diff --git a/joss.05610/10.21105.joss.05610.crossref.xml b/joss.05610/10.21105.joss.05610.crossref.xml new file mode 100644 index 0000000000..a977e2e608 --- /dev/null +++ b/joss.05610/10.21105.joss.05610.crossref.xml @@ -0,0 +1,413 @@ + + + + 20240112T221014-ee78979829443840087bf1da0c3c99433a95de76 + 20240112221014 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 01 + 2024 + + + 9 + + 93 + + + + cellanneal: A user-friendly deconvolution software for +transcriptomics data + + + + Lisa + Buchauer + https://orcid.org/0000-0002-4722-8390 + + + Shalev + Itzkovitz + https://orcid.org/0000-0003-0685-2522 + + + + 01 + 12 + 2024 + + + 5610 + + + 10.21105/joss.05610 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10405043 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5610 + + + + 10.21105/joss.05610 + https://joss.theoj.org/papers/10.21105/joss.05610 + + + https://joss.theoj.org/papers/10.21105/joss.05610.pdf + + + + + + Benchmarking of cell type deconvolution +pipelines for transcriptomics data + Cobos + Nature communications + 1 + 11 + 10.1038/s41467-020-19015-1 + 2020 + Cobos, F. 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Terminal +differentiation of villus-tip enterocytes is governed by distinct +members of tgf\beta superfamily. bioRxiv, 2022–2011. +https://doi.org/10.1101/2022.11.11.516138 + + + A single cell atlas of the human liver tumor +microenvironment + Massalha + Mol. Syst. Biol. + 12 + 16 + 10.15252/msb.20209682 + 2020 + Massalha, H., Bahar Halpern, K., +Abu-Gazala, S., Jana, T., Massasa, E. E., Moor, A. E., Buchauer, L., +Rozenberg, M., Pikarsky, E., Amit, I., Zamir, G., & Itzkovitz, S. +(2020). A single cell atlas of the human liver tumor microenvironment. +Mol. Syst. Biol., 16(12), e9682. +https://doi.org/10.15252/msb.20209682 + + + + + + diff --git a/joss.05610/10.21105.joss.05610.jats b/joss.05610/10.21105.joss.05610.jats new file mode 100644 index 0000000000..d5b7b521d1 --- /dev/null +++ b/joss.05610/10.21105.joss.05610.jats @@ -0,0 +1,812 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5610 +10.21105/joss.05610 + +cellanneal: A user-friendly +deconvolution software for transcriptomics data + + + +https://orcid.org/0000-0002-4722-8390 + +Buchauer +Lisa + + + +* + + +https://orcid.org/0000-0003-0685-2522 + +Itzkovitz +Shalev + + + + + +Department of Molecular Cell Biology, Weizmann Institute of +Science, Rehovot, Israel + + + + +Department of Infectious Diseases and Respiratory Medicine, +Charité-Universitätsmedizin Berlin, Berlin, Germany + + + + +* E-mail: + + +12 +6 +2023 + +9 +93 +5610 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +bioinformatics +computational biology +mixture deconvolution +bulk deconvolution +transcriptomics +RNA sequencing +omics methods + + + + + + Summary +

Single-cell sequencing methods enable precise characterization of + gene expression patterns in individual cells. However, they may + provide inaccurate information about the cell type composition of + samples, as required preprocessing procedures such as tissue + dissociation or cell sorting affect viability of different cell types + to varying extent + (Erdmann-Pham + et al., 2021). Further, especially in the clinical context, + single-cell sequencing of patient samples is currently not routinely + applied because of high cost and required expertise, while bulk + sequencing is more prevalent.

+

For these reasons, computational deconvolution methods are gaining + popularity in basic and clinical research. Computational deconvolution + approaches infer the cell type proportions constituting a given bulk + RNA sample based on separately obtained cell type reference data. + Several computational deconvolution methods have been developed in the + last decade and have contributed to our understanding of tissue + composition + (Cobos + et al., 2020; + Sturm + et al., 2019). Generally, during deconvolution, the + computational mixture is constructed from a set of cell type fractions + and reference gene expression vectors for each of the participating + cell types, most commonly derived from single-cell data. The cell type + fractions are then iteratively changed until agreement between the + in silico gene expression vector and the observed + bulk sample gene expression vector is optimal by a measure of choice. + Here, published methods rely almost exclusively on minimizing the sum + of squared residuals between bulk and computationally mixed vectors. + Algorithms for such optimization problems are readily available and + include variants of least squares regression (e.g. weighted least + squares regression + (Racle + et al., 2017), non-negative least squares regression + (Jew + et al., 2020; + Wang + et al., 2019) or least trimmed squares + (Hao + et al., 2019)) and support vector regression + (Newman + et al., 2015, + 2019).

+

However, least squares-based optimization is faced with a + particular challenge in bulk RNAseq deconvolution because of the + highly skewed nature of mRNA copy number distributions, ranging from + less than 1 to more than 10,000 average mRNA copies per cell + (Li + et al., 2016; + Schwanhäusser + et al., 2011). In such settings, optimization results may be + strongly influenced by few highly expressed genes and are thus not + robust to noise or platform effects influencing the readout of these + genes. Support vector regression based models like CIBERSORT + (Newman + et al., 2015) perform gene feature selection out of a + user-defined signature gene list, the contents of which can strongly + affect the cell proportion estimates. Overall, identifying the right + genes for deconvolution becomes a task in itself + (Aliee + & Theis, 2021). As a result, deconvolution methods may + yield inferred mixed gene expression vectors that do not correlate + well with measured bulk gene expression.

+

Here, we introduce cellanneal, a + python-based software for deconvolving bulk RNA sequencing data. + cellanneal relies on the optimization of + Spearman’s rank correlation coefficient between experimental and + computational mixture gene expression vectors using simulated + annealing. Transforming gene expression values into ranks prior to + optimization allows genes of different expression magnitudes to + contribute similarly to deconvolution; further, + cellanneal employs a permissive gene selection + procedure that includes as many informative genes as possible. + Together, these approaches limit the influence of highly expressed + genes on the one hand and reduce dependency on specific gene list + choices. cellanneal can be used as a python + package or via a command line interface, but importantly also provides + a simple graphical user interface which is distributed as a single + executable file for user convenience.

+ + + Statement of need +

Making sense of bulk RNA sequencing datasets often requires + analysis of the cell type composition of the samples. This is + particularly relevant in clinical samples that analyze the + transcriptome of tissues or tumors which consist of epithelial, + stromal and immune cell types. In parallel, publicly available + single-cell data sets enable precise characterization of the + expression signature of multiple individual cell types. However, + software tools for computational bulk deconvolution are often slow, + non-robust and not easy to use. Some existing methods address the + aspect of user-friendliness by providing graphical web interfaces, but + submitting sensitive medical data to an external web server is not + always compatible with privacy legislation.

+

To address these challenges, we have developed + cellanneal, a deconvolution approach that uses + Spearman’s rank correlation coefficient between synthetic and bulk + gene expression vectors as the optimization procedure’s objective + function. Because this correlation measure is calculated from ranks + rather than absolute data values, each gene influences the + optimization result to a similar extent. Users are encouraged to + include as many informative genes as possible in the analysis. + cellanneal optimizes cell type fractions by + simulated annealing, a flexible, rapid and robust algorithm for global + optimization + (Kirkpatrick + et al., 1983; + Virtanen + et al., 2020). cellanneal can be used as + a python package, via its command line interface or via a + user-friendly graphical software which runs locally. Its typical + processing time for one mixture sample is below one minute on a + desktop machine (MacBook Pro 2020, 2.3 GHz Quad-Core Intel Core i7, 16 + GB RAM).

+ + + Availability and Features +

The python package and command line interface are available at + https://github.com/LiBuchauer/cellanneal + and can be installed using pip. The graphical + software for Microsoft Windows and MacOS can be downloaded at + http://shalevlab.weizmann.ac.il/resources + and does not require installation. Instructions for installation and + use as well as general documentation is available at + https://github.com/LiBuchauer/cellanneal.

+

The python package provides functions for the three main steps of a + deconvolution analysis with cellanneal: + identification of a gene set for deconvolution, deconvolution using + simulated annealing, and plotting the results. A quick start workflow + is available as part of the documentation. For the command line + interface and the graphical user interface, these three steps are + combined into one call (click).

+

cellanneal runs which were started from + either the command line or the graphical user interface produce a + collection of result files including tabular deconvolution results + (cell type fractions for each sample) and figures illustrating these + cell type distributions. Further, cellanneal + computes and stores the gene-wise fold change between the observed + bulk expression and the estimated expression based on the inferred + cell type composition. This enables identifying genes for which + expression may be specifically induced or inhibited in the bulk sample + compared to the single cell reference. Such genes may be of biological + or medical interest. + Figures produced by cellanneal include a + heatmap showing sample compositions + ([fig:heatmap]), + pie charts showing sample compositions + ([fig:pie]), and + scatter plots showing correlation between experimental bulk gene + expression values and their cellanneal-derived + counterparts from the best identified computational mixture + ([fig:scatter]). + The examples presented in this manuscript use data from + (Massalha + et al., 2020).

+ +

A heatmap produced by cellanneal. + Constituting cell types are on the y-axis, deconvolved bulk sample + names on the x-axis. The colour scale shows the fractional presence + of cell type in each + bulk.

+ + + +

Pie charts produced by + cellanneal. Each pie corresponds to one + deconvolved bulk sample from the input + data.

+ + + +

Gene correlation scatter plots produced by + cellanneal Each panel corresponds to one + deconvolved bulk sample from the input data. Each dot represents a + gene used during deconvolution. The x-axis shows the experimentally + measured expression of each gene after normalizing so that the total + count sum is 1. The y-axis shows the normalized expression of each + gene in the best identified synthetic bulk mixed from cell type + signature data + .

+ + +

cellanneal relies on the python packages + scipy + (Virtanen + et al., 2020), numpy + (Harris + et al., 2020), pandas + (Pandas + Development Team, 2020), seaborn + (Waskom, + 2021) and matplotlib + (Hunter, + 2007).

+ + + Citations +

Examples of published research projects using cellanneal include + (Egozi + et al., 2023) and + (Berková + et al., 2022).

+ + + Acknowledgements +

We thank all members of the Itzkovitz lab for valuable feedback and + for testing the software.

+

L.B. was supported by the European Molecular Biology Organization + under EMBO Long-Term Fellowship ALTF 724-2019. S.I. is supported by + the Wolfson Family Charitable Trust, the Edmond de Rothschild + Foundations, the Fannie Sherr Fund, the Helen and Martin Kimmel + Institute for Stem Cell Research grant, the Minerva Stiftung grant, + the Israel Science Foundation grant No. 1486/16, the Broad + Institute‐Israel Science Foundation grant No. 2615/18, the European + Research Council (ERC) under the European Union’s Horizon 2020 + research and innovation program grant No. 768956, the Chan Zuckerberg + Initiative grant No. CZF2019‐002434, the Bert L. and N. Kuggie Vallee + Foundation and the Howard Hughes Medical Institute (HHMI) + international research scholar award.

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