glm_gp {glmGamPoi} | R Documentation

## Fit a Gamma-Poisson Generalized Linear Model

### Description

This function provides a simple to use interface to fit Gamma-Poisson generalized linear models. It works equally well for small scale (a single model) and large scale data (e.g. thousands of rows and columns, potentially stored on disk). The function automatically determines the appropriate size factors for each sample and efficiently finds the best overdispersion parameter for each gene.

### Usage

    glm_gp(
      data,
      design = ~1,
      col_data = NULL,
      reference_level = NULL,
      offset = 0,
      size_factors = c("normed_sum", "deconvolution", "poscounts"),
      overdispersion = TRUE,
      overdispersion_shrinkage = TRUE,
      do_cox_reid_adjustment = TRUE,
      subsample = FALSE,
      on_disk = NULL,
      verbose = FALSE
    )

### Arguments

`data` | any matrix-like object (e.g. matrix, DelayedArray, HDF5Matrix) or anything that can be cast to a SummarizedExperiment (e.g. `MSnSet`, `eSet` etc.) with one column per sample and row per gene.
`design` | a specification of the experimental design used to fit the Gamma-Poisson GLM. It can be a `model.matrix()` with one row for each sample and one column for each coefficient. Alternatively, `design` can be a `formula`. The entries in the formula can refer to global objects, columns in the `col_data` parameter, or the `colData(data)` of `data` if it is a `SummarizedExperiment`. The third option is that `design` is a vector where each element specifies to which condition a sample belongs. Default: `design = ~ 1`, which means that all samples are treated as if they belong to the same condition. Note that this is the fasted option.
`col_data` | a dataframe with one row for each sample in `data`. Default: `NULL`.
`reference_level` | a single string that specifies which level is used as reference when the model matrix is created. The reference level becomes the intercept and all other coefficients are calculated with respect to the `reference_level`. Default: `NULL`.
`offset` | Constant offset in the model in addition to `log(size_factors)`. It can either be a single number, a vector of length `ncol(data)` or a matrix with the same dimensions as `dim(data)`. Note that if data is a DelayedArray or HDF5Matrix, `offset` must be as well. Default: `0`.
`size_factors` | in large scale experiments, each sample is typically of different size (for example different sequencing depths). A size factor is an internal mechanism of GLMs to correct for this effect. `size_factors` is either a numeric vector with positive entries that has the same lengths as columns in the data that specifies the size factors that are used. Or it can be a string that species the method that is used to estimate the size factors (one of `c("normed_sum", "deconvolution", "poscounts")`). Note that `"normed_sum"` and `"poscounts"` are fairly simple methods and can lead to suboptimal results. For the best performance, I recommend to use `size_factors = "deconvolution"` which calls `scran::calculateSumFactors()`. However, you need to separately install the `scran` package from Bioconductor for this method to work. Also note that `size_factors = 1` and `size_factors = FALSE` are equivalent. If only a single gene is given, no size factor is estimated (ie. `size_factors = 1`). Default: `"normed_sum"`.
`overdispersion` | the simplest count model is the Poisson model. However, the Poisson model assumes that variance = mean. For many applications this is too rigid and the Gamma-Poisson allows a more flexible mean-variance relation (variance = mean + mean^2 * overdispersion). `overdispersion` can either be a single boolean that indicates if an overdispersion is estimated for each gene. a numeric vector of length `nrow(data)` fixing the overdispersion to those values. the string `"global"` to indicate that one dispersion is fit across all genes. Note that `overdispersion = 0` and `overdispersion = FALSE` are equivalent and both reduce the Gamma-Poisson to the classical Poisson model. Default: `TRUE`.
`overdispersion_shrinkage` | the overdispersion can be difficult to estimate with few replicates. To improve the overdispersion estimates, we can share information across genes and shrink each individual overdispersion estimate towards a global overdispersion estimate. Empirical studies show however that the overdispersion varies based on the mean expression level (lower expression level => higher dispersion). If `overdispersion_shrinkage = TRUE`, a median trend of dispersion and expression level is fit and used to estimate the variances of a quasi Gamma Poisson model (Lund et al. 2012). Default: `TRUE`.
`do_cox_reid_adjustment` | the classical maximum likelihood estimator of the `overdisperion` is biased towards small values. McCarthy et al. (2012) showed that it is preferable to optimize the Cox-Reid adjusted profile likelihood. `do_cox_reid_adjustment` can be either be `TRUE` or `FALSE` to indicate if the adjustment is added during the optimization of the `overdispersion` parameter. Default: `TRUE`.
`subsample` | the estimation of the overdispersion is the slowest step when fitting a Gamma-Poisson GLM. For datasets with many samples, the estimation can be considerably sped up without loosing much precision by fitting the overdispersion only on a random subset of the samples. Default: `FALSE` which means that the data is not subsampled. If set to `TRUE`, at most 1,000 samples are considered. Otherwise the parameter just specifies the number of samples that are considered for each gene to estimate the overdispersion.
`on_disk` | a boolean that indicates if the dataset is loaded into memory or if it is kept on disk to reduce the memory usage. Processing in memory can be significantly faster than on disk. Default: `NULL` which means that the data is only processed in memory if `data` is an in-memory data structure.
`verbose` | a boolean that indicates if information about the individual steps are printed while fitting the GLM. Default: `FALSE`.

### Details

The method follows the following steps:

1. The size factors are estimated.
   If `size_factors = "normed_sum"`, the column-sum for each cell is calculated and the resulting size factors are normalized so that their geometric mean is `1`. If `size_factors = "poscounts"`, a slightly adapted version of the procedure proposed by Anders and Huber (2010) in equation (5) is used. To handle the large number of zeros the geometric means are calculated for Y + 0.5 and ignored during the calculation of the median. Columns with all zeros get a default size factor of 0.001. If `size_factors = "deconvolution"`, the method `scran::calculateSumFactors()` is called.

2. The dispersion estimates are initialized based on the moments of each row of Y.

3. The coefficients of the model are estimated.
   If all samples belong to the same condition (i.e. `design = ~ 1`), the betas are estimated using a quick Newton-Raphson algorithm. This is similar to the behavior of `edgeR`. For more complex designs, the general Fisher-scoring algorithm is used. Here, the code is based on a fork of the internal function `fitBeta()` from `DESeq2`. It does however contain some modification to make the fit more robust and faster.

4. The mean for each gene and sample is calculate.
   Note that this step can be very IO intensive if `data` is or contains a DelayedArray.

5. The overdispersion is estimated.
   The classical method for estimating the overdispersion for each gene is to maximize the Gamma-Poisson log-likelihood by iterating over each count and summing the the corresponding log-likelihood. It is however, much more efficient for genes with many small counts to work on the contingency table of the counts. Originally, this approach had already been used by Anscombe (1950). In this package, I have implemented an extension of their method that can handle general offsets.
   See also `overdispersion_mle()`.

6. The beta coefficients are estimated once more with the updated overdispersion estimates

7. The mean for each gene and sample is calculated again.

This method can handle not just in memory data, but also data stored on disk. This is essential for large scale datasets with thousands of samples, as they sometimes encountered in modern single-cell RNA-seq analysis. `glmGamPoi` relies on the `DelayedArray` and `beachmat` package to efficiently implement the access to the on-disk data.

### Value

The method returns a list with the following elements:

`Beta`

  a matrix with dimensions `nrow(data) x n_coefficients` where `n_coefficients` is based on the `design` argument. It contains the estimated coefficients for each gene.

`overdispersions`

  a vector with length `nrow(data)`. The overdispersion parameter for each gene. It describes how much more the counts vary than one would expect according to the Poisson model.

`overdispersion_shrinkage_list`

  a list with additional information from the quasi-likelihood shrinkage. For details see `overdispersion_shrinkage()`.

`deviances`

  a vector with the deviance of the fit for each row. The deviance is a measure how well the data is fit by the model. A smaller deviance means a better fit.

`Mu`

  a matrix with the same dimensions as `dim(data)`. If the calculation happened on disk, than `Mu` is a `HDF5Matrix`. It contains the estimated mean value for each gene and sample.

`size_factors`

  a vector with length `ncol(data)`. The size factors are the inferred correction factors for different sizes of each sample. They are also sometimes called the exposure factor.

`data`

  a `SummarizedExperiment` that contains the input counts and the `col_data`

`model_matrix`

  a matrix with dimensions `ncol(data) x n_coefficients`. It is build based on the `design` argument.

### References

- McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research, 40(10), 4288–4297. https://doi.org/10.1093/nar/gks042.

- Anders Simon, & Huber Wolfgang. (2010). Differential expression analysis for sequence count data. Genome Biology. https://doi.org/10.1016/j.jcf.2018.05.006.

- Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8.

- Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. https://doi.org/10.1093/bioinformatics/btp616.

- Lun ATL, Pagès H, Smith ML (2018). “beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types.” PLoS Comput. Biol., 14(5), e1006135. doi: 10.1371/journal.pcbi.1006135..

- Lund, S. P., Nettleton, D., McCarthy, D. J., & Smyth, G. K. (2012). Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology, 11(5). https://doi.org/10.1515/1544-6115.1826.

- Lun ATL, Bach K and Marioni JC (2016). Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17:75 https://doi.org/10.1186/s13059-016-0947-7

### See Also

`overdispersion_mle()` and `overdispersion_shrinkage()` for the internal functions that do the work. For differential expression analysis, see `test_de()`.

### Examples

     set.seed(1)
     # The simplest example
     y <- rnbinom(n = 10, mu = 3, size = 1/2.4)
     c(glm_gp(y, size_factors = FALSE))

     # Fitting a whole matrix
     model_matrix <- cbind(1, rnorm(5))
     true_Beta <- cbind(rnorm(n = 30), rnorm(n = 30, mean = 3))
     sf <- exp(rnorm(n = 5, mean = 0.7))
     model_matrix
     Y <- matrix(rnbinom(n = 30 * 5, mu = sf * exp(true_Beta %*% t(model_matrix)), size = 1/2.4),
                 nrow = 30, ncol = 5)

     fit <- glm_gp(Y, design = model_matrix, size_factors = sf, verbose = TRUE)
     summary(fit)

     # Fitting a model with covariates
     data <- data.frame(fav_food = sample(c("apple", "banana", "cherry"), size = 50, replace = TRUE),
     city = sample(c("heidelberg", "paris", "new york"), size = 50, replace = TRUE),
     age = rnorm(n = 50, mean = 40, sd = 15))
     Y <- matrix(rnbinom(n = 100 * 50, mu = 3, size = 1/3.1), nrow = 100, ncol = 50)
     fit <- glm_gp(Y, design = ~ fav_food + city + age, col_data = data)
     summary(fit)

[Package glmGamPoi version 1.2.0 Index]