scPhere,
a package written in Python and TensorFlow, for learning the
low-dimensional latent structures in single-cell RNA-Seq data.
scumi,
a package written in Python takes FASTQ files as inputs and produces
gene by cell UMI count matrices or read count matrices. We call it the
‘swiss knife’ for summarizing the gene by cell count matrices as it can
be configured to analyze data generated by most scRNA-seq protocols. We
used it in our project systematically comparing seven single-cell
RNA-Seq lab methods.
scvis,
a package written in Python and TensorFlow, is for dimension reduction
of single-cell RNA-seq data, mass-cytometry data, etc. Our method is
among the first extending variational autoencoders for scRNA-seq data
processing.
densityCut
is an R package for clustering high-throughput biological data, and the
time-consuming components are written in C.
xseq is a
statistical model to analyze the impact of individual mutatiosn on gene
expression profiles, written in R and the time-consuming part is written
in C.
mutationSeq
is a Python package predicting somatic single nucleotide variants from
next-generation sequencing data.
DriverNet
is an R package using a combinatorial algorithm to predict cancer driver
genes.
rhsmm
is a hidden semi-Markov model with a mixture of Student’s t observation
distributions to predict copy number variations from array CGH data,
written in Matlab and C++.