a column of the colData and reducedDims yellow shading. The SingleCellExperiment package is implicitly installed and loaded when using any package that depends on the SingleCellExperiment class, but it can also be explicitly installed and loaded as follows:.

Additionally, we use some functions from the scater and scran packages, as well as the CRAN package uwot which conveniently can also be installed through BiocManager::install. We then load the SingleCellExperiment package into our R session. This avoids the need to prefix our function calls with ::especially for packages that are heavily used throughout a workflow. If we imagine the SingleCellExperiment object to be a cargo ship, the slots can be thought of as individual cargo boxes with different contents, e.

In the rest of this chapter, we will discuss the available slots, their expected formats, and how we can interact with them. More experienced readers may note the similarity with the SummarizedExperiment class, and if you are such a reader, you may wish to jump directly to the end of this chapter for the single-cell-specific aspects of this class. To construct a rudimentary SingleCellExperiment object, we only need to fill the assays slot. This contains primary data such as a matrix of sequencing counts where rows correspond to features genes and columns correspond to samples cells Figure 4.

Note that we provide our data as a named list where each entry of the list is a matrix. To inspect the object, we can simply type sce into the console to see some pertinent information, which will display an overview of the various slots available to us which may or may not have any data.

What makes the assays slot especially powerful is that it can hold multiple representations of the primary data. This is especially useful for storing the raw count matrix as well as a normalized version of the data.

We can do just that as shown below, using the scater package to compute a normalized and log-transformed representation of the initial primary data. Note that, at each step, we overwrite our previous sce by reassigning the results back to sce.

scanpy umap

This is possible because these particular functions return a SingleCellExperiment object that contains the results in addition to original data. Some functions - especially those outside of single-cell oriented Bioconductor packages - do not, in which case you will need to append your results to the sce object - see below for an example.In the meanwhile, we have added and removed a few pieces.

scanpy umap

On a unix system, you can uncomment and run the following to download and unpack the data. The last line creates a directory for writing processed data. Download the notebook by clicking on the Edit on GitHub button. Alternatively, download the whole scanpy-tutorial repository. Hit it twice to expand the view. It also comes with its own HDF5 file format:. Show those genes that yield the highest fraction of counts in each single cells, across all cells.

Let us assemple some information about mitochondrial genes, which are important for quality control. With pp.

2020 STAT115 Lect9.3 Gene Expression Analysis Scenario

Actually do the filtering by slicing the AnnData object. Set the. This simply freezes the state of the AnnData object. You can get back an AnnData of the object in.

The result of the previous highly-variable-genes detection is stored as an annotation in. In that case, the step actually do the filtering below is unnecessary, too. Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed.

Scale the data to unit variance. Reduce the dimensionality of the data by running principal component analysis PCAwhich reveals the main axes of variation and denoises the data. Let us inspect the contribution of single PCs to the total variance in the data.

This gives us information about how many PCs we should consider in order to compute the neighborhood relations of cells, e.

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In our experience, often, a rough estimate of the number of PCs does fine. Let us compute the neighborhood graph of cells using the PCA representation of the data matrix. You might simply use default values here. It is potentially more faithful to the global connectivity of the manifold than tSNE, i. In some ocassions, you might still observe disconnected clusters and similar connectivity violations.

They can usually be remedied by running:. As we set the. Note that Leiden clustering directly clusters the neighborhood graph of cells, which we already computed in the previous section. Let us compute a ranking for the highly differential genes in each cluster. For this, by default, the. The simplest and fastest method to do so is the t-test. The result of a Wilcoxon rank-sum Mann-Whitney-U test is very similar.Meet BioTuring Browser and its new CITE-seq dashboard, a complete package for interactively exploring single-cell gene expression data in parallel with surface protein information.

Instantly access and reanalyze latest single-cell RNA-seq and CITE-seq datasets from publications, all uniformly annotated, and ready for visualization. Look for populations that own similar transcriptional profiles to your selected cells in the entire public database, at the same time study what genes are similarly expressed and enrichment processes.

Detect studies with positive expression of one or multiple genes, at the same time quickly compare gene expression among different clusters, cell types, disease conditions, The knowledge base for prediction can be customized to your own definition.

BioTuring Single-cell Browser is optimized to visualize up to 1. Finding differentially expressed genes. Identifying cell types with real-time prediction.

Pairing clonotype data with expression data. Viewing composition of a cell population. For RNA sequencing data Hera. Webinars Blog Videos. Log in Sign up. E: support bioturing. BioTuring Browser. A next-generation platform for single-cell multi-omics data Meet BioTuring Browser and its new CITE-seq dashboard, a complete package for interactively exploring single-cell gene expression data in parallel with surface protein information. Access cells from published works.

A new way to review published data Instantly access and reanalyze latest single-cell RNA-seq and CITE-seq datasets from publications, all uniformly annotated, and ready for visualization. Search similar cell populations in the entire database Look for populations that own similar transcriptional profiles to your selected cells in the entire public database, at the same time study what genes are similarly expressed and enrichment processes.

Explore gene expression across all studies Detect studies with positive expression of one or multiple genes, at the same time quickly compare gene expression among different clusters, cell types, disease conditions, See list of studies.Single-cell experiments are often performed on tissues containing many cell types.

Monocle 3 provides a simple set of functions you can use to group your cells according to their gene expression profiles into clusters. Often cells form clusters that correspond to one cell type or a set of highly related cell types. Monocle 3 uses techniques to do this that are widely accepted in single-cell RNA-seq analysis and similar to the approaches used by Seuratscanpyand other tools.

In this section, you will learn how to cluster cells using Monocle 3. We will demonstrate the main functions used for clustering with the C. Now that the data's all loaded up, we need to pre-process it. We will just use the standard PCA method in this demonstration. When using PCA, you should specify the number of principal components you want Monocle to compute.

It's a good idea to check that you're using enough PCs to capture most of the variation in gene expression across all the cells in the data set.

We can see that using more than PCs would capture only a small amount of additional variation, and each additional PC makes downstream steps in Monocle slower. Now we're ready to visualize the cells. As you can see the cells form many groups, some with thousands of cells, some with only a few. Passing umap. If your computer has multiple cores, you can use the cores argument to make UMAP multithreaded.

If you want, you can also use t-SNE to visualize your data. When performing gene expression analysis, it's important to check for batch effectswhich are systematic differences in the transcriptome of cells measured in different experimental batches.

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These could be technical in nature, such as those introduced during the single-cell RNA-seq protocol, or biological, such as those that might arise from different litters of mice. How to recognize batch effects and account for them so that they don't confound your analysis can be a complex issue, but Monocle provides tools for dealing with them. You should always check for batch effects when you perform dimensionality reduction.

A benchmark of batch-effect correction methods for single-cell RNA sequencing data

You should add a column to the colData that encodes which batch each cell is from. Then you can simply color the cells by batch. Coloring the UMAP by plate reveals:. Dramatic batch effects are not evident in this data. If the data contained more substantial variation due to plate, we'd expect to see groups of cells that really only come from one plate.

Monocle 3 does so by calling Aaron Lun's excellent package batchelor. Grouping cells into clusters is an important step in identifying the cell types represented in your data. Monocle uses a technique called community detection to group cells.

This approach was introduced by Levine et al as part of the phenoGraph algorithm. You can visualize these partitions like this:. For example, the call below colors the cells according to their cell type annotation, and each cluster is labeled according the most common annotation within it:. Once cells have been clustered, we can ask what genes makes them different from one another. We could group the cells according to cluster, partition, or any categorical variable in colData cds.

You can rank the table according to one or more of the specificity metrics and take the top gene for each cluster.Metrics details. Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions.

PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

Single-cell RNA-seq offers unparalleled opportunities for comprehensive molecular profiling of thousands of individual cells, with expected major impacts across a broad range of biomedical research. The resulting datasets are often discussed using the term transcriptional landscape.

However, the algorithmic analysis of cellular heterogeneity and patterns across such landscapes still faces fundamental challenges, for instance, in how to explain cell-to-cell variation. Current computational approaches attempt to achieve this usually in one of two ways [ 1 ]. Clustering assumes that data is composed of biologically distinct groups such as discrete cell types or states and labels these with a discrete variable—the cluster index.

By contrast, inferring pseudotemporal orderings or trajectories of cells [ 2 — 4 ] assumes that data lie on a connected manifold and labels cells with a continuous variable—the distance along the manifold.

While the former approach is the basis for most analyses of single-cell data, the latter enables a better interpretation of continuous phenotypes and processes such as development, dose response, and disease progression. Here, we unify both viewpoints. A central example of dissecting heterogeneity in single-cell experiments concerns data that originate from complex cell differentiation processes. However, analyzing such data using pseudotemporal ordering [ 25 — 9 ] faces the problem that biological processes are usually incompletely sampled.


As a consequence, experimental data do not conform with a connected manifold and the modeling of data as a continuous tree structure, which is the basis for existing algorithms, has little meaning.

This problem exists even in clustering-based algorithms for the inference of tree-like processes [ 10 — 12 ], which make the generally invalid assumption that clusters conform with a connected tree-like topology. Moreover, they rely on feature-space based inter-cluster distances, like the euclidean distance of cluster means. However, such distance measures quantify biological similarity of cells only at a local scale and are fraught with problems when used for larger-scale objects like clusters.

Efforts for addressing the resulting high non-robustness of tree-fitting to distances between clusters [ 10 ] by sampling [ 1112 ] have only had limited success. Partition-based graph abstraction PAGA resolves these fundamental problems by generating graph-like maps of cells that preserve both continuous and disconnected structure in data at multiple resolutions. The data-driven formulation of PAGA allows to robustly reconstruct branching gene expression changes across different datasets and, for the first time, enabled reconstructing the lineage relations of a whole adult animal [ 13 ].

Furthermore, we show that PAGA-initialized manifold learning algorithms converge faster, produce embeddings that are more faithful to the global topology of high-dimensional data, and introduce an entropy-based measure for quantifying such faithfulness.


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