Deconvolution of bulk RNA-Seq data using context-specific deconvolution models based on Deep Neural Networks using scRNA-Seq data as input. These models are able to make accurate estimates of the cell composition of bulk RNA-Seq samples from the same context using the advances provided by Deep Learning and the meaningful information provided by scRNA-Seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> for more details.

rlang, Matrix, Matrix.utils, methods, tidyr,
SingleCellExperiment, SummarizedExperiment, zinbwave, stats,
pbapply, S4Vectors, dplyr, tools, reshape2, gtools, reticulate,
keras, tensorflow, ggplot2, ggpubr, RColorBrewer

rlang, Matrix, methods, tidyr, SingleCellExperiment,
SummarizedExperiment, zinbwave, stats, pbapply, S4Vectors,
dplyr, tools, reshape2, gtools, reticulate, keras, tensorflow,
ggplot2, ggpubr, RColorBrewer

The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.

Allows to get the data from a graph by providing
calibration points

c(
person("Timothée", "Poisot", email =  "tim@poisotlab.io", role  = c("aut")),
person("Rene", "Sachse", email = "rene.sachse@googlemail.com", role = c("aut")),
person("Jaime", "Ashander", email = "jashander@ucdavis.edu", role  = c("aut", "cre")),
person("Tal", "Galili",  email = "tal.galili@gmail.com", role  = c("aut"))
)
Import data from a digital image; it requires user input for
calibration and to locate the data points. The end result is similar to
'DataThief' and other other programs that 'digitize' published plots or
graphs.
Timothée Poisot [aut],
Rene Sachse [aut],
Jaime Ashander [aut, cre],
Tal Galili [aut]

Data-Informed Link Strength. Combine multiple-relationship
networks into a single weighted network. Impute (fill-in)
missing network links.
Combine multiple-relationship networks into a single weighted
network.  The approach is similar to factor analysis in the that
contribution from each constituent network varies so as to maximize the
information gleaned from the multiple-relationship networks.
This implementation uses Principal Component Analysis calculated using
'prcomp' with bootstrap subsampling.  Missing links are imputed using
the method of Chen et al.
(2012).
'GetSampleFromDataFrame.R' 'RelationStrengthSimilarity.R'
'ScalablePCA.R' 'GetSampleFromFile.R' 'GetSampleFromDb.R'
'RssCell.R' 'RssThisRadius.R' 'EdgelistFill.R'
'EdgelistFromAdjacency.R' 'EdgelistFromIgraph.R'
'GenerateDilsNetwork.R' 'MergeEdgelists.R'
'AdjacencyFromEdgelist.R' 'IgraphFromEdgelist.R'
'MeasureNetworkInformation.R' 'RssSuggestedNetwork.R'
'RelativeNetworkInformation.R'