Final commit after bioconductor revision

• This is the final version of BASiCS prior to the Bioconductor submission

This release is to mark the final stable version before a big merge.

This is to mark the current version of BASiCS before merging with @nilseling changes (in preparation for Bioconductor submission)

This release (v0.3.2)

This release includes the following changes:

• New slot GeneNames in 'BASiCS_Data' class
• Changes in the constructor newBASiCS_Data to allow easier construction of BASiCS_Data objects
• Minor changes to some functions' output to use the new GeneNames slot of 'BASiCS_Data' class
• Minor changes to the output of BASiCS_MCMC function (colnames of the elements related to the parameter $\theta$)
• Addition of extra optional parameter ls.phi0 to BASiCS_MCMC function. This is helpful on situations where the default value led to slow mixing of the chains related to the normalising constants $\phi_j$'s.

This release (v0.3.0)

This release includes the following changes:

• New slot 'BacthInfo' in 'BASiCS_Data' class to allow batch effect correction
• Batch-specific technical variability parameters are allowed
• 'BASiCS_VarianceDecomp' modified to accommodate batch membership (including graphical output)

Introduction

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where:

1. Cell-specific normalization constants are estimated as part of the model parameters,
2. Technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cells lysate and
3. The total variability of the expression counts is decomposed into technical and biological components.

BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalized by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by applied users.

---

This release

This release includes the following changes:

1. Argument 'GeneNames' has been added to functions 'BASiCS_VarianceDecomp', 'BASiCS_DetectHVG', 'BASiCS_DetectLVG' so that users can specify gene labels or names that will be used for these functions's output.

---

More details in

Catalina A. Vallejos, John C. Marioni and Sylvia Richardson (2015) BASiCS: Bayesian Analysis of Single-Cell Sequencing Data PLOS Computational Biology http://dx.doi.org/10.1371/journal.pcbi.1004333

Introduction

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where:

1. Cell-specific normalization constants are estimated as part of the model parameters,
2. Technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cells lysate and
3. The total variability of the expression counts is decomposed into technical and biological components.

BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalized by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by applied users.

---

This release

This release is an slightly updated version of the original release. Here we list the major changes:

1. We modified the sampler by using Dirichlet proposals for the mRNA content size factors $\phi_j$. While this does not affect the posterior inference (numerical results are virtually the same as in the previous implementation), the algorithm becomes more efficient, since the chains for $\phi_j$ have a better mixing.
2. We added the functions BASiCS_DenoisedCounts and BASiCS_DenoisedRates which might be helpful to perform other downstream analyses that are not included in this implementation.

• BASiCS_DenoisedCounts provides a denoised version of the expression counts. For each gene $i$ and cell $j$ this function returns $$ x^*{ij} = \frac{ x{ij} } {\hat{\phi}_j \hat{\nu}j}, $$ where $x{ij}$ is the observed expression count of gene $i$ in cell $j$, $\hat{\phi}_j$ denotes the posterior median of $\phi_j$ and $\hat{\nu}_j$ is the posterior median of $\nu_j$.
• BASiCS_DenoisedRates estimates normalised and denoised expression rates underlying the expression of all genes across cells. For each gene $i$ and cell $j$ this function returns $$ \Lambda_{ij} = \hat{\mu_i} \hat{\rho}{ij}, $$ where $\hat{\mu_i}$ represents the posterior median of $\mu_j$ and $\hat{\rho}{ij}$ is given by its posterior mean (Monte Carlo estimate based on the MCMC sample of all model parameters).

---

More details in

Catalina A. Vallejos, John C. Marioni and Sylvia Richardson (2015) BASiCS: Bayesian Analysis of Single-Cell Sequencing Data PLOS Computational Biology http://dx.doi.org/10.1371/journal.pcbi.1004333