Description Usage Arguments Details Value Examples

Performs traditional (i.e. compare defined groups) differential expression using a negative binomial model with MM zero-inflation. Functions tagged with "bg__" are not meant for direct usage and are not available in the Bioconductor release.

1 2 | ```
bg__get_mean2disp(expr_mat)
bg__fitdispersion(expr_mat)
``` |

`expr_mat` |
a numeric matrix of library-size normalized expression values, columns = samples, rows = genes. |

THESE FUNCTIONS SHOULD NOT BE USED.

`unfinished__m3dropTraditionalDE`

: Uses a log-likelihood ratio test to perform model selection between a model of constant mean expression vs a model of different mean expression across the biological groups.
Probabilities of observing the data given the model are calculated using a zero-inflated negative binomial distribution. Global relationships between mean and dispersion (power-law) as well as mean and
dropouts (Michaelis-Menten) for genes are fit independently for each batch. Dispersions are fixed for each batch and calculated using the fitted power-law using the global mean expression of each gene.
Significance is evaluated using the chi-square distribution.

`unfinished__m3dropTraditionalDEShiftDisp`

: Uses a log-likelihood ratio test to perform model selection between a model of constant mean expression vs a model of different mean expression across the biological groups.
Probabilities of observing the data given the model are calculated using a zero-inflated negative binomial distribution. Global relationships between mean and dispersion (power-law) as well as mean and
dropouts (Michaelis-Menten) for genes are fit independently for each batch. Dispersions are shifted from the global variance according to the mean expression for each biological group, using batch-specific power-laws.
Significance is evaluated using the chi-square distribution.

`broken__m3dropCTraditionalDE`

: Uses a log-likelihood ratio test to perform model selection between a model of constant mean expression vs a model of different mean expression across the biological groups.
Probabilities of observing the data given the model are calculated using a zero-inflated negative binomial distribution. Global relationships between mean and dispersion (power-law) as well as mean and
dropouts (Michaelis-Menten) for genes are fit to the full dataset. Significance is evaluated using the chi-square distribution.

`bg__get_mean2disp`

fits a power-law relationship between the squared coefficient of variation and mean expression of each gene, which is used to calculate the expected dispersion parameter for the negative binomial distribution from a given mean expression value.

`bg__fitdispersion`

estimates gene-specific dispersions from the mean and variance of gene expression values

*r = mu^2/(var-mu)*

. Then fits a power-law relationship between the estimated dispersion and mean exprssion.

`bg__m3dropTraditionalDE`

: a table of observed mean expression levels for each biological group and each batch with raw p-values and FDR corrected p-values for each gene.
`bg__m3dropTraditionalDEShiftDisp`

: a table of observed mean expression levels for each biological group and each batch with raw p-values and FDR corrected p-values for each gene.
`broken__m3dropCTraditionalDE`

: a table of observed mean expression levels for each biological group with raw p-values and FDR corrected p-values for each gene.
`bg__get_mean2disp`

: a function which calculates the expected dispersion given the mean expression.
`bg__fitdispersion`

: exponent of the power-law relationship between dispersion and mean expression.

1 2 3 4 5 6 7 | ```
library(M3DExampleData)
#Normalized_data <- M3DropCleanData(Mmus_example_list$data,
# labels = Mmus_example_list$labels,
# is.counts=TRUE, min_detected_genes=2000)
#DE_output <- bg__m3dropTraditionalDE(Normalized_data$data[1:100,], Normalized_data$labels)
#DE_shifted_output <- bg__m3dropTraditionalDEShiftDisp(Normalized_data$data[1:100,], Normalized_data$labels)
#DE_output_batches <- bg__m3dropTraditionalDE(Normalized_data$data[1:100,], Normalized_data$labels, batches=Normalized_data$labels) # each biological condition was performed separately
``` |

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