boost: boost: Bayesian Modeling of Spatial Transcriptomics Data
In estfernan/boost: Bayesian Modeling of Spatial Transcriptomics Data
boost R Documentation
boost: Bayesian Modeling of Spatial Transcriptomics Data
Description
This package provides functions to detect genes with spatial expression
pattern, also known as spatially variable (SV) genes, in spatial
transcriptomics (ST) data. Two major and novel Bayesian models are
implemented via a Gaussian process or Ising model. In addition, it also
provides other standard statistical tools such as SPARK, binSpect, etc.
Utilities are available to normalize count data, dichotomise expression
levels, get spatial neighbors, and view the results of the procedures.
Details
See Jiang et al. (2021) and Li et al. (2020) for details and illustrations
of how the Bayesian models are fitted and their applications.
References
Jiang, X., Li, Q., & Xiao, G. (2021). Bayesian Modeling of
Spatial Transcriptomics Data via a Modified Ising Model.
arXiv preprint arXiv:2104.13957.
Li, Q., Zhang M., Xie Y., & Xiao, G. (2020). Bayesian Modeling of
Spatial Molecular Profiling Data via Gaussian Process.
arXiv preprint arXiv:2012.03326.
estfernan/boost documentation built on June 24, 2022, 12:20 a.m.
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| boost | R Documentation |
boost: Bayesian Modeling of Spatial Transcriptomics Data
Description
This package provides functions to detect genes with spatial expression pattern, also known as spatially variable (SV) genes, in spatial transcriptomics (ST) data. Two major and novel Bayesian models are implemented via a Gaussian process or Ising model. In addition, it also provides other standard statistical tools such as SPARK, binSpect, etc. Utilities are available to normalize count data, dichotomise expression levels, get spatial neighbors, and view the results of the procedures.
Details
See Jiang et al. (2021) and Li et al. (2020) for details and illustrations of how the Bayesian models are fitted and their applications.
References
Jiang, X., Li, Q., & Xiao, G. (2021). Bayesian Modeling of Spatial Transcriptomics Data via a Modified Ising Model. arXiv preprint arXiv:2104.13957.
Li, Q., Zhang M., Xie Y., & Xiao, G. (2020). Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process. arXiv preprint arXiv:2012.03326.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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