Release notes

Contents

Release notes#

Version 1.12#

1.12.0.dev117+g2f4c8a4e 2025-06-12#

Breaking changes#

  • Adopt the Scientific Python deprecation schedule: remove Python 3.10 support and add Python 3.13 support, require anndata≥0.9 P Angerer (#3485)

Bug fixes#

  • Ensure axis_nnz calculates its chunk size/shape correctly with dask I Gold (#3667)

Development Process#

Documentation#

Features#

  • Added n_components parameter to tsne() Kitsune (#2803)

  • Add zarr support and convert_strings_to_categoricals parameter to scanpy.write() P Angerer (#3498)

  • Add support for scipy.sparse.csr_array and scipy.sparse.csc_array P Angerer (#3563)

  • Added a new compressed parameter to the read_10x_mtx function to support reading uncompressed matrix files produced by tools like STARsolo. This parameter allows users to read uncompressed outputs from tools that don’t produce gzipped files by default. (#3564)

Miscellaneous improvements#

Version 1.11#

1.11.2 2025-05-28#

Bug fixes#

  • Fix zappy compatibility for clip_array P Angerer (#3351)

  • Fixes an error where regress_out would fail to work with integer types S Dicks (#3461)

  • Prevent plotting with mask_obs from mutating data V Menon (#3496)

  • Prevent scanpy.pp.scale() from creating a dask Array with numpy.matrix chunks P Angerer (#3597)

  • Allow using sklearn ≥1.6, Dask ≥2024.8, and sphinx ≥8.2.1 P Angerer (#3611)

  • Fixed handling of ext argument in scanpy.read() I Gold #3643

  • Fix error message when trying to use sc.pp.pca(x, zero_center=False) with a sparse dask array. P Angerer (#3646)

Documentation#

Performance#

  • Speed up for a categorical regressor in regress_out() S Dicks I Gold (#3353)

  • In pp.normalize_total, the median is now computed in-memory when using Dask S Dicks (#3379)

  • Speed up pp.normalize_total with a numba kernel for csr-matrices S Dicks (#3571)

1.11.1 2025-03-31#

Bug fixes#

  • Fix compatibility with IPython 9 P Angerer (#3499)

  • Prevent too-low matplotlib version from being used P Angerer (#3534)

Features#

Performance#

  • Speed up wilcoxon rank-sum test with numba G Wu (#3529)

1.11.0 2025-02-14#

Release candidates:

  • rc2 2025-01-24

  • rc1 2024-12-20

Features#

Performance#

Documentation#

Bug fixes#

Development Process#

  • rc2 Fix version number inference in development environments (CI and local) P Angerer (#3441)

Version 1.10#

1.10.4 2024-11-12#

Breaking changes#

  • Remove Python 3.9 support P Angerer (#3283)

Bug fixes#

1.10.3 2024-09-17#

Bug fixes#

1.10.2 2024-06-25#

Development Process#

  • Add performance benchmarking #2977 R Shrestha, P Angerer

Documentation#

  • Document several missing parameters in docstring #2888 S Cheney

  • Fixed incorrect instructions in “testing” dev docs #2994 I Virshup

  • Update marsilea tutorial to use group_ methods #3001 I Virshup

  • Fixed citations #3032 P Angerer

  • Improve dataset documentation #3060 P Angerer

Bug fixes#

  • Compatibility with matplotlib 3.9 #2999 I Virshup

  • Add clear errors where backed mode-like matrices (i.e., from sparse_dataset) are not supported #3048 I gold

  • Write out full pca results when _choose_representation is called i.e., neighbors() without pca() #3078 I gold

  • Fix deprecated use of .A with sparse matrices #3084 P Angerer

  • Fix zappy support #3089 P Angerer

  • Fix dotplot group order with pandas 1.x #3101 P Angerer

Performance#

  • sparse_mean_variance_axis now uses all cores for the calculations #3015 S Dicks

  • pp.highly_variable_genes with flavor=seurat_v3 now uses a numba kernel #3017 S Dicks

  • Speed up scrublet() #3044 S Dicks and #3056 P Angerer

  • Speed up clipping of array in scale() #3100 P Ashish & S Dicks

1.10.1 2024-04-09#

Documentation#

Bug fixes#

  • Fix aggregate when aggregating by more than two groups #2965 I Virshup

Performance#

  • scale() now uses numba kernels for sparse.csr_matrix and sparse.csc_matrix when zero_center==False and mask_obs is provided. This greatly speed up execution #2942 S Dicks

1.10.0 2024-03-26#

scanpy 1.10 brings a large amount of new features, performance improvements, and improved documentation.

Some highlights:

  • Improved support for out-of-core workflows via dask. See new tutorial: Using dask with Scanpy demonstrating counts-to-clusters for 1.4 million cells in <10 min.

  • A new basic clustering tutorial demonstrating an updated workflow.

  • Opt-in increased performance for neighbor search and clustering (how to guide).

  • Ability to mask observations or variables from a number of methods (see Customizing Scanpy plots for an example with plotting embeddings)

  • A new function aggregate() for computing aggregations of your data, very useful for pseudo bulking!

Features#

Documentation#

Bug fixes#

  • Updated read_visium() such that it can read spaceranger 2.0 files L Lehner

  • Fix normalize_total() for dask #2466 P Angerer

  • Fix setting :attr:scanpy.settings.verbosity in some cases #2605 P Angerer

  • Fix all remaining pandas warnings #2789 P Angerer

  • Fix some annoying plotting warnings around violin plots #2844 P Angerer

  • Scanpy now has a test job which tests against the minumum versions of the dependencies. In the process of implementing this, many bugs associated with using older versions of pandas, anndata, numpy, and matplotlib were fixed. #2816 I Virshup

  • Fix warnings caused by internal usage of pandas.DataFrame.stack with pandas>=2.1 #2864I Virshup

  • scanpy.get.aggregate() now always returns numpy.ndarray #2893 S Dicks

  • Removes self from array of neighbors for use_approx_neighbors = True in scrublet() #2896S Dicks

  • Compatibility with scipy 1.13 #2943 I Virshup

  • Fix use of dendrogram() on highly correlated low precision data #2928 P Angerer

  • Fix pytest deprecation warning #2879 P Angerer

Development Process#

  • Scanpy is now tested against python 3.12 #2863 ivirshup

  • Fix testing package build #2468 P Angerer

Deprecations#

  • Dropped support for Python 3.8. More details here. #2695 P Angerer

  • Deprecated specifying large numbers of function parameters by position as opposed to by name/keyword in all public APIs. e.g. prefer sc.tl.umap(adata, min_dist=0.1, spread=0.8) over sc.tl.umap(adata, 0.1, 0.8) #2702 P Angerer

  • Dropped support for umap<0.5 for performance reasons. #2870 P Angerer

Version 1.9#

1.9.8 2024-01-26#

Bug fixes#

  • Fix handling of numpy array palettes for old numpy versions #2832 P Angerer

1.9.7 2024-01-25#

Bug fixes#

1.9.6 2023-10-31#

Bug fixes#

1.9.5 2023-09-08#

Bug fixes#

  • Remove use of deprecated dtype argument to AnnData constructor #2658 Isaac Virshup

1.9.4 2023-08-24#

Bug fixes#

1.9.3 2023-03-02#

Bug fixes#

  • Variety of fixes against pandas 2.0.0rc0 #2434 I Virshup

1.9.2 2023-02-16#

Bug fixes#

  • highly_variable_genes() layer argument now works in tandem with batches #2302 D Schaumont

  • highly_variable_genes() with flavor='cell_ranger' now handles the case in #2230 where the number of calculated dispersions is less than n_top_genes #2231 L Zappia

  • Fix compatibility with matplotlib 3.7 #2414 I Virshup P Fisher

  • Fix scrublet numpy matrix compatibility issue #2395 A Gayoso

1.9.1 2022-04-05#

Bug fixes#

  • normalize_total() works when Dask is not installed #2209 R Cannoodt

  • Fix embedding plots by bumping matplotlib dependency to version 3.4 #2212 I Virshup

1.9.0 2022-04-01#

Tutorials#

Experimental module#

Features#

  • filter_rank_genes_groups() now allows to filter with absolute values of log fold change #1649 S Rybakov

  • _choose_representation now subsets the provided representation to n_pcs, regardless of the name of the provided representation (should affect mostly neighbors()) #2179 I Virshup PG Majev

  • scanpy.pp.scrublet() (and related functions) can now be used on AnnData objects containing multiple batches #1965 J Manning

  • Number of variables plotted with pca_loadings() can now be controlled with n_points argument. Additionally, variables are no longer repeated if the anndata has less than 30 variables #2075 Yves33

  • Dask arrays now work with scanpy.pp.normalize_total() #1663 G Buckley, I Virshup

  • embedding_density() now allows more than 10 groups #1936 A Wolf

  • Embedding plots can now pass colorbar_loc to specify the location of colorbar legend, or pass None to not show a colorbar #1821 A Schaar I Virshup

  • Embedding plots now have a dimensions argument, which lets users select which dimensions of their embedding to plot and uses the same broadcasting rules as other arguments #1538 I Virshup

  • print_versions() now uses session_info #2089 P Angerer I Virshup

Ecosystem#

Multiple packages have been added to our ecosystem page, including:

  • decoupler a for footprint analysis and pathway enrichement #2186 PB Mompel

  • dandelion for B-cell receptor analysis #1953 Z Tuong

  • CIARA a feature selection tools for identifying rare cell types #2175 M Stock

Bug fixes#

  • Fixed finding variables with use_raw=True and basis=None in scanpy.pl.scatter() #2027 E Rice

  • Fixed scanpy.pp.scrublet() to address #1957 FlMai and ensure raw counts are used for simulation

  • Functions in scanpy.datasets no longer throw OldFormatWarnings when using anndata 0.8 #2096 I Virshup

  • Fixed use of scanpy.pp.neighbors() with method='rapids': RAPIDS cuML no longer returns a squared Euclidean distance matrix, so we should not square-root the kNN distance matrix. #1828 M Zaslavsky

  • Removed pytables dependency by implementing read_10x_h5 with h5py due to installation errors on Windows #2064

  • Fixed bug in scanpy.external.pp.hashsolo() where default value was set improperly #2190 B Reiz

  • Fixed bug in scanpy.pl.embedding() functions where an error could be raised when there were missing values and large numbers of categories #2187 I Virshup

Version 1.8#

1.8.2 2021-11-3#

Documentation#

  • Update conda installation instructions #1974 L Heumos

Bug fixes#

Ecosystem#

  • Added PASTE (a tool to align and integrate spatial transcriptomics data) to scanpy ecosystem.

1.8.1 2021-07-07#

Bug fixes#

1.8.0 2021-06-28#

Metrics module#

Features#

Ecosystem#

  • Added Cubé to ecosystem page #1878 C Lambden

  • Added triku a feature selection method to the ecosystem page #1722 AM Ascensión

  • Added dorothea and progeny to the ecosystem page #1767 P Badia-i-Mompel

Documentation#

Bug fixes#

  • Fix scanpy.pl.paga_path() TypeError with recent versions of anndata #1047 P Angerer

  • Fix detection of whether IPython is running #1844 I Virshup

  • Fixed reproducibility of scanpy.tl.diffmap() (added random_state) #1858 I Kucinski

  • Fixed errors and warnings from embedding plots with small numbers of categories after sns.set_palette was called #1886 I Virshup

  • Fixed handling of gene_symbols argument in a number of sc.pl.rank_genes_groups* functions #1529 F Ramirez I Virshup

  • Fixed handling of use_raw for sc.tl.rank_genes_groups when no .raw is present #1895 I Virshup

  • scanpy.pl.rank_genes_groups_violin() now works for raw=False #1669 M van den Beek

  • scanpy.pl.dotplot() now uses smallest_dot argument correctly #1771 S Flemming

Development Process#

  • Switched to flit for building and deploying the package, a simple tool with an easy to understand command line interface and metadata #1527 P Angerer

  • Use pre-commit for style checks #1684 #1848 L Heumos I Virshup

Deprecations#

Version 1.7#

1.7.2 2021-04-07#

Bug fixes#

Ecosystem#

  • Added triku a feature selection method to the ecosystem page #1722 AM Ascensión

  • Added dorothea and progeny to the ecosystem page #1767 P Badia-i-Mompel

1.7.1 2021-02-24#

Documentation#

  • More twitter handles for core devs #1676 G Eraslan

Bug fixes#

1.7.0 2021-02-03#

Features#

  • Add new 10x Visium datasets to visium_sge() #1473 G Palla

  • Enable download of source image for 10x visium datasets in visium_sge() #1506 H Spitzer

  • Refactor of scanpy.pl.spatial(). Better support for plotting without an image, as well as directly providing images #1512 G Palla

  • Dict input for scanpy.queries.enrich() #1488 G Eraslan

  • rank_genes_groups_df() can now return fraction of cells in a group expressing a gene, and allows retrieving values for multiple groups at once #1388 G Eraslan

  • Color annotations for gene sets in heatmap() are now matched to color for cluster #1511 L Sikkema

  • PCA plots can now annotate axes with variance explained #1470 bfurtwa

  • Plots with groupby arguments can now group by values in the index by passing the index’s name (like pd.DataFrame.groupby). #1583 F Ramirez

  • Added na_color and na_in_legend keyword arguments to embedding() plots. Allows specifying color for missing or filtered values in plots like umap() or spatial() #1356 I Virshup

  • embedding() plots now support passing dict of {cluster_name: cluster_color, ...} for palette argument #1392 I Virshup

External tools (new)#

External tools (changes)#

Documentation#

Performance#

Bugfixes#

  • Consistent fold-change, fractions calculation for filter_rank_genes_groups #1391 S Rybakov

  • Fixed bug where score_genes would error if one gene was passed #1398 I Virshup

  • Fixed log1p inplace on integer dense arrays #1400 I Virshup

  • Fix docstring formatting for rank_genes_groups() #1417 P Weiler

  • Removed PendingDeprecationWarning`s from use of `np.matrix #1424 P Weiler

  • Fixed indexing byg in ~scanpy.pp.highly_variable_genes #1456 V Bergen

  • Fix default number of genes for marker_genes_overlap #1464 MD Luecken

  • Fixed passing groupby and dendrogram_key to dendrogram() #1465 M Varma

  • Fixed download path of pbmc3k_processed #1472 D Strobl

  • Better error message when computing DE with a group of size 1 #1490 J Manning

  • Update cugraph API usage for v0.16 #1494 R Ilango

  • Fixed marker_gene_overlap default value for top_n_markers #1464 MD Luecken

  • Pass random_state to RAPIDs UMAP #1474 C Nolet

  • Fixed anndata version requirement for concat() (re-exported from scanpy as sc.concat) #1491 I Virshup

  • Fixed the width of the progress bar when downloading data #1507 M Klein

  • Updated link for moignard15 dataset #1542 I Virshup

  • Fixed bug where calling set_figure_params could block if IPython was installed, but not used. #1547 I Virshup

  • violin() no longer fails if .raw not present #1548 I Virshup

  • spatial() refactoring and better handling of spatial data #1512 G Palla

  • pca() works with chunked=True again #1592 I Virshup

  • ingest() now works with umap-learn 0.5.0 #1601 S Rybakov

Version 1.6#

1.6.0 2020-08-15#

This release includes an overhaul of dotplot(), matrixplot(), and stacked_violin() (#1210 F Ramirez), and of the internals of rank_genes_groups() (#1156 S Rybakov).

Overhaul of dotplot(), matrixplot(), and stacked_violin() #1210 F Ramirez#

  • An overhauled tutorial Core plotting functions.

  • New plotting classes can be accessed directly (e.g., DotPlot) or using the return_fig param.

  • It is possible to plot log fold change and p-values in the rank_genes_groups_dotplot() family of functions.

  • Added ax parameter which allows embedding the plot in other images.

  • Added option to include a bar plot instead of the dendrogram containing the cell/observation totals per category.

  • Return a dictionary of axes for further manipulation. This includes the main plot, legend and dendrogram to totals

  • Legends can be removed.

  • The groupby param can take a list of categories, e.g., groupby=[‘tissue’, ‘cell type’].

  • Added padding parameter to dotplot and stacked_violin. #1270

  • Added title for colorbar and positioned as in dotplot for matrixplot().

  • dotplot() changes:

    • Improved the colorbar and size legend for dotplots. Now the colorbar and size have titles, which can be modified using the colorbar_title and size_title params. They also align at the bottom of the image and do not shrink if the dotplot image is smaller.

    • Allow plotting genes in rows and categories in columns (swap_axes).

    • Using DotPlot, the dot_edge_color and line width can be modified, a grid can be added, and other modifications are enabled.

    • 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).

  • stacked_violin() changes:

    • Violin colors can be colored based on average gene expression as in dotplots.

    • The linewidth of the violin plots is thinner.

    • 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.

Additions#

Bug fixes#

  • Avoid warning in rank_genes_groups() if ‘t-test’ is passed #1303 A Wolf

  • Restrict sphinx version to <3.1, >3.0 #1297 I Virshup

  • Clean up _ranks and fix dendrogram for scipy 1.5 #1290 S Rybakov

  • Use .raw to translate gene symbols if applicable #1278 E Rice

  • Fix diffmap (#1262) G Eraslan

  • Fix neighbors in spring_project #1260 S Rybakov

  • Fix default size of dot in spatial plots #1255 #1253 giovp

  • Bumped version requirement of scipy to scipy>1.4 to support rmatmat argument of LinearOperator #1246 I Virshup

  • Fix asymmetry of scores for the 'wilcoxon' method in rank_genes_groups() #754 S Rybakov

  • Avoid trimming of gene names in rank_genes_groups() #753 S Rybakov

Version 1.5#

1.5.1 2020-05-21#

Bug fixes#

  • Fixed a bug in pca(), where random_state did not have an effect for sparse input #1240 I Virshup

  • Fixed docstring in pca() which included an unused argument #1240 I Virshup

1.5.0 2020-05-15#

The 1.5.0 release adds a lot of new functionality, much of which takes advantage of anndata updates 0.7.0 - 0.7.2. Highlights of this release include support for spatial data, dedicated handling of graphs in AnnData, sparse PCA, an interface with scvi, and others.

Spatial data support#

  • Tutorials for basic analysis and integration with single cell data G Palla

  • read_visium() read 10x Visium data #1034 G Palla, P Angerer, I Virshup

  • visium_sge() load Visium data directly from 10x Genomics #1013 M Mirkazemi, G Palla, P Angerer

  • spatial() plot spatial data #1012 G Palla, P Angerer

New functionality#

External tools#

  • scanpy.external.pp.scvi for preprocessing with scVI #1085 G Xing

  • Guide for using Scanpy in R #1186 L Zappia

Performance#

  • pca() now uses efficient implicit centering for sparse matrices. This can lead to signifigantly improved performance for large datasets #1066 A Tarashansky

  • score_genes() now has an efficient implementation for sparse matrices with missing values #1196 redst4r.

Warning

The new pca() implementation can result in slightly different results for sparse matrices. See the pr (#1066) and documentation for more info.

Code design#

Bug fixes#

Version 1.4#

1.4.6 2020-03-17#

Functionality in external#

Code design#

Bug fixes#

  • adapt ingest() for UMAP 0.4 #1038 #1106 S Rybakov

  • compat with matplotlib 3.1 and 3.2 #1090 I Virshup, P Angerer

  • fix PAGA for new igraph #1037 P Angerer

  • fix rapids compat of louvain #1079 LouisFaure

1.4.5 2019-12-30#

Please install scanpy==1.4.5.post3 instead of scanpy==1.4.5.

New functionality#

Code design#

  • downsample_counts now always preserves the dtype of it’s input, instead of converting floats to ints #865 I Virshup

  • allow specifying a base for log1p() #931 G Eraslan

  • run neighbors on a GPU using rapids #830 T White

  • param docs from typed params P Angerer

  • embedding_density() now only takes one positional argument; similar for embedding_density(), which gains a param groupby #965 A Wolf

  • webpage overhaul, ecosystem page, release notes, tutorials overhaul #960 #966 A Wolf

Warning

  • changed default solver in pca() from auto to arpack

  • changed default use_raw in score_genes() from False to None

1.4.4 2019-07-20#

New functionality#

  • scanpy.get adds helper functions for extracting data in convenient formats #619 I Virshup

Bug fixes#

  • Stopped deprecations warnings from AnnData 0.6.22 I Virshup

Code design#

  • normalize_total() gains param exclude_highly_expressed, and fraction is renamed to max_fraction with better docs A Wolf

1.4.3 2019-05-14#

Bug fixes#

  • neighbors() correctly infers n_neighbors again from params, which was temporarily broken in v1.4.2 I Virshup

Code design#

  • calculate_qc_metrics() is single threaded by default for datasets under 300,000 cells – allowing cached compilation #615 I Virshup

1.4.2 2019-05-06#

New functionality#

  • combat() supports additional covariates which may include adjustment variables or biological condition #618 G Eraslan

  • highly_variable_genes() has a batch_key option which performs HVG selection in each batch separately to avoid selecting genes that vary strongly across batches #622 G Eraslan

Bug fixes#

  • rank_genes_groups() t-test implementation doesn’t return NaN when variance is 0, also changed to scipy’s implementation #621 I Virshup

  • umap() with init_pos='paga' detects correct dtype A Wolf

  • louvain() and leiden() auto-generate key_added=louvain_R upon passing restrict_to, which was temporarily changed in 1.4.1 A Wolf

Code design#

1.4.1 2019-04-26#

New functionality#

Code design#

  • .layers support of scatter plots F Ramirez

  • fix double-logarithmization in compute of log fold change in rank_genes_groups() A Muñoz-Rojas

  • fix return sections of docs P Angerer

Version 1.3#

1.3.8 2019-02-05#

1.3.7 2019-01-02#

  • API changed from import scanpy as sc to import scanpy.api as sc.

  • phenograph() wraps the graph clustering package Phenograph [Levine et al., 2015] thanks to A Mousa

1.3.6 2018-12-11#

Major updates#

  • a new plotting gallery for visualizing-marker-genes F Ramirez

  • tutorials are integrated on ReadTheDocs, pbmc3k and paga-paul15 A Wolf

Interactive exploration of analysis results through manifold viewers#

Code design#

1.3.5 2018-12-09#

  • uncountable figure improvements #369 F Ramirez

1.3.4 2018-11-24#

1.3.3 2018-11-05#

Major updates#

  • a fully distributed preprocessing backend T White and the Laserson Lab

Code design#

Note

Also see changes in anndata 0.6.

  • changed default compression to None in write_h5ad() to speed up read and write, disk space use is usually less critical

  • performance gains in write_h5ad() due to better handling of strings and categories S Rybakov

1.3.1 2018-09-03#

RNA velocity in single cells [La Manno et al., 2018]#

  • Scanpy and AnnData support loom’s layers so that computations for single-cell RNA velocity [La Manno et al., 2018] become feasible S Rybakov and V Bergen

  • scvelo harmonizes with Scanpy and is able to process loom files with splicing information produced by Velocyto [La Manno et al., 2018], it runs a lot faster than the count matrix analysis of Velocyto and provides several conceptual developments

Plotting (Generic)#

  • dotplot() for visualizing genes across conditions and clusters, see here #199 F Ramirez

  • heatmap() for pretty heatmaps #175 F Ramirez

  • violin() produces very compact overview figures with many panels #175 F Ramirez

There now is a section on imputation in external:#

Version 1.2#

1.2.1 2018-06-08#

Plotting of Generic marker genes and quality control.#

1.2.0 2018-06-08#

  • paga() improved, see PAGA; the default model changed, restore the previous default model by passing model='v1.0'

Version 1.1#

1.1.0 2018-06-01#

Version 1.0#

1.0.0 2018-03-30#

Major updates#

  • Scanpy is much faster and more memory efficient: preprocess, cluster and visualize 1.3M cells in 6h, 130K cells in 14min, and 68K cells in 3min A Wolf

  • the API gained a preprocessing function neighbors() and a class Neighbors() to which all basic graph computations are delegated A Wolf

Warning

Upgrading to 1.0 isn’t fully backwards compatible in the following changes

  • the graph-based tools louvain() dpt() draw_graph() umap() diffmap() paga() require prior computation of the graph: sc.pp.neighbors(adata, n_neighbors=5); sc.tl.louvain(adata) instead of previously sc.tl.louvain(adata, n_neighbors=5)

  • install numba via conda install numba, which replaces cython

  • the default connectivity measure (dpt will look different using default settings) changed. setting method='gauss' in sc.pp.neighbors uses gauss kernel connectivities and reproduces the previous behavior, see, for instance in the example paul15.

  • namings of returned annotation have changed for less bloated AnnData objects, which means that some of the unstructured annotation of old AnnData files is not recognized anymore

  • replace occurances of group_by with groupby (consistency with pandas)

  • it is worth checking out the notebook examples to see changes, e.g. the seurat example.

  • upgrading scikit-learn from 0.18 to 0.19 changed the implementation of PCA, some results might therefore look slightly different

Further updates#

  • UMAP [McInnes et al., 2018] can serve as a first visualization of the data just as tSNE, in contrast to tSNE, UMAP directly embeds the single-cell graph and is faster; UMAP is also used for measuring connectivities and computing neighbors, see neighbors() A Wolf

  • graph abstraction: AGA is renamed to PAGA: paga(); now, it only measures connectivities between partitions of the single-cell graph, pseudotime and clustering need to be computed separately via louvain() and dpt(), the connectivity measure has been improved A Wolf

  • logistic regression for finding marker genes rank_genes_groups() with parameter method='logreg' A Wolf

  • louvain() provides a better implementation for reclustering via restrict_to A Wolf

  • scanpy no longer modifies rcParams upon import, call :func:scanpy.set_figure_params to set the ‘scanpy style’ A Wolf

  • default cache directory is ./cache/, set settings.cachedir to change this; nested directories in this are avoided A Wolf

  • show edges in scatter plots based on graph visualization draw_graph() and umap() by passing edges=True A Wolf

  • downsample_counts() for downsampling counts MD Luecken

  • default 'louvain_groups' are called 'louvain' A Wolf

  • 'X_diffmap' contains the zero component, plotting remains unchanged A Wolf

Version 0.4#

0.4.4 2018-02-26#

0.4.3 2018-02-09#

0.4.2 2018-01-07#

  • amendments in PAGA and its plotting functions A Wolf

0.4.0 2017-12-23#

Version 0.3#

0.3.2 2017-11-29#

0.3.0 2017-11-16#

Version 0.2#

0.2.9 2017-10-25#

Initial release of the new trajectory inference method PAGA#

  • paga() computes an abstracted, coarse-grained (PAGA) graph of the neighborhood graph A Wolf

  • paga_compare() plot this graph next an embedding A Wolf

  • paga_path() plots a heatmap through a node sequence in the PAGA graph A Wolf

0.2.1 2017-07-24#

Scanpy includes preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing and simulation of gene regulatory networks. The implementation efficiently deals with datasets of more than one million cells. A Wolf, P Angerer

Version 0.1#

0.1.0 2017-05-17#

Scanpy computationally outperforms and allows reproducing both the Cell Ranger R kit’s and most of Seurat’s clustering workflows. A Wolf, P Angerer

Contents