空间转录组学分析包-Spatial transcriptomics Packages

  • Merfishtools – [Python] – MERFISHtools implement a Bayesian framework for accurately predicting gene or transcript expression from MERFISH data.MERFISHtools用于预测MERFISH数据的基因或转录表达。最重要的是,可以在两个或多个条件下进行差异基因表达分析,包括倍数变化和变异系数的可信间隔,并控制FDR。

    Citation

    If you use MERFISHtools, please cite our paper

    Köster, Johannes, Myles Brown, X. Shirley Liu. “A Bayesian Model for Single Cell Transcript Expression Analysis on MERFISH Data.” Bioinformatics 2018. https://doi.org/10.1093/bioinformatics/bty718.

    Installation

    MERFISHtools can be installed and updated easily via Bioconda.

    Usage

    For usage instructions at the command line, please issue

    merfishtools --help
    

    A typical MERFISHtools workflow is as follows.

    Step 1: Estimate transcript expressions

    Transcript expressions are estimated from raw MERFISH data via

    merfishtools exp --threads 8 codebook.txt data.txt --estimate estimates.txt > expression.txt
    

    See

    merfishtools exp --help
    

    for additional parameters.

    Input

    The file codebook.txt is a MERFISH codebook (example), consisting of tab separated columns:

    • feature,
    • codeword
    • expressed.

    The last column has to contain a 1 if a feature is expressed (e.g. it is a transcript or gene), and a 0 if it is a misidentification probe (see Chen et al. Science 2015). Note that these probes are important for MERFISHtools to estimate noise rates and provide more accurate predictions.

    The file data.txt (example) contains MERFISH readouts in tab-separated format. The expected columns are

    • cell,
    • feature,
    • hamming_dist,
    • cell_position_x,
    • cell_position_y,
    • rna_position_x,
    • rna_position_y.

    When using MERFISH protocol v2, you will have a binary file format containing the readouts. Merfishtools will detect this automatically.

    Output

    Results are provided as probability mass functions (PMF) at STDOUT, in the format

    • cell,
    • feature (e.g. gene, transcript),
    • expression,
    • posterior probability.

    Further, the optional flag --estimate estimates.txt results in a table with expression estimates of the form:

    • cell,
    • feature,
    • maximum a posteriori (MAP) estimate,
    • standard deviation,
    • maximum a-posteriori probability estimate (MAP),
    • lower bound of 95% credible interval,
    • upper bound of 95% credible interval.

    Step 2: Estimate differential expression

    Two conditions

    In case of two conditions, you can issue

    merfishtools diffexp --threads 8 expression1.txt expression2.txt > diffexp.txt
    

    to calculate differentially expressed transcripts. See

    merfishtools diffexp --help
    

    for additional parameters.

    Input

    The files expression1.txt and expression2.txt (example) contain the PMFs of the two conditions to compare, and are obtained by running step 1 on the data for each condition.

    Output

    Results are provided as tab separated table at STDOUT (here piped into the file diffexp.txt) with columns

    • feature (e.g. gene, transcript),
    • posterior error probability (PEP) for differential expression,
    • expected FDR when selecting all features down to the current,
    • bayes factor (BF) for differential expression,
    • maximum a posteriori (MAP) log2 fold change of first vs second group,
    • standard deviation of log2 fold change,
    • maximum a posteriori (MAP) log2 fold change,
    • lower bound of 95% credible interval of log2 fold change,
    • upper bound of 95% credible interval of log2 fold change.

    Multiple conditions

    In case of more than two conditions, you can issue

    merfishtools multidiffexp --threads 8 expression1.txt expression2.txt expression3.txt ... > diffexp.txt
    

    to calculate differentially expressed transcripts. Here, the coefficient of variation over the condition means is used as measure for differential expression. See

    merfishtools multidiffexp --help
    

    for additional parameters.

    Input

    The files expression1.txt and expression2.txt, … (example) contain the PMFs of the conditions to compare, and are obtained by running step 1 on the data for each condition.

    Output

    Results are provided as tab separated table at STDOUT (here piped into the file diffexp.txt) with columns

    • feature (e.g. gene, transcript),
    • posterior error probability (PEP) for differential expression,
    • expected FDR when selecting all features down to the current,
    • bayes factor (BF) for differential expression,
    • maximum a posteriori (MAP) coefficient of variation (CV),
    • standard deviation of CV,
    • maximum a posteriori (MAP) CV,
    • lower bound of 95% credible interval of CV,
    • upper bound of 95% credible interval of CV.

    Author

    Johannes Köster

  • NMFreg – [Python] – The method is proposed in Slide-seq paper and reconstructs expression of each Slide-seq bead as a weighted combination of metagene factors, each corresponding to the expression signature of an individual cell type, defined from scRNA-seq.
  • Starspace – [Python] – Defines a schema for gene or protein expression data containing spatially localized information. Converts data from a variety of assay types, including Spatial Transcriptomics, CODEX, In-situ Sequencing, MERFISH, osmFISH, and starMAP. Demonstrates how to visualize and interact with these data using common analysis packages, and convert the formats into loom and anndata objects, for downstream analysis in R and Python.
  • SpatialDe – [Python] – SpatialDE is a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA-sequencing data.

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