This is as a part of PanelBuildeR (https://github.com/exaexa/panelbuilder),
implementing fast variants of the gradient-descent based unmixing. With a bit
of luck, you may be able to use the package standalone as well -- the package
exports a single function nougad which has standard documentation.
Importantly, this package has multiple versions with other fast
implementations. The default version with "canonical" C implementation of the
unmixing is in branch master; you may install it with devtools as follows:
devtools::install_github("exaexa/nougad")After that, either use the function from PanelBuilder, or read the
documentation using ?nougad. A simple benchmarking and testing tool is ready
in function nougad.benchmark.
Utilization of multiple threads is enabled by default and requires no specific
setup. You can use parameter threads=N to precisely set up your desired
number of threads to use for unmixing; default (threads=0) uses all available
CPU threads as reported by std::thread::hardware_concurrency().
Setting threads=1 disables all calls to threading libraries, which may be
helpful on legacy or embedded systems.
The CPU code is written so that it exposes many vectorization possibilities to
the compiler, enabling further speedups. The code typically benefits from
presence of packed-single-float instructions like vfmadd132ps and
vfmadd213ps available on CPUs with AVX FMA extension, or similar ones from
the SSE extensions. Depending on the platform, you may get additional speedup
between 2× and 4× simply by enabling the vectorization for the CPU version.
To do that, you need to instruct R to compile C++ code with
architecture-specific optimization flags. The easiest way is to just set the
"native" architecture by adding the following line to your Makevars
configuration file:
CXXFLAGS += -O3 -march=nativeThe Makevars configuration typically resides in local user's configuration
directory; on UNIX that is usually in ~/.R/Makevars.
CUDA version is installed just as the Vulkan version, using the proper branch from the repository:
devtools::install_github('exaexa/nougad', ref='cuda')You will need a working CUDA compiler (nvcc) for the installation to work;
usually it is sufficient to install the nVidia CUDA toolkit (on debians and
ubuntus, it is in the package nvidia-cuda-toolkit).
You may try a Vulkan variant of the function, which should be able to use your
Vulkan-compatible GPU. You need to have Vulkan C library and headers installed
(usually from package like libvulkan-dev). You also need the ICD runtime for
your hardware (usually in packages such as mesa-vulkan-drivers or
nvidia-vulkan-icd).
Install with:
devtools::install_github('exaexa/nougad', ref='vulkan')In short, it can help you filter lots of unwanted noise from highly-expressed
channels, which vastly reduces the (induced) noise. The following examples show
the problem on a common use-case from cytometry. The data is generated using
the nougad.benchmark function; refer to the source for details. Notably, the
original cell expressions have a very precise expressions around zeroes
(compare that with the very precise measurements from mass cytometry).
Ideally, the expression is only disturbed by positive Poisson noise, and data
never get negative. OLS cannot handle this distinction easily, and produces
problems, mostly resulting in the infamous "spillover spread".
