Description Usage Arguments Details Value Note Author(s) Examples
View source: R/estimate_noise.R
Estimates the dropout model and technical variance from spikeins present in the sample.
1 2 3 4 
SCEList 
A 
plot 
When 
sd_inflate 
An optional parameter to modulate the estimated noise. The estimated standard deviation of spikeins can be scaled by this factor. We recommend leaving the value at the default of 0. 
bins 
The parameter determines the number of bins for comparison of the quality of fit between the mixedmodel and observed data for each spikein alpha in order to calculate the relationship between alpha and mean in the noise model. This should be set lower for small datasets and higher for datasets with more observations 
max_cumprob 
Because a cumulative distribution will range from n=0 to a countable infinity, the event space needs to be set to cover a reasonable fraction of the probability density. This parameter determines the the fraction of probability density covered by the event space, which in turn defines the highes count number in the event space. We recommend users use the default value of 0.9999. 
write.noise.model 
When 
file 
Describes the root name for files written out by 
dropout_inflate 
A scaling parameter for increasing explicitly the number of dropouts present beyond those estimated by spikeins. The value must be greater than 0 or an error will occur. Values below one will diminish dropouts in simulated replicates, and values above one will increase dropouts in simulated replicates. We recommend users use the default value of 1. 
model_view 

alpha_resolution 
Because the alpha parameter is enumerated discretely and empirically evaluated for each value for each spikein, it is necessary to specify the resolution (how small the step is between each explicit alpha test); this parameter defines the resolution of alpha values tested for maximum empirical fit to spikeins. It is recommended that users utilize the default resolution. 
tie_function 
The parameter 
BEARscc consists of three steps: modelling technical variance based on spikeins (Step 1); simulating technical replicates (Step 2); and clustering simulated replicates (Step 3). In Step 1, an experimentspecific model of technical variability ("noise") is estimated using observed spikein read counts. This model consists of two parts. In the first part, expressiondependent variance is approximated by fitting read counts of each spikein across cells to a mixture model (see Methods). The second part, addresses dropout effects. Based on the observed dropout rate for spikeins of a given concentration, the 'dropout injection distribution' models the likelihood that a given transcript concentration will result in a dropout. The 'dropout recovery distribution' is estimated from the dropout injection distribution using Bayes' theorem and models the likelihood that a transcript that had no observed counts in a cell was a false negative. This function performs the first step of BEARscc. For further algorithmic detail please refer to our manuscript methods.
The resulting output of estimate_noiseparameters()
is another
SingleCellExperiment
class object; however four new annotations that
describe the dropout and variance models computed by BEARscc
have been added to the metadata
of the SingleCellExperiment
object. Specifically.

A 

A 

A 
Frequently, the user will want to compute simulated technical replicates in
a high performance computational environment. While the function outputs
the necessary information for create_noiseinjected_counts()
, with
the option write.noise.model=TRUE
users are able to save two tab
delimited files necessary to run HPC_generate_noise_matrices.R
on
a high performance computational cluster. The option file
is used
to indicate the desired root label of the files, "*_bayesianestimates.xls"
and "*_parameters4randomize.xls".
In the examples section, the parameter, alpha_resolution
is set
to 0.25, which is a terrible resolution for estimating noise,
but allows the example to run in reasonable to time for checking
the help files. We recommend the default parameter:
alpha_resolution
=0.005.
David T. Severson <david_severson@hms.harvard.edu>
Maintainer: Benjamin SchusterBoeckler <benjamin.schusterboeckler@ludwig.ox.ac.uk>
1 2 3 4 5 6 7 8 9 10 11 12 13  library("SingleCellExperiment")
data("BEARscc_examples")
#For execution on local machine
BEAR_examples.sce < estimate_noiseparameters(BEAR_examples.sce,
alpha_resolution=0.25, write.noise.model=FALSE)
BEAR_examples.sce
#To save results as files for abnalysis on a
#high performance computational cluster
estimate_noiseparameters(BEAR_examples.sce, write.noise.model=TRUE,
alpha_resolution=0.25, file="noise_estimation",
model_view=c("Observed","Optimized"))

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