Package: metacells
Metacells 0.9.4 - Single-cell RNA Sequencing AnalysisThe metacells package implements the improved metacell algorithm[1]for single-cell RNA sequencing (scRNA-seq) data
analysis within thescipyframework, and projection algorithm based on it[2]. The original
metacell algorithm[3]was implemented in R. The python package contains various algorithmic improvements and is
scalable for larger data sets (millions of cells).Metacell AnalysisNaively, scRNA_seq data is a set of cell profiles, where for each one, for each gene, we get a count of the mRNA
molecules that existed in the cell for that gene. This serves as an indicator of how “expressed” or “active” the gene
is.As in any real world technology, the raw data may suffer from technical artifacts (counting the molecules of two cells
in one profile, counting the molecules from a ruptured cells, counting only the molecules from the cell nucleus, etc.).
This requires pruning the raw data to exclude such artifacts.The current technology scRNA-seq data is also very sparse (typically <<10% the RNA molecules are counted). This
introduces large sampling variance on top of the original signal, which itself contains significant inherent biological
noise.Analyzing scRNA-seq data therefore requires processing the profiles in bulk. Classically, this has been done by directly
clustering the cells using various methods.In contrast, the metacell approach groups together profiles of the “same” biological state into groups of cells of the
“same” biological state, with theminimalnumber of profiles needed for computing robust statistics (in particular,
mean gene expression). Each such group is a single “metacell”.By summing profiles of cells of the “same” state together, each metacell greatly reduces the sampling variance, and
provides a more robust estimation of the transcription state. Note a metacell isnota cell type (multiple metacells
may belong to the same “type”, or even have the “same” state, if the data sufficiently over-samples this state). Also, a
metacell isnota parametric model of the cell state. It is merely a more robust description of some cell state.The metacells should therefore be further analyzed as if they were cells, using additional methods to classify cell
types, detect cell trajectories and/or lineage, build parametric models for cell behavior, etc. Using metacells as input
for such analysis techniques should benefit both from the more robust, less noisy input; and also from the (~100-fold)
reduction in the number of cells to analyze when dealing with large data (e.g. analyzing millions of individual cells).A common use case is taking a new data set and using an existing atlas with annotations (in particular, “type”
annotations) to provide initial annotations for the new data set. As of version 0.9 this capability is provided
by this package.Metacell projection provides a quantitative “projected” genes profile for each query metacell in the atlas, together
with a “corrected” one for the same subset of genes shared between the query and the atlas. Actual correction is
optional, to be used only if there are technological differences between the data sets, e.g. 10X v2 vs. 10X v3. This
allows performing a quantitative comparison between the projected and corrected gene expression profiles for determining
whether the query metacell is a novel state that does not exist in the atlas, or, if it does match an atlas state,
analyze any differences it may still have. This serves both for quality control and for quantitative analysis of
perturbed systems (e.g. knockouts or disease models) in comparison to a baseline atlas.Terminology and Results FormatNOTE: Version 0.9breaks compatibilitywith version 0.8 when it comes to some APIs and the names and semantics
of the result annotations. See below for the description of updated results (and how they differ from version 0.8). The
new format is meant to improve the usability of the system in downstream analysis pipelines. For convenience we also
list here the results of the new projection pipeline added in version 0.9.*. Versions 0.9.1 and 0.9.2 contain some bug
fixes. Version 0.9.3 allows specifying target UMIs for the metacells, in addition to the target size in cells, and
adaptively tries to satisfy both. This should produce better-sized metacells “out of the box” compared to the 0.9.[0-2]
versions. The latest published version, 0.9.4, contains minor bug fixes and updates for newer versions of dependency
packages.If you have existing metacell data that was computed using version 0.8 (the current published version you will get
from usingpip install metacells, you can use the providedconversion scriptscript to migrate your data to the format described below, while preserving any additional annotations you may have
created for your data (e.g. metacells type annotations). The script will not modify your existing data files, so you can
examine the results and tweak them if necessary.In the upcoming version 0.10 we will migrate from usingAnnDatato usingdafto represent the data (h5adfiles will still be supported, either directly through an adapter or via a conversion process). This will again
unavoidingly break API compatibility, but will provide many advantages over the restrictedAnnDataAPIs.We apologize for the inconvenience.Metacells ComputationIn theory, the only inputs required for metacell analysis are cell gene profiles with a UMIs count per gene per cell. In
practice, a key part of the analysis is specifying lists of genes for special treatment. We use the following
terminology for these lists:excluded_gene,excluded_cellmasksExcluded genes (and/or cells) are totally ignored by the algorithm (e.g. mytochondrial genes, cells with too few
UMIs).Deciding on the “right” list of excluded genes (and cells) is crucial for creating high-quality metacells. We rely
on the analyst to provide this list based on prior biological knowledge. To support this supervised task, we provide
theexcluded_genesandexclude_cellsfunctions which implement “reasonable” strategies for detecting some
(not all) of the genes and cells to exclude. For example, these will exclude any genes found byfind_bursty_lonely_genes, (calledfind_noisy_lonely_genesin v0.8). Additional considerations might be to
userelate_genesto (manually) exclude genes that are highly correlated with known-to-need-to-be-excluded genes,
or exclude any cells that are marked as doublets, etc.Currently the 1st step of the processing must be to create a “clean” data set which lacks the excluded genes and
cells (e.g. usingextract_clean_data). When we switch todafwe’ll just stay with the original data set and
apply the exclusion masks to the rest of the algorithm.lateral_genemaskLateral genes are forbidden from being selected for computing cells similarity (e.g., cell cycle genes). In version
0.8 these were called “forbidden” genes. Lateral genes are still counted towards the total UMIs count when computing
gene expression levels for cells similarity. In addition, lateral genes are still used to compute deviant (outlier)
cells. That is, each computed metacell should still have a consistent gene expression level even for lateral genes.The motivation is that we don’t want the algorithm to even try to create metacells based on these genes. Since these
genes may be very strong (again, cell cycle), they would overcome the cell-type genes we are interested in,
resulting in for example an “M-state” metacell which combines cells from several (similar) cell types.Deciding on the “right” list of lateral genes is crucial for creating high-quality metacells. We rely on the analyst
to provide this list based on prior biological knowledge. To support this supervised task, we provide therelate_genespipeline for identifying genes closely related to known lateral genes, so they can be added to the
list.noisy_genemaskNoisy genes are given more freedom when computing deviant (outlier) cells. That is, we don’t expect the expression
level of such genes in the cells in the same metacell to be as consistent as we do for regular (non-noisy) genes.
Note this isn’t related to the question of whether the gene is lateral of not. That is, a gee maybe lateral, noisy,
both, or neither.The motivation is that some genes are inherently bursty and therefore cause many cells which are otherwise a good
match for their metacell to be marked as deviant (outliers). An indication for this is by examining thedeviant_foldmatrix (see below).Deciding on the “right” list of noisy genes is again crucial for creating high-quality metacells (and minimizing the
fraction of outlier cells). Again we rely on the analyst here,Having determined the inputs and possibly tweaking the hyper-parameters (a favorite one is thetarget_metacell_size,
which by default is 160K UMIs; this may be reduced for small data sets and may be increased for larger data sets), one
typically runsdivide_and_conquer_pipelineto obtain the following:metacell(index) vs.metacell_name(string) per cellThe result of computing metacells for a set of cells with the above assigns each cell a metacell index. We also give
each metacell a name of the formatM<index>.<checksum>where the checksum reflects the cells grouped into the
metacell. This protects the analyst from mistakenly applying metadata assigned to metacells from an old computation
to different newly computed metacells.We provide functions (convey_obs_to_group,convey_group_to_obs) for conveying between per-cell and
per-metacell annotations, which all currently use the metacell integer indices (this will change when we switch todaf). The metacell string names are safer to use, especially when slicing the data.dissolvecells maskWhether the cell was in a candidate matecall that was dissolved due to being too small (too few cells and/or total
UMIs). This may aid quality control when there are a large number of outliers; lowering thetarget_metacell_sizemay help avoid this.selected_genemaskWhether each gene was ever selected to be used to compute the similarity between cells to compute the metacells.
When using the divide-and-conquer algorithm, this mask is different for each pile (especially in the second phase
when piles are homogeneous). This mask is the union of all the masks used in all the piles. It is useful for
ensuring no should-be-lateral genes were selected as this would reduce the quality of the metacells. If such genes
exist, add them to thelateral_genemask and recompute the metacells.Having computed the metacells, the next step is to runcollect_metacellsto create a newAnnDataobject for them
(when usingdaf, they will be created in the same dataset for easier analysis), which will contain all the per-gene
metadata, and also:Xper gene per metacellOnce the metacells have been computed (typically usingdivide_and_conquer_pipeline), we can collect the gene
expression levels profile for each one. The main motivation for computing metacells is that they allow for a robust
estimation of the gene expression level, and therefore we by default compute a matrix of gene fractions (which sum
to 1) in each metacell, rather than providing a UMIs count for each. This simplifies the further analysis of the
computed metacells (this is known ase_gcin the old R metacells package).Note that the expression level of noisy genes is less reliable, as we do not guarantee the cells in each metacell
have a consistent expression level for such genes. Our estimator therefore uses a normal weighted mean for most
genes and a normalized geometric mean for the noisy gene. Since the sizes of the cells collected into the same
metacell may vary, our estimator also ensures one large cell doesn’t dominate the results. That is, the computed
fractions arenotsimply “sum of the gene UMIs in all cells divided by the sum of all gene UMIs in all cells”.groupedper metacellThe number of cells grouped into each metacell.total_umisper metacell, and per gene per metacellWe still provide the total UMIs count for each each gene for each cell in each metacell, and the total UMIs in each
metacell. Note that the estimated fraction of each gene in the metacell isnotits total UMIs divided by the
metacell’s total UMIs; the actual estimator is more complex.The total UMIs are important to ensure that analysis is meaningful. For example, comparing expression levels of
lowly-expressed genes in two metacells will yield wildly inaccurate results unless a sufficient number of UMIs were
used (the sum of UMIs of the gene in both compared metacells). The functions provided here for computing fold
factors (log base 2 of the ratio) and related comparisons automatically ignore cases when this sum is below some
threshold (40) by considering the effective fold factor to be 0 (that is, “no difference”).metacells_levelper cell or metacellThis is 0 for rare gene module metacells, 1 for metacells computed from the main piles in the 2nd divide-and-conquer
phase and 2 for metacells computed for their outliers.If usingdivide_and_conquer_pipeline, the following are also computed (but not by the simplecompute_divide_and_conquer_metacells:rare_gene_module_<N>mask (for N = 0, …)A mask of the genes combined into each of the detected “rare gene modules”. This is done in (expensive)
pre-processing before the full divide-and-conquer algorithm to increase the sensitivity of the method, by creating
metacells computed only from cells that express each rare gene module.rare_genemaskA mask of all the genes in all the rare gene modules, for convenience.rare_gene_moduleper cell or metacellThe index of the rare gene module each cell or metacell expresses (or negative for the common case it expresses none
of them).rare_cell,rare_metacellmasksA mask of all the cells or metacells expressing any of the rare gene modules, for convenience.In theory one is free to go use the metacells for further analysis, but it is prudent to perform quality control first.
One obvious measure is the number of outlier cells (with a negative metacell index and a metacell name ofOutliers).
In addition, one should compute and look at the following (an easy way to compute all of them at once is to callcompute_for_mcview, this will change in the future):most_similar,most_similar_nameper cell (computed bycompute_outliers_most_similar)For each outlier cell (whose metacell index is-1and metacell name isOutliers), the index and name of the
metacell which is the “most similar” to the cell (has highest correlation).deviant_foldper gene per cell (computed bycompute_deviant_folds)For each cell, for each gene, thedeviant_foldholds the fold factor (log base 2) between the expression level
of the gene in the cell and the metacell it belongs to (or the most similar metacell for outlier cells). This uses
the same (strong) normalization factor we use when computing deviant (outlier) cells, so for outliers, you should
see some (non-excluded, non-noisy) genes with a fold factor above 3 (8x), or some (non-excluded, noisy) genes with a
fold factor above 5 (32x), which justify why we haven’t merged that cell into a metacell; for cells grouped into
metacells, you shouldn’t see (many) such genes. If there is a large number of outlier cells and a few non-noisy
genes have a high fold factor for many of them, you should consider marking these genes as noisy and recomputing the
metacells. If they are already marked as noisy, you may want to completely exclude them.inner_foldper gene per metacell (computed bycompute_inner_folds)For each metacell, for each gene, theinner_foldis the strongest (highest absolute value)deviant_foldof
any of the cells contained in the metacell. Both this and theinner_stdev_logbelow can be used for quality
control over the consistency of the gene expression in the metacell.significant_inner_folds_countper geneFor each gene, the number of metacells in which there’s at least one cell with a highdeviant_fold(that is,
where theinner_foldis high). This helps in identifying troublesome genes, which can be then marked as noisy,
lateral or even excluded, depending on their biological significance.inner_stdev_logper gene per metacell (computed bycompute_inner_stdev_logs)For each metacell, for each gene, the standard deviation of the log (base 2) of the fraction of the gene across the
cells of the metacell. Ideally, the standard deviation should be ~1/3rd of thedeviants_min_gene_fold_factor(which is3by default), indicating that (all)most cells are within that maximal fold factor. In practice we may
see higher values - the lower, the better. Both this and theinner_foldabove can be used for quality control over the consistency of the gene expression in the metacell.marker_genemask (computed byfind_metacells_marker_genes)Given the computed metacells, we can identify genes that have a sufficient number of effective UMIs (in some
metacells) and also have a wide range of expressions (between different metacells). These genes serve as markers for
identifying the “type” of the metacell (or, more generally, the “gene programs” that are active in each metacell).Typically analysis groups the marker genes into “gene modules” (or, more generally, “gene programs”), and then use
the notion of “type X expresses the gene module/programs Y, Z, …”. As of version 0.9, collecting such gene modules
(or programs) is left to the analyst with little or no direct support in this package, other than providing the rare
gene modules (which by definition would apply only to a small subset of the metacells).x,yper metacell (computed bycompute_umap_by_markers)A common and generally effective way to visualize the computed metacells is to project them to a 2D view. Currently
we do this by giving UMAP a distance metric between metacells based on a logistic function based on the expression
levels of the marker genes. In version 0.8 this was based on picking (some of) the selected genes.This view is good for quality control. If it forces “unrelated” cell types together, this might mean that more genes
should be made lateral, or noisy, or even excluded; or maybe the data contains a metacell of doublets; or metacells
mixing cells from different types, if too many genes were marked as lateral or noisy, or excluded. It takes a
surprising small number of such doublet/mixture metacells to mess up the UMAP projection.Also, one shouldn’t read too much from the 2D layout, as by definition it can’t express the “true” structure of the
data. Looking at specific gene-gene plots gives much more robust insight into the actual differences between the
metacell types, identify doublets, etc.obs_outgoing_weightsper metacell per metacell (also computed bycompute_umap_by_markers)The (sparse) matrix of weights of the graph used to generate thexandy2D projection. This graph isverysparse, that is, has a very low degree for the nodes. It is meant to be used only in conjunction with the 2D
coordinates for visualization, and shouldnotbe used by any downstream analysis to determine which metacells
are “near” each other for any other purpose.Metacells ProjectionFor the use case of projecting metacells we use the following terminology:atlasA set of metacells with associated metadata, most importantly atypeannotation per metacell. In addition, the
atlas may provide anessential_gene_of_<type>mask for each type. For a query metacell to successfully project
to a given type will require that the query’s expression of the type’s essential genes matches the atlas. We also
use the metadata listed above (specifically,lateral_gene,noisy_geneandmarker_gene).queryA set of metacells with minimal associated metadata, specifically without atype. This may optionally contain
its ownlateral_gene,noisy_geneand/or evenmarker_geneannotations.ignored_genemask,ignored_gene_of_<type>maskA set of genes to not even try to match between the query and the atlas. In general the projection matches only a
subset of the genes (that are common to the atlas and the query). However, the analyst has the option to force
additional genes to be ignored, either in general or only when projecting metacells of a specific type. Manually
ignoring specific genes which are known not to match (e.g., due to the query being some experiment, e.g. a knockout
or a disease model) can improve the quality of the projection for the genes which do match.Given these two input data sets, theprojection_pipelinecomputes the following (inside the queryAnnDataobject):atlas_genemaskA mask of the query genes that also exist in the atlas. We match genes by their name; if projecting query data from
a different technology, we expect the caller to modify the query gene names to match the atlas before projecting
it.atlas_lateral_gene,atlas_noisy_gene,atlas_marker_gene,essential_gene_of_<type>masksThese masks are copied from the atlas to the query (restricting them to the commonatlas_genesubset).projected_noisy_geneThe mask of the genes that were considered “noisy” when computing the projection. By default this is the union
of the noisy atlas and query genes.corrected_fractionper gene per query metacellFor eachatlas_gene, its fraction in each query metacell, out of only the atlas genes. This may be further
corrected (see below) if projecting between different scRNA-seq technologies (e.g. 10X v2 and 10X v3). For
non-atlas_genethis is 0.projected_fractionper gene per query metacellFor eachatlas_gene, its fraction in its projection on the atlas. This projection is computed as a weighted
average of some atlas metacells (see below), which are all sufficiently close to each other (in terms of gene
expression), so averaging them is reasonable to capture the fact the query metacell may be along some position on
some gradient that isn’t an exact match for any specific atlas metacell. For non-atlas_genethis is 0.total_atlas_umisper query metacellThe total UMIs of theatlas_genein each query metacell. This is used in the analysis as described fortotal_umisabove, that is, to ensure comparing expression levels will ignore cases where the total number of
UMIs of both compared gene profiles is too low to make a reliable determination. In such cases we take the fold
factor to be 0.weightsper query metacell per atlas metacsllThe weights used to compute theprojected_fractions. Due toAnnDatalimitations this is returned as a
separate object, but indafwe should be able to store this directly into the query object.In theory, this would be enough for looking at the query metacells and comparing them to the atlas, and to project
metadata from the atlas to the query (e.g., the metacell type) usingconvey_atlas_to_query. In practice, there is
significant amount of quality control one needs to apply before accepting these results, which we compute as follows:correction_factorper geneIf projecting a query on an atlas with different technologies (e.g., 10X v3 to 10X v2), an automatically computed
factor we multiplied the query gene fractions by to compensate for the systematic difference between the
technologies (1.0 for uncorrected genes and 0.0 for non-atlas_gene).projected_typeper query metacellFor each query metacell, the best atlastypewe can assign to it based on its projection. Note this does not
indicate that the query metacell is “truly” of this type; to make this determination one needs to look at the
quality control data below.projected_secondary_typeper query metacellIn some cases, a query metacell may fail to project well to a single region of the atlas, but does project well to a
combination of two distinct atlas regions. This may be due to the query metacell containing doublets, of a mixture
of cells which match different atlas regions (e.g. due to sparsity of data in the query data set). Either way, if
this happens, we place here the type that best describes the secondary region the query metacell was projected to;
otherwise this would be the empty string. Note that theweightsmatrix above does not distinguish between the
regions.fitted_gene_of_<type>maskFor each type, the genes that were projected well from the query to the atlas for most cells of that type; anyatlas_geneoutside this mask failed to project well from the query to the atlas for most metacells of this type.
For non-atlas_genethis is set toFalse.Whether failing to project well some of theatlas_genefor most metacells of someprojected_typeindicates
that they aren’t “truly” of that type is a decision which only the analyst can make based, on prior biological
knowledge of the relevant genes.fittedmask per gene per query metacellFor eachatlas_genefor each query metacell, whether the gene was expected to be projected well, based on the
query metacellprojected_type(and theprojected_secondary_type, if any). For non-atlas_genethis is set
toFalse. This does not guarantee the gene was actually projected well.misfitmask per gene per query metacellFor eachatlas_genefor each query metacell, whether thecorrected_fractionof the gene was significantly
different from theprojected_fractions(that is, whether the gene was not projected well for this metacell). For
non-atlas_genethis is set toFalse, to make it easier to identify problematic genes.This is expected to be rare forfittedgenes and common for the rest of theatlas_gene. If too manyfittedgenes are alsomisfit, then one should be suspicious whether the query metacell is “truly” of theprojected_type.essentialmask per gene per query metacellWhich of theatlas_genewere also listed in theessential_gene_of_<type>for theprojected_type(and
also theprojected_secondary_type, if any) of each query metacell.If anessentialgene is also amisfitgene, then one should be very suspicious whether the query metacell is
“truly” of theprojected_type.projected_correlationper query metacellThe correlation between between thecorrected_fractionand theprojected_fractionfor only thefittedgenes expression levels of each query metacell. This serves as a very rough estimator for the quality of the
projection for this query metacell (e.g. can be used to compute R^2 values).In general we expect high correlation (more than 0.9 in most metacells) since we restricted thefittedgenes
mask only to genes we projected well.projected_foldper gene per query metacellThe fold factor between thecorrected_fractionand theprojected_fraction(0 for non-atlas_gene). If
the absolute value of this is high (3 for 8x ratio) then the gene was not projected well for this metacell. This
will be 0 for non-atlas_gene.It is expected this would have low values for mostfittedgenes and high values for the rest of theatlas_gene, but specific values will vary from one query metacell to another. This allows the analyst to make
fine-grained determination about the quality of the projection, and/or identify quantitative differences between the
query and the atlas (e.g., when studying perturbed systems such as knockouts or disease models).similarmask per query metacellA conservative determination of whether the query metacell is “similar” to its projection on the atlas. This is
based on whether the number ofmisfitfor the query metacell is low enough (by default, up to 3 genes), and also
that at least 75% of theessentialgenes of the query metacell were notmisfitgenes. Note that this
explicitly allows for aprojected_secondary_type, that is, a metacell of doublets will be “similar” to the
atlas, but a metacell of a novel state missing from the atlas will be “dissimilar”.The final determination of whether to accept the projection is, as always, up to the analyst, based on prior
biological knowledge, the context of the collection of the query (and atlas) data sets, etc. The analyst need not
(indeed,should not) blindly accept thesimilardetermination without examining the rest of the quality
control data listed above.InstallationIn short:pip install metacells. Note thatmetacellsrequires many “heavy” dependencies, most notablynumpy,pandas,scipy,scanpy, whichpipshould automatically install for you. If you are running inside acondaenvironment, you might prefer to use it to first install these dependencies, instead of havingpipinstall
them fromPyPI.Note thatmetacellsonly runs natively on Linux and MacOS. To run it on a Windows computer, you must activateWindows Subsystem for Linuxand installmetacellswithin it.The metacells package contains extensions written in C++. Themetacellsdistribution provides pre-compiled Python
wheels for both Linux and MacOS, so installing it usingpipshould not require a C++ compilation step.Note that for X86 CPUs, these pre-compiled wheels were built to use AVX2 (Haswell/Excavator CPUs or newer), and will not
work on older CPUs which are limited to SSE. Also, these wheels will not make use of any newer instructions (such as
AVX512), even if available. While these wheels may not the perfect match for the machine you are running on, they are
expected to work well for most machines.To see the native capabilities of your machine, you cangrep flags /proc/cpuinfo | head-1which will give you a
long list of supported CPU features in an arbitrary order, which may includesse,avx2,avx512, etc. You can
therefore simplygrep avx2 /proc/cpuinfo | head-1to test whether AVX2 is/not supported by your machine.You can avoid installing the pre-compiled wheel by runningpip install metacells--no-binary:all:. This will forcepipto compile the C++ extensions locally on your machine, optimizing for its native capabilities, whatever these
may be. This will take much longer but may give yousomewhatfaster results (note: the results willnotbe exactly
the same as when running the precompiled wheel due to differences in floating-point rounding). Also, this requires you
to have a C++ compiler which supports C++14 installed (eitherg++orclang). Installing a C++ compiler depends
on your specific system (usingcondamay make this less painful).VignettesThe latest vignettes can be foundhere.ReferencesPlease cite the references appropriately in case they are used:[1]Ben-Kiki, O., Bercovich, A., Lifshitz, A. et al. Metacell-2: a divide-and-conquer metacell algorithm for scalable
scRNA-seq analysis. Genome Biol 23, 100 (2022).https://doi.org/10.1186/s13059-022-02667-1[2]Ben-Kiki, O., Bercovich, A., Lifshitz, A. et al. MCProj: metacell projection for interpretable and quantitative
use of transcriptional atlases. Genome Biol 24, 220 (2023).https://doi.org/10.1186/s13059-023-03069-7[3]Baran, Y., Bercovich, A., Sebe-Pedros, A. et al. MetaCell: analysis of single-cell RNA-seq data using K-nn graph
partitions. Genome Biol 20, 206 (2019).10.1186/s13059-019-1812-2License (MIT)Copyright © 2020-2023 Weizmann Institute of SciencePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the “Software”), to deal in the Software without restriction, including without limitation the
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COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.History0.5First published version.0.6More robust graph partition.Allow forcing feature genes.Rename “project” to “convey” to prepare for addition of atlas projection functionality.0.7.0Switch to new project template.Fix some edge cases in the pipeline.Switch to usingpsutilfor detecting system resources.Fix binary wheel issues.Give up on using travis-ci.0.8.0Add inner fold factor computation for metacells quality control.Add deviant fold factor computation for metacells quality control.Add projection of query data onto an atlas.Self-adjusting pile sizes.Add convenience function for computing data for MCView.Better control over filtering using absolute fold factors.Fix edge case in computing noisy lonely genes.Additional outliers certificates.Stricter deviants detection policy0.9.0Improved and published projection algorithm.Restrict number of rare gene candidates.Tighter control over metacells size and internal quality.Improved divide-and-conquer strategy.Base deviants (outliers) on gaps between cells.Terminology changes (see the README for details).Projection!0.9.1Fix build for python 3.11.More robust gene selection, KNN graph creation, and metacells partition.More thorough binary wheels.0.9.2Fix numpy compatibility issue.Fix K of UMAP skeleton KNN graph (broken in 0.9.1).0.9.3Allow specifying both target UMIs and target size (in cells) for the metacells, and adaptively try to
satisfy both. This should produce better-sized metacells “out of the box” compared to 0.9.[0-2].0.9.4Fix minor bug in regularization of metacell fractions.Fix issue with canonical sparse matrices after downsampling (probably due to scipy.sparse updates?)Fix using deprecated AnnData APIs.