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Genes2Genes documentation master file, created by + sphinx-quickstart on Wed Aug 16 09:01:25 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Genes2Genes +======================================= + +**A framework for single-cell pseudotime trajectory alignment** + +.. contents:: Contents + +Genes2Genes (G2G) is a new Python framework for aligning single-cell pseudotime trajectories of gene expression between any reference and query for a pairwise comparison such as: + + * Organoid vs. Reference tissue + * Control vs. Treatment + * Healthy vs. Disease + +Trajectory alignment: What & Why? +########### + +A single-cell trajectory describes the transcriptomic state of cells along some axis of progression (such as time), due to undergoing some dynamic process (e.g. cell differentiation, treatment response, or disease infection). Given an scRNA-seq profile, there are various tools available today to infer such trajectory by estimating a pseudo ordering of the cells along an axis, commonly referred to as 'pseudotime'. The pseudotime axis of a trajectory can be descritized to represent it as a sequence of discrete time points. Given two such discrete pseudotime sequences of two trajectories, a pairwise alignment between them defines a non-linear mapping between their time points. This mapping could have 1-to-1 matches as well as 1-to-many/many-to-1 matches (a.k.a warps) between the time points, while unmapping the time points which have significantly different transcriptomic states. Below is an example visualization of two cell differentiation trajectories. + + +.. image:: images/DocFigs1.png + :width: 600 + :alt: What is trajectory alignment? + :align: center + +For two trajectories representing single lineages as above, G2G generates an **optimal pairwise trajectory alignment** that captures the matches and mismatches between their time points in sequential order, allowing a user to quantify the degree of similarity between them. + +.. image:: images/DocFigs1-1.png + :width: 600 + :alt: Example mapping + :align: center +| +G2G defines 5 different states of alignment between any two **R** and **Q** time points, corresponding to all possible match and mismatch states. They are: 1-to-1 match (``M``), 1-to-many match (``V``), many-to-1 match (``W``), insertion (``I``) and deletion (``D``). Here, ``I`` or ``D`` refer to a mismatched time point in Q or R, respectively. These states jointly cover the alignment states defined in classical dynamic time warping and biological sequence alignment. + +.. image:: images/DocFigs1-3.png + :width: 600 + :alt: 5 states of alignment + :align: center +| +Accordingly, we can describe any trajectory alignment as a 5-state alignment string. For example, the 5-state alignment string of the above illustrated trajectory alignment is: + +.. code-block:: + + IIIMMMWWWIIIDDDMMMIIIIDDDD + +This G2G alignment string enables us to identify the time regions of match and mismatch along the trajectories. For instance, we can interpret the above illustrated alignment as follow -- *R and Q trajectories have mid and late mismatches, with the early stage of Q being mismatched, yet starting to match to the early stage of R at the middle of Q's trajectory. Overall, there are 9 R and Q pseudotime pairs getting matched (with 34.62% alignment similarity)*. + + +Outputs from Genes2Genes +########### + +Given an scRNA-seq dataset with their pseudotime estimates and a specified set of genes (e.g. all transcription factors, highly variable genes, biological/signaling pathway genes), G2G generates fully-descriptive alignments for each gene (i.e. **gene-level alignment**), as well as an average (aggregate) alignment (i.e. **cell-level alignment**) across all genes. + +Below is an example gene-level alignment of the gene *JUNB* in T cell differentiation between a pan-fetal reference and an artificial thymic organoid system: + +.. image:: images/DocFigs2.png + :width: 600 + :alt: Example gene-level alignment? + :align: center +| +.. code-block:: + + IIIIIDMMMMMMMMIDDDDD + +Each gene-level alignment carries its 5-state string, an alignment similarity percentage statistic, and the optimal alignment cost (in *nits* -- the unit measure of information). For the above gene, the aligment similarity is 40%, and the total cost of alignment is 53.47 nits. When the degree of difference in gene expression between the reference and query is high, the alignment cost will also be high. + +G2G uses the inferred gene-level alignments to inform: + +#. **The degree of similarity between the two profiles** as an average percentage of alignment similarity across all the genes tested, +#. **An aggregate cell-level alignment across all genes** to inform the average states of match and mismatch between the two profiles (again represented by a 5-state string), +#. **A ranked list of genes across time (from the most distant to most similar)** based on their alignment similarity percentage statistic, +#. **The diversity of different alignment patterns in genes**, by clustering gene-level alignments to identify different matching and mismatching patterns along time, + +between the two single-cell reference and query profiles in comparison. + +For further downstream analysis, G2G provides a wrapper function to check gene-set overrepresentation analysis of the identified gene-clusters and the list of the top distant (differentially-expressed) genes across time, using `GSEApy `_ Enrichr interface. The user is also able to compute an average alignment across any gene subset of their interest. + +**Note**: G2G has been developed only for single-lineage trajectory comparison. In the case of a trajectory with multiple branches, we recommend separating out the singe-lineage branches before any pairwise comparison. + +Our approach to trajectory alignment +########### + +We employ a **dynamic programming** (DP) based algorithm that can capture both matches and mismatches in gene expression in a unified way. This combines the classical **Gotoh's algorithm** for biological sequence alignment with **dynamic time warping**. Our DP algorithm uses a **Bayesian information-theoretic scoring scheme** based on the **minimum message length** criterion to generate an optimal alignment between two gene trajectories. This scheme evaluates the distributional similarity of gene expression between R and Q for each pair of time points, in terms of both their mean and variance of expression modelled as Gaussian distributions. +For more details on the methods, please see our `manuscript `_. + +Getting started +=========== + +For now, G2G needs to be installed from GitHub: + +.. code-block:: shell + + pip install git+https://github.com/Teichlab/Genes2Genes.git + +The package will be made available soon on PyPi. + +**Input to Gene2Genes** + +G2G takes reference and query input data as ``anndata`` objects, where each ``adata`` object has: + +* log1p normalised gene expression stored at ``adata.X`` +* pseudotime estimates of the cells stored as ``adata.obs['time']`` + +The user can estimate pseudotime of the cells in their datasets using any suitable method available (such as `Diffusion pseudotime `_, `CellRank `_, `Palantir `_, `GPLVM `_ etc.). +For better visualisation and interpretation of the alignment results, we recommend the data to be annotated with their cell types (manually and/or using an automatic annotation tool such as `CellTypist `_). + +Please refer to our `Tutorial `_ for an example analysis between a reference and query dataset from literature. + +Citing Genes2Genes +=========== +Our manuscript is currently available as a `preprint `_ at bioRxiv: + +*Sumanaweera, D., Suo, C., Cujba, A.M., Muraro, D., Dann, E., Polanski, K., Steemers, A.S., Lee, W., Oliver, A.J., Park, J.E. and Meyer, K.B., 2023.* **Gene-level alignment of single cell trajectories**. *bioRxiv, pp.2023-03.* + +This publication is part of the `Human Cell Atlas `_ + +Funding Acknowledgement +=========== +Marie Skłodowska-Curie grant agreement No: 101026506 (Marie Curie Individual Fellowship) under the European Union’s Horizon 2020 research and innovation programme; Wellcome Trust Ph.D. Fellowship for Clinicians; Wellcome Trust (WT206194); ERC Consolidator Grant (646794); Wellcome Sanger Institute’s Translation Committee Fund. + + +.. toctree:: + :hidden: + + self diff --git a/_sources/source/.ipynb_checkpoints/G2Gtutorial-checkpoint.rst.txt b/_sources/source/.ipynb_checkpoints/G2Gtutorial-checkpoint.rst.txt new file mode 100644 index 0000000..57facf0 --- /dev/null +++ b/_sources/source/.ipynb_checkpoints/G2Gtutorial-checkpoint.rst.txt @@ -0,0 +1,11 @@ + +.. _G2Gtutorial: +Tutorial +======================================= + + +Load the reference and query datasets: + + + + diff --git a/_sources/source/.ipynb_checkpoints/index-checkpoint.rst.txt b/_sources/source/.ipynb_checkpoints/index-checkpoint.rst.txt new file mode 100644 index 0000000..83b7b91 --- /dev/null +++ b/_sources/source/.ipynb_checkpoints/index-checkpoint.rst.txt @@ -0,0 +1,124 @@ +.. Genes2Genes documentation master file, created by + sphinx-quickstart on Wed Aug 16 09:01:25 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Genes2Genes +======================================= + +**A framework for single-cell pseudotime trajectory alignment** + +.. contents:: Contents + +Genes2Genes (G2G) is a new Python framework for aligning single-cell pseudotime trajectories of gene expression between any reference and query for a pairwise comparison such as: + + * Organoid vs. Reference tissue + * Control vs. Treatment + * Healthy vs. Disease + +Trajectory alignment: What & Why? +########### + +A single-cell trajectory describes the transcriptomic state of cells along some axis of progression (such as time), due to undergoing some dynamic process (e.g. cell differentiation, treatment response, or disease infection). Given an scRNA-seq profile, there are various tools available today to infer such trajectory by estimating a pseudo ordering of the cells along an axis, commonly referred to as 'pseudotime'. The pseudotime axis of a trajectory can be descritized to represent it as a sequence of discrete time points. Given two such discrete pseudotime sequences of two trajectories, a pairwise alignment between them defines a non-linear mapping between their time points. This mapping could have 1-to-1 matches as well as 1-to-many/many-to-1 matches (a.k.a warps) between the time points, while unmapping the time points which have significantly different transcriptomic states. Below is an example visualization of two cell differentiation trajectories. + + +.. image:: images/DocFigs1.png + :width: 600 + :alt: What is trajectory alignment? + :align: center + +For two trajectories representing single lineages as above, G2G generates an **optimal pairwise trajectory alignment** that captures the matches and mismatches between their time points in sequential order, allowing a user to quantify the degree of similarity between them. + +.. image:: images/DocFigs1-1.png + :width: 600 + :alt: Example mapping + :align: center +| +G2G defines 5 different states of alignment between any two **R** and **Q** time points, corresponding to all possible match and mismatch states. They are: 1-to-1 match (``M``), 1-to-many match (``V``), many-to-1 match (``W``), insertion (``I``) and deletion (``D``). Here, ``I`` or ``D`` refer to a mismatched time point in Q or R, respectively. These states jointly cover the alignment states defined in classical dynamic time warping and biological sequence alignment. + +.. image:: images/DocFigs1-3.png + :width: 600 + :alt: 5 states of alignment + :align: center +| +Accordingly, we can describe any trajectory alignment as a 5-state alignment string. For example, the 5-state alignment string of the above illustrated trajectory alignment is: + +.. code-block:: + + IIIMMMWWWIIIDDDMMMIIIIDDDD + +This G2G alignment string enables us to identify the time regions of match and mismatch along the trajectories. For instance, we can interpret the above illustrated alignment as follow -- *R and Q trajectories have mid and late mismatches, with the early stage of Q being mismatched, yet starting to match to the early stage of R at the middle of Q's trajectory. Overall, there are 9 R and Q pseudotime pairs getting matched (with 34.62% alignment similarity)*. + + +Outputs from Genes2Genes +########### + +Given an scRNA-seq dataset with their pseudotime estimates and a specified set of genes (e.g. all transcription factors, highly variable genes, biological/signaling pathway genes), G2G generates fully-descriptive alignments for each gene (i.e. **gene-level alignment**), as well as an average (aggregate) alignment (i.e. **cell-level alignment**) across all genes. + +Below is an example gene-level alignment of the gene *JUNB* in T cell differentiation between a pan-fetal reference and an artificial thymic organoid system: + +.. image:: images/DocFigs2.png + :width: 600 + :alt: Example gene-level alignment? + :align: center +| +.. code-block:: + + IIIIIDMMMMMMMMIDDDDD + +Each gene-level alignment carries its 5-state string, an alignment similarity percentage statistic, and the optimal alignment cost (in *nits* -- the unit measure of information). For the above gene, the aligment similarity is 40%, and the total cost of alignment is 53.47 nits. When the degree of difference in gene expression between the reference and query is high, the alignment cost will also be high. + +G2G uses the inferred gene-level alignments to inform: + +#. **The degree of similarity between the two profiles** as an average percentage of alignment similarity across all the genes tested, +#. **An aggregate cell-level alignment across all genes** to inform the average states of match and mismatch between the two profiles (again represented by a 5-state string), +#. **A ranked list of genes across time (from the most distant to most similar)** based on their alignment similarity percentage statistic, +#. **The diversity of different alignment patterns in genes**, by clustering gene-level alignments to identify different matching and mismatching patterns along time, + +between the two single-cell reference and query profiles in comparison. + +For further downstream analysis, G2G provides a wrapper function to check gene-set overrepresentation analysis of the identified gene-clusters and the list of the top distant (differentially-expressed) genes across time, using `GSEApy `_ Enrichr interface. The user is also able to compute an average alignment across any gene subset of their interest. + +**Note**: G2G has been developed only for single-lineage trajectory comparison. In the case of a trajectory with multiple branches, we recommend separating out the singe-lineage branches before any pairwise comparison. + +Our approach to trajectory alignment +########### + +We employ a **dynamic programming** (DP) based algorithm that can capture both matches and mismatches in gene expression in a unified way. This combines the classical **Gotoh's algorithm** for biological sequence alignment with **dynamic time warping**. Our DP algorithm uses a **Bayesian information-theoretic scoring scheme** based on the **minimum message length** criterion to generate an optimal alignment between two gene trajectories. This scheme evaluates the distributional similarity of gene expression between R and Q for each pair of time points, in terms of both their mean and variance of expression modelled as Gaussian distributions. +For more details on the methods, please see our `manuscript `_. + +Getting started +=========== + +For now, G2G needs to be installed from GitHub: + +.. code-block:: shell + + pip install git+https://github.com/Teichlab/Genes2Genes.git + +The package will be made available soon on PyPi. + +**Input to Gene2Genes** + +G2G takes reference and query input data as ``anndata`` objects, where each ``adata`` object has: + +* log1p normalised gene expression stored at ``adata.X`` +* pseudotime estimates of the cells stored as ``adata.obs['time']`` + +The user can estimate pseudotime of the cells in their datasets using any suitable method available (such as `Diffusion pseudotime `_, `Palantir `_, `GPLVM `_, `Monocle `_ etc.). +For better visualisation and interpretation of the alignment results, we recommend the data to be annotated with their cell types (manually and/or using an automatic annotation tool such as `CellTypist `_). + +Please refer to our :doc:`G2Gtutorial` for an example analysis between a reference and query dataset from literature. + +Citing Genes2Genes +=========== +Our manuscript is currently available as a `preprint `_ at bioRxiv: + +*Sumanaweera, D., Suo, C., Cujba, A.M., Muraro, D., Dann, E., Polanski, K., Steemers, A.S., Lee, W., Oliver, A.J., Park, J.E. and Meyer, K.B., 2023.* **Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation**. *bioRxiv, pp.2023-03.* + + +.. toctree:: + :hidden: + + self + ../notebooks/Tutorial \ No newline at end of file diff --git a/_sources/source/G2Gtutorial.rst.txt b/_sources/source/G2Gtutorial.rst.txt new file mode 100644 index 0000000..57facf0 --- /dev/null +++ b/_sources/source/G2Gtutorial.rst.txt @@ -0,0 +1,11 @@ + +.. _G2Gtutorial: +Tutorial +======================================= + + +Load the reference and query datasets: + + + + diff --git a/_sources/source/index.rst.txt b/_sources/source/index.rst.txt new file mode 100644 index 0000000..83b7b91 --- /dev/null +++ b/_sources/source/index.rst.txt @@ -0,0 +1,124 @@ +.. Genes2Genes documentation master file, created by + sphinx-quickstart on Wed Aug 16 09:01:25 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Genes2Genes +======================================= + +**A framework for single-cell pseudotime trajectory alignment** + +.. contents:: Contents + +Genes2Genes (G2G) is a new Python framework for aligning single-cell pseudotime trajectories of gene expression between any reference and query for a pairwise comparison such as: + + * Organoid vs. Reference tissue + * Control vs. Treatment + * Healthy vs. Disease + +Trajectory alignment: What & Why? +########### + +A single-cell trajectory describes the transcriptomic state of cells along some axis of progression (such as time), due to undergoing some dynamic process (e.g. cell differentiation, treatment response, or disease infection). Given an scRNA-seq profile, there are various tools available today to infer such trajectory by estimating a pseudo ordering of the cells along an axis, commonly referred to as 'pseudotime'. The pseudotime axis of a trajectory can be descritized to represent it as a sequence of discrete time points. Given two such discrete pseudotime sequences of two trajectories, a pairwise alignment between them defines a non-linear mapping between their time points. This mapping could have 1-to-1 matches as well as 1-to-many/many-to-1 matches (a.k.a warps) between the time points, while unmapping the time points which have significantly different transcriptomic states. Below is an example visualization of two cell differentiation trajectories. + + +.. image:: images/DocFigs1.png + :width: 600 + :alt: What is trajectory alignment? + :align: center + +For two trajectories representing single lineages as above, G2G generates an **optimal pairwise trajectory alignment** that captures the matches and mismatches between their time points in sequential order, allowing a user to quantify the degree of similarity between them. + +.. image:: images/DocFigs1-1.png + :width: 600 + :alt: Example mapping + :align: center +| +G2G defines 5 different states of alignment between any two **R** and **Q** time points, corresponding to all possible match and mismatch states. They are: 1-to-1 match (``M``), 1-to-many match (``V``), many-to-1 match (``W``), insertion (``I``) and deletion (``D``). Here, ``I`` or ``D`` refer to a mismatched time point in Q or R, respectively. These states jointly cover the alignment states defined in classical dynamic time warping and biological sequence alignment. + +.. image:: images/DocFigs1-3.png + :width: 600 + :alt: 5 states of alignment + :align: center +| +Accordingly, we can describe any trajectory alignment as a 5-state alignment string. For example, the 5-state alignment string of the above illustrated trajectory alignment is: + +.. code-block:: + + IIIMMMWWWIIIDDDMMMIIIIDDDD + +This G2G alignment string enables us to identify the time regions of match and mismatch along the trajectories. For instance, we can interpret the above illustrated alignment as follow -- *R and Q trajectories have mid and late mismatches, with the early stage of Q being mismatched, yet starting to match to the early stage of R at the middle of Q's trajectory. Overall, there are 9 R and Q pseudotime pairs getting matched (with 34.62% alignment similarity)*. + + +Outputs from Genes2Genes +########### + +Given an scRNA-seq dataset with their pseudotime estimates and a specified set of genes (e.g. all transcription factors, highly variable genes, biological/signaling pathway genes), G2G generates fully-descriptive alignments for each gene (i.e. **gene-level alignment**), as well as an average (aggregate) alignment (i.e. **cell-level alignment**) across all genes. + +Below is an example gene-level alignment of the gene *JUNB* in T cell differentiation between a pan-fetal reference and an artificial thymic organoid system: + +.. image:: images/DocFigs2.png + :width: 600 + :alt: Example gene-level alignment? + :align: center +| +.. code-block:: + + IIIIIDMMMMMMMMIDDDDD + +Each gene-level alignment carries its 5-state string, an alignment similarity percentage statistic, and the optimal alignment cost (in *nits* -- the unit measure of information). For the above gene, the aligment similarity is 40%, and the total cost of alignment is 53.47 nits. When the degree of difference in gene expression between the reference and query is high, the alignment cost will also be high. + +G2G uses the inferred gene-level alignments to inform: + +#. **The degree of similarity between the two profiles** as an average percentage of alignment similarity across all the genes tested, +#. **An aggregate cell-level alignment across all genes** to inform the average states of match and mismatch between the two profiles (again represented by a 5-state string), +#. **A ranked list of genes across time (from the most distant to most similar)** based on their alignment similarity percentage statistic, +#. **The diversity of different alignment patterns in genes**, by clustering gene-level alignments to identify different matching and mismatching patterns along time, + +between the two single-cell reference and query profiles in comparison. + +For further downstream analysis, G2G provides a wrapper function to check gene-set overrepresentation analysis of the identified gene-clusters and the list of the top distant (differentially-expressed) genes across time, using `GSEApy `_ Enrichr interface. The user is also able to compute an average alignment across any gene subset of their interest. + +**Note**: G2G has been developed only for single-lineage trajectory comparison. In the case of a trajectory with multiple branches, we recommend separating out the singe-lineage branches before any pairwise comparison. + +Our approach to trajectory alignment +########### + +We employ a **dynamic programming** (DP) based algorithm that can capture both matches and mismatches in gene expression in a unified way. This combines the classical **Gotoh's algorithm** for biological sequence alignment with **dynamic time warping**. Our DP algorithm uses a **Bayesian information-theoretic scoring scheme** based on the **minimum message length** criterion to generate an optimal alignment between two gene trajectories. This scheme evaluates the distributional similarity of gene expression between R and Q for each pair of time points, in terms of both their mean and variance of expression modelled as Gaussian distributions. +For more details on the methods, please see our `manuscript `_. + +Getting started +=========== + +For now, G2G needs to be installed from GitHub: + +.. code-block:: shell + + pip install git+https://github.com/Teichlab/Genes2Genes.git + +The package will be made available soon on PyPi. + +**Input to Gene2Genes** + +G2G takes reference and query input data as ``anndata`` objects, where each ``adata`` object has: + +* log1p normalised gene expression stored at ``adata.X`` +* pseudotime estimates of the cells stored as ``adata.obs['time']`` + +The user can estimate pseudotime of the cells in their datasets using any suitable method available (such as `Diffusion pseudotime `_, `Palantir `_, `GPLVM `_, `Monocle `_ etc.). +For better visualisation and interpretation of the alignment results, we recommend the data to be annotated with their cell types (manually and/or using an automatic annotation tool such as `CellTypist `_). + +Please refer to our :doc:`G2Gtutorial` for an example analysis between a reference and query dataset from literature. + +Citing Genes2Genes +=========== +Our manuscript is currently available as a `preprint `_ at bioRxiv: + +*Sumanaweera, D., Suo, C., Cujba, A.M., Muraro, D., Dann, E., Polanski, K., Steemers, A.S., Lee, W., Oliver, A.J., Park, J.E. and Meyer, K.B., 2023.* **Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation**. *bioRxiv, pp.2023-03.* + + +.. toctree:: + :hidden: + + self + ../notebooks/Tutorial \ No newline at end of file diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..a41f746 --- /dev/null +++ b/genindex.html @@ -0,0 +1,106 @@ + + + + + + Index — Genes2Genes v1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Dinithi Sumanaweera, Teichmann Lab.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..b0b76a4 --- /dev/null +++ b/index.html @@ -0,0 +1,206 @@ + + + + + + + Genes2Genes — Genes2Genes v1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Genes2Genes

+

A framework for single-cell pseudotime trajectory alignment

+ +

Genes2Genes (G2G) is a new Python framework for aligning single-cell pseudotime trajectories of gene expression between any reference and query for a pairwise comparison such as:

+
+
    +

  • Organoid vs. Reference tissue

    • +

  • Control vs. Treatment

    • +

  • Healthy vs. Disease

    • +
+
+
+

Trajectory alignment: What & Why?

+

A single-cell trajectory describes the transcriptomic state of cells along some axis of progression (such as time), due to undergoing some dynamic process (e.g. cell differentiation, treatment response, or disease infection). Given an scRNA-seq profile, there are various tools available today to infer such trajectory by estimating a pseudo ordering of the cells along an axis, commonly referred to as ‘pseudotime’. The pseudotime axis of a trajectory can be descritized to represent it as a sequence of discrete time points. Given two such discrete pseudotime sequences of two trajectories, a pairwise alignment between them defines a non-linear mapping between their time points. This mapping could have 1-to-1 matches as well as 1-to-many/many-to-1 matches (a.k.a warps) between the time points, while unmapping the time points which have significantly different transcriptomic states. Below is an example visualization of two cell differentiation trajectories.

+What is trajectory alignment? +

For two trajectories representing single lineages as above, G2G generates an optimal pairwise trajectory alignment that captures the matches and mismatches between their time points in sequential order, allowing a user to quantify the degree of similarity between them.

+Example mapping +
+

+
+

G2G defines 5 different states of alignment between any two R and Q time points, corresponding to all possible match and mismatch states. They are: 1-to-1 match (M), 1-to-many match (V), many-to-1 match (W), insertion (I) and deletion (D). Here, I or D refer to a mismatched time point in Q or R, respectively. These states jointly cover the alignment states defined in classical dynamic time warping and biological sequence alignment.

+5 states of alignment +
+

+
+

Accordingly, we can describe any trajectory alignment as a 5-state alignment string. For example, the 5-state alignment string of the above illustrated trajectory alignment is:

+
IIIMMMWWWIIIDDDMMMIIIIDDDD
+
+
+

This G2G alignment string enables us to identify the time regions of match and mismatch along the trajectories. For instance, we can interpret the above illustrated alignment as follow – R and Q trajectories have mid and late mismatches, with the early stage of Q being mismatched, yet starting to match to the early stage of R at the middle of Q’s trajectory. Overall, there are 9 R and Q pseudotime pairs getting matched (with 34.62% alignment similarity).

+
+
+

Outputs from Genes2Genes

+

Given an scRNA-seq dataset with their pseudotime estimates and a specified set of genes (e.g. all transcription factors, highly variable genes, biological/signaling pathway genes), G2G generates fully-descriptive alignments for each gene (i.e. gene-level alignment), as well as an average (aggregate) alignment (i.e. cell-level alignment) across all genes.

+

Below is an example gene-level alignment of the gene JUNB in T cell differentiation between a pan-fetal reference and an artificial thymic organoid system:

+Example gene-level alignment? +
+

+
+
IIIIIDMMMMMMMMIDDDDD
+
+
+

Each gene-level alignment carries its 5-state string, an alignment similarity percentage statistic, and the optimal alignment cost (in nits – the unit measure of information). For the above gene, the aligment similarity is 40%, and the total cost of alignment is 53.47 nits. When the degree of difference in gene expression between the reference and query is high, the alignment cost will also be high.

+

G2G uses the inferred gene-level alignments to inform:

+
    +

  1. The degree of similarity between the two profiles as an average percentage of alignment similarity across all the genes tested,

    2. +

  2. An aggregate cell-level alignment across all genes to inform the average states of match and mismatch between the two profiles (again represented by a 5-state string),

    3. +

  3. A ranked list of genes across time (from the most distant to most similar) based on their alignment similarity percentage statistic,

    4. +

  4. The diversity of different alignment patterns in genes, by clustering gene-level alignments to identify different matching and mismatching patterns along time,

    5. +
+

between the two single-cell reference and query profiles in comparison.

+

For further downstream analysis, G2G provides a wrapper function to check gene-set overrepresentation analysis of the identified gene-clusters and the list of the top distant (differentially-expressed) genes across time, using GSEApy Enrichr interface. The user is also able to compute an average alignment across any gene subset of their interest.

+

Note: G2G has been developed only for single-lineage trajectory comparison. In the case of a trajectory with multiple branches, we recommend separating out the singe-lineage branches before any pairwise comparison.

+
+
+

Our approach to trajectory alignment

+

We employ a dynamic programming (DP) based algorithm that can capture both matches and mismatches in gene expression in a unified way. This combines the classical Gotoh’s algorithm for biological sequence alignment with dynamic time warping. Our DP algorithm uses a Bayesian information-theoretic scoring scheme based on the minimum message length criterion to generate an optimal alignment between two gene trajectories. This scheme evaluates the distributional similarity of gene expression between R and Q for each pair of time points, in terms of both their mean and variance of expression modelled as Gaussian distributions. +For more details on the methods, please see our manuscript.

+
+
+
+

Getting started

+

For now, G2G needs to be installed from GitHub:

+
pip install git+https://github.com/Teichlab/Genes2Genes.git
+
+
+

The package will be made available soon on PyPi.

+

Input to Gene2Genes

+

G2G takes reference and query input data as anndata objects, where each adata object has:

+
    +

  • log1p normalised gene expression stored at adata.X

    • +

  • pseudotime estimates of the cells stored as adata.obs['time']

    • +
+

The user can estimate pseudotime of the cells in their datasets using any suitable method available (such as Diffusion pseudotime, CellRank, Palantir, GPLVM etc.). +For better visualisation and interpretation of the alignment results, we recommend the data to be annotated with their cell types (manually and/or using an automatic annotation tool such as CellTypist).

+

Please refer to our Tutorial for an example analysis between a reference and query dataset from literature.

+
+
+

Citing Genes2Genes

+

Our manuscript is currently available as a preprint at bioRxiv:

+

Sumanaweera, D., Suo, C., Cujba, A.M., Muraro, D., Dann, E., Polanski, K., Steemers, A.S., Lee, W., Oliver, A.J., Park, J.E. and Meyer, K.B., 2023. Gene-level alignment of single cell trajectories. bioRxiv, pp.2023-03.

+

This publication is part of the Human Cell Atlas

+
+
+

Funding Acknowledgement

+

Marie Skłodowska-Curie grant agreement No: 101026506 (Marie Curie Individual Fellowship) under the European Union’s Horizon 2020 research and innovation programme; Wellcome Trust Ph.D. Fellowship for Clinicians; Wellcome Trust (WT206194); ERC Consolidator Grant (646794); Wellcome Sanger Institute’s Translation Committee Fund.

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Dinithi Sumanaweera, Teichmann Lab.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000..b2d5965 --- /dev/null +++ b/objects.inv @@ -0,0 +1,6 @@ +# Sphinx inventory version 2 +# Project: Genes2Genes +# Version: +# The remainder of this file is compressed using zlib. +xڕ >vef%&}Ah &A'ki ˙nXz32k Z1' PKpR]8d AAD +LIFƦO'#7M; cZ-i%|պ + + + + + Search — Genes2Genes v1.0 documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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    • +
+
+
+
+
+ + + + +
+ +
+ +
+
+
+ +
+ +
+

© Copyright 2023, Dinithi Sumanaweera, Teichmann Lab.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000..ac67691 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"Citing Genes2Genes": [[0, "citing-genes2genes"], [2, "citing-genes2genes"], [4, "citing-genes2genes"]], "Contents": [[0, "contents"], [2, "contents"], [4, "contents"]], "Funding Acknowledgement": [[0, "funding-acknowledgement"]], "Genes2Genes": [[0, "genes2genes"], [2, "genes2genes"], [4, "genes2genes"]], "Getting started": [[0, "getting-started"], [2, "getting-started"], [4, "getting-started"]], "Our approach to trajectory alignment": [[0, "our-approach-to-trajectory-alignment"], [2, "our-approach-to-trajectory-alignment"], [4, "our-approach-to-trajectory-alignment"]], "Outputs from Genes2Genes": [[0, "outputs-from-genes2genes"], [2, "outputs-from-genes2genes"], [4, "outputs-from-genes2genes"]], "Trajectory alignment: What & Why?": [[0, "trajectory-alignment-what-why"], 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2, 4], "cite": [0, 2, 4], "content": [0, 2, 4], "from": [0, 2, 4], "fund": 0, "genes2gen": [0, 2, 4], "get": [0, 2, 4], "our": [0, 2, 4], "output": [0, 2, 4], "start": [0, 2, 4], "trajectori": [0, 2, 4], "tutori": [1, 3], "what": [0, 2, 4], "why": [0, 2, 4]}}) \ No newline at end of file diff --git a/source/.ipynb_checkpoints/G2Gtutorial-checkpoint.html b/source/.ipynb_checkpoints/G2Gtutorial-checkpoint.html new file mode 100644 index 0000000..c85828c --- /dev/null +++ b/source/.ipynb_checkpoints/G2Gtutorial-checkpoint.html @@ -0,0 +1,106 @@ + + + + + + + Tutorial — Genes2Genes v1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Tutorial

+

Load the reference and query datasets:

+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Dinithi Sumanaweera, Teichmann Lab.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/source/.ipynb_checkpoints/index-checkpoint.html b/source/.ipynb_checkpoints/index-checkpoint.html new file mode 100644 index 0000000..c5204ae --- /dev/null +++ b/source/.ipynb_checkpoints/index-checkpoint.html @@ -0,0 +1,200 @@ + + + + + + + Genes2Genes — Genes2Genes v1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Genes2Genes

+

A framework for single-cell pseudotime trajectory alignment

+ +

Genes2Genes (G2G) is a new Python framework for aligning single-cell pseudotime trajectories of gene expression between any reference and query for a pairwise comparison such as:

+
+
    +

  • Organoid vs. Reference tissue

    • +

  • Control vs. Treatment

    • +

  • Healthy vs. Disease

    • +
+
+
+

Trajectory alignment: What & Why?

+

A single-cell trajectory describes the transcriptomic state of cells along some axis of progression (such as time), due to undergoing some dynamic process (e.g. cell differentiation, treatment response, or disease infection). Given an scRNA-seq profile, there are various tools available today to infer such trajectory by estimating a pseudo ordering of the cells along an axis, commonly referred to as ‘pseudotime’. The pseudotime axis of a trajectory can be descritized to represent it as a sequence of discrete time points. Given two such discrete pseudotime sequences of two trajectories, a pairwise alignment between them defines a non-linear mapping between their time points. This mapping could have 1-to-1 matches as well as 1-to-many/many-to-1 matches (a.k.a warps) between the time points, while unmapping the time points which have significantly different transcriptomic states. Below is an example visualization of two cell differentiation trajectories.

+What is trajectory alignment? +

For two trajectories representing single lineages as above, G2G generates an optimal pairwise trajectory alignment that captures the matches and mismatches between their time points in sequential order, allowing a user to quantify the degree of similarity between them.

+Example mapping +
+

+
+

G2G defines 5 different states of alignment between any two R and Q time points, corresponding to all possible match and mismatch states. They are: 1-to-1 match (M), 1-to-many match (V), many-to-1 match (W), insertion (I) and deletion (D). Here, I or D refer to a mismatched time point in Q or R, respectively. These states jointly cover the alignment states defined in classical dynamic time warping and biological sequence alignment.

+5 states of alignment +
+

+
+

Accordingly, we can describe any trajectory alignment as a 5-state alignment string. For example, the 5-state alignment string of the above illustrated trajectory alignment is:

+
IIIMMMWWWIIIDDDMMMIIIIDDDD
+
+
+

This G2G alignment string enables us to identify the time regions of match and mismatch along the trajectories. For instance, we can interpret the above illustrated alignment as follow – R and Q trajectories have mid and late mismatches, with the early stage of Q being mismatched, yet starting to match to the early stage of R at the middle of Q’s trajectory. Overall, there are 9 R and Q pseudotime pairs getting matched (with 34.62% alignment similarity).

+
+
+

Outputs from Genes2Genes

+

Given an scRNA-seq dataset with their pseudotime estimates and a specified set of genes (e.g. all transcription factors, highly variable genes, biological/signaling pathway genes), G2G generates fully-descriptive alignments for each gene (i.e. gene-level alignment), as well as an average (aggregate) alignment (i.e. cell-level alignment) across all genes.

+

Below is an example gene-level alignment of the gene JUNB in T cell differentiation between a pan-fetal reference and an artificial thymic organoid system:

+Example gene-level alignment? +
+

+
+
IIIIIDMMMMMMMMIDDDDD
+
+
+

Each gene-level alignment carries its 5-state string, an alignment similarity percentage statistic, and the optimal alignment cost (in nits – the unit measure of information). For the above gene, the aligment similarity is 40%, and the total cost of alignment is 53.47 nits. When the degree of difference in gene expression between the reference and query is high, the alignment cost will also be high.

+

G2G uses the inferred gene-level alignments to inform:

+
    +

  1. The degree of similarity between the two profiles as an average percentage of alignment similarity across all the genes tested,

    2. +

  2. An aggregate cell-level alignment across all genes to inform the average states of match and mismatch between the two profiles (again represented by a 5-state string),

    3. +

  3. A ranked list of genes across time (from the most distant to most similar) based on their alignment similarity percentage statistic,

    4. +

  4. The diversity of different alignment patterns in genes, by clustering gene-level alignments to identify different matching and mismatching patterns along time,

    5. +
+

between the two single-cell reference and query profiles in comparison.

+

For further downstream analysis, G2G provides a wrapper function to check gene-set overrepresentation analysis of the identified gene-clusters and the list of the top distant (differentially-expressed) genes across time, using GSEApy Enrichr interface. The user is also able to compute an average alignment across any gene subset of their interest.

+

Note: G2G has been developed only for single-lineage trajectory comparison. In the case of a trajectory with multiple branches, we recommend separating out the singe-lineage branches before any pairwise comparison.

+
+
+

Our approach to trajectory alignment

+

We employ a dynamic programming (DP) based algorithm that can capture both matches and mismatches in gene expression in a unified way. This combines the classical Gotoh’s algorithm for biological sequence alignment with dynamic time warping. Our DP algorithm uses a Bayesian information-theoretic scoring scheme based on the minimum message length criterion to generate an optimal alignment between two gene trajectories. This scheme evaluates the distributional similarity of gene expression between R and Q for each pair of time points, in terms of both their mean and variance of expression modelled as Gaussian distributions. +For more details on the methods, please see our manuscript.

+
+
+
+

Getting started

+

For now, G2G needs to be installed from GitHub:

+
pip install git+https://github.com/Teichlab/Genes2Genes.git
+
+
+

The package will be made available soon on PyPi.

+

Input to Gene2Genes

+

G2G takes reference and query input data as anndata objects, where each adata object has:

+
    +

  • log1p normalised gene expression stored at adata.X

    • +

  • pseudotime estimates of the cells stored as adata.obs['time']

    • +
+

The user can estimate pseudotime of the cells in their datasets using any suitable method available (such as Diffusion pseudotime, Palantir, GPLVM, Monocle etc.). +For better visualisation and interpretation of the alignment results, we recommend the data to be annotated with their cell types (manually and/or using an automatic annotation tool such as CellTypist).

+

Please refer to our G2Gtutorial for an example analysis between a reference and query dataset from literature.

+
+
+

Citing Genes2Genes

+

Our manuscript is currently available as a preprint at bioRxiv:

+

Sumanaweera, D., Suo, C., Cujba, A.M., Muraro, D., Dann, E., Polanski, K., Steemers, A.S., Lee, W., Oliver, A.J., Park, J.E. and Meyer, K.B., 2023. Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation. bioRxiv, pp.2023-03.

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Dinithi Sumanaweera, Teichmann Lab.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/source/G2Gtutorial.html b/source/G2Gtutorial.html new file mode 100644 index 0000000..b03f505 --- /dev/null +++ b/source/G2Gtutorial.html @@ -0,0 +1,106 @@ + + + + + + + Tutorial — Genes2Genes v1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Tutorial

+

Load the reference and query datasets:

+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Dinithi Sumanaweera, Teichmann Lab.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/source/index.html b/source/index.html new file mode 100644 index 0000000..316b4b5 --- /dev/null +++ b/source/index.html @@ -0,0 +1,200 @@ + + + + + + + Genes2Genes — Genes2Genes v1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Genes2Genes

+

A framework for single-cell pseudotime trajectory alignment

+ +

Genes2Genes (G2G) is a new Python framework for aligning single-cell pseudotime trajectories of gene expression between any reference and query for a pairwise comparison such as:

+
+
    +

  • Organoid vs. Reference tissue

    • +

  • Control vs. Treatment

    • +

  • Healthy vs. Disease

    • +
+
+
+

Trajectory alignment: What & Why?

+

A single-cell trajectory describes the transcriptomic state of cells along some axis of progression (such as time), due to undergoing some dynamic process (e.g. cell differentiation, treatment response, or disease infection). Given an scRNA-seq profile, there are various tools available today to infer such trajectory by estimating a pseudo ordering of the cells along an axis, commonly referred to as ‘pseudotime’. The pseudotime axis of a trajectory can be descritized to represent it as a sequence of discrete time points. Given two such discrete pseudotime sequences of two trajectories, a pairwise alignment between them defines a non-linear mapping between their time points. This mapping could have 1-to-1 matches as well as 1-to-many/many-to-1 matches (a.k.a warps) between the time points, while unmapping the time points which have significantly different transcriptomic states. Below is an example visualization of two cell differentiation trajectories.

+What is trajectory alignment? +

For two trajectories representing single lineages as above, G2G generates an optimal pairwise trajectory alignment that captures the matches and mismatches between their time points in sequential order, allowing a user to quantify the degree of similarity between them.

+Example mapping +
+

+
+

G2G defines 5 different states of alignment between any two R and Q time points, corresponding to all possible match and mismatch states. They are: 1-to-1 match (M), 1-to-many match (V), many-to-1 match (W), insertion (I) and deletion (D). Here, I or D refer to a mismatched time point in Q or R, respectively. These states jointly cover the alignment states defined in classical dynamic time warping and biological sequence alignment.

+5 states of alignment +
+

+
+

Accordingly, we can describe any trajectory alignment as a 5-state alignment string. For example, the 5-state alignment string of the above illustrated trajectory alignment is:

+
IIIMMMWWWIIIDDDMMMIIIIDDDD
+
+
+

This G2G alignment string enables us to identify the time regions of match and mismatch along the trajectories. For instance, we can interpret the above illustrated alignment as follow – R and Q trajectories have mid and late mismatches, with the early stage of Q being mismatched, yet starting to match to the early stage of R at the middle of Q’s trajectory. Overall, there are 9 R and Q pseudotime pairs getting matched (with 34.62% alignment similarity).

+
+
+

Outputs from Genes2Genes

+

Given an scRNA-seq dataset with their pseudotime estimates and a specified set of genes (e.g. all transcription factors, highly variable genes, biological/signaling pathway genes), G2G generates fully-descriptive alignments for each gene (i.e. gene-level alignment), as well as an average (aggregate) alignment (i.e. cell-level alignment) across all genes.

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Below is an example gene-level alignment of the gene JUNB in T cell differentiation between a pan-fetal reference and an artificial thymic organoid system:

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IIIIIDMMMMMMMMIDDDDD
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Each gene-level alignment carries its 5-state string, an alignment similarity percentage statistic, and the optimal alignment cost (in nits – the unit measure of information). For the above gene, the aligment similarity is 40%, and the total cost of alignment is 53.47 nits. When the degree of difference in gene expression between the reference and query is high, the alignment cost will also be high.

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G2G uses the inferred gene-level alignments to inform:

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  1. The degree of similarity between the two profiles as an average percentage of alignment similarity across all the genes tested,

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  2. An aggregate cell-level alignment across all genes to inform the average states of match and mismatch between the two profiles (again represented by a 5-state string),

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  3. A ranked list of genes across time (from the most distant to most similar) based on their alignment similarity percentage statistic,

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  4. The diversity of different alignment patterns in genes, by clustering gene-level alignments to identify different matching and mismatching patterns along time,

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between the two single-cell reference and query profiles in comparison.

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For further downstream analysis, G2G provides a wrapper function to check gene-set overrepresentation analysis of the identified gene-clusters and the list of the top distant (differentially-expressed) genes across time, using GSEApy Enrichr interface. The user is also able to compute an average alignment across any gene subset of their interest.

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Note: G2G has been developed only for single-lineage trajectory comparison. In the case of a trajectory with multiple branches, we recommend separating out the singe-lineage branches before any pairwise comparison.

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Our approach to trajectory alignment

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We employ a dynamic programming (DP) based algorithm that can capture both matches and mismatches in gene expression in a unified way. This combines the classical Gotoh’s algorithm for biological sequence alignment with dynamic time warping. Our DP algorithm uses a Bayesian information-theoretic scoring scheme based on the minimum message length criterion to generate an optimal alignment between two gene trajectories. This scheme evaluates the distributional similarity of gene expression between R and Q for each pair of time points, in terms of both their mean and variance of expression modelled as Gaussian distributions. +For more details on the methods, please see our manuscript.

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Getting started

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For now, G2G needs to be installed from GitHub:

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pip install git+https://github.com/Teichlab/Genes2Genes.git
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The package will be made available soon on PyPi.

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Input to Gene2Genes

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G2G takes reference and query input data as anndata objects, where each adata object has:

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  • log1p normalised gene expression stored at adata.X

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  • pseudotime estimates of the cells stored as adata.obs['time']

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The user can estimate pseudotime of the cells in their datasets using any suitable method available (such as Diffusion pseudotime, Palantir, GPLVM, Monocle etc.). +For better visualisation and interpretation of the alignment results, we recommend the data to be annotated with their cell types (manually and/or using an automatic annotation tool such as CellTypist).

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Please refer to our Tutorial for an example analysis between a reference and query dataset from literature.

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Citing Genes2Genes

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Our manuscript is currently available as a preprint at bioRxiv:

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Sumanaweera, D., Suo, C., Cujba, A.M., Muraro, D., Dann, E., Polanski, K., Steemers, A.S., Lee, W., Oliver, A.J., Park, J.E. and Meyer, K.B., 2023. Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation. bioRxiv, pp.2023-03.

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© Copyright 2023, Dinithi Sumanaweera, Teichmann Lab.

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