twosigma_custom {twosigma}

Fit the TWO-SIGMA model with custom user-specified model formulas.

Description

Fit the TWO-SIGMA model with custom user-specified model formulas.

Usage

twosigma_custom(
  count_matrix,
  mean_form,
  zi_form,
  id,
  return_summary_fits = TRUE,
  silent = FALSE,
  disp_covar = NULL,
  weights = rep(1, ncol(count_matrix)),
  control = glmmTMBControl(),
  ncores = 1,
  cluster_type = "Fork",
  chunk_size = 10,
  lb = FALSE,
  internal_call = FALSE
)

Arguments

count_matrix

Matrix of non-negative integer read counts, with rows corresponding to genes and columns corresponding to cells. It is recommended to make the rownames the gene names for better output.

mean_form

Custom two-sided model formula for the (conditional) mean model. Formula is passed directly into glmmTMB with random effects specified as in the lme4 package. Users should ensure that the LHS of the formula begins with "count."

zi_form

Custom one-sided model formula for the zero-inflation model. Formula is passed directly into glmmTMB with random effects specified as in lme4.

id

Vector of individual-level (sample-level) ID's. Used for random effect prediction but required regardless of their presence in the model.

return_summary_fits

If TRUE, the package returns a summary.glmmTMB object for each gene. If FALSE, a glmmTMB object is returned for each gene. The latter requires far more storage space.

silent

If TRUE, progress is not printed.

disp_covar

Covariates for a log-linear model for the dispersion. Either a matrix of covariates or = 1 to indicate an intercept only model. Random effect terms are not permitted in the dispersion model.

weights

weights, as in glm. Defaults to 1 for all observations and no scaling or centering of weights is performed.

control

Control parameters for optimization in glmmTMB. See ?glmmTMBControl.

ncores

Number of cores used for parallelization. Defaults to 1, meaning no parallelization of any kind is done.

cluster_type

Whether to use a "cluster of type "Fork" or "Sock". On Unix systems, "Fork" will likely improve performance. On Windows, only "Sock" will actually result in parallelized computing.

chunk_size

Number of genes to be sent to each parallel environment. Parallelization is more efficient, particularly with a large count matrix, when the count matrix is 'chunked' into some common size (e.g. 10, 50, 200). Defaults to 10.

lb

Should load balancing be used for parallelization? Users will likely want to set to FALSE for improved performance.

internal_call

Not needed by users called twosigma_custom directly.

Value

A list with the following elements:

• fit: If return_summary_fits=TRUE, returns a list of model fit objects of class summary.glmmTMB. If return_summary_fits=FALSE, returns a list of model fit objects of class glmmTMB. In either case, the order matches the row order of count_matrix, and the names of the list elements are taken as the rownames of count_matrix.

• gene_error: Vector indicating whether the particular gene produced an error during model fitting (TRUE) or not (FALSE).

Details

This function is likely only needed if users wish to include random effect terms beyond random intercepts. Users should be confident in their abilities to specify random effects using the syntax of lme4.

Examples

# Set Parameters to Simulate Some Data

nind<-10;ncellsper<-rep(50,nind)
sigma.a<-.5;sigma.b<-.5;phi<-.1
alpha<-c(1,0,-.5,-2);beta<-c(2,0,-.1,.6)
beta2<-c(2,1,-.1,.6)
id.levels<-1:nind;nind<-length(id.levels)
id<-rep(id.levels,times=ncellsper)
sim.seed<-1234

# Simulate individual level covariates

t2d_sim<-rep(rbinom(nind,1,p=.4),times=ncellsper)
cdr_sim<-rbeta(sum(ncellsper),3,6)
age_sim<-rep(sample(c(20:60),size=nind,replace = TRUE),times=ncellsper)

# Construct design matrices

Z<-cbind(scale(t2d_sim),scale(age_sim),scale(cdr_sim))
colnames(Z)<-c("t2d_sim","age_sim","cdr_sim")
X<-cbind(scale(t2d_sim),scale(age_sim),scale(cdr_sim))
colnames(X)<-c("t2d_sim","age_sim","cdr_sim")

# Simulate Data

sim_dat<-matrix(nrow=2,ncol=sum(ncellsper))
for(i in 1:nrow(sim_dat)){
   sim_dat[i,]<-simulate_zero_inflated_nb_random_effect_data(ncellsper,X,Z,alpha,beta2
   ,phi,sigma.a,sigma.b,id.levels=NULL)$Y
}
rownames(sim_dat)<-paste("Gene",1:2)

# Run twosigma_custom

twosigma_custom(sim_dat[1:2,],mean_form = count~X,zi_form = ~0,id=id)