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FlashDEG WinNGS

bioinformatics differential-expression genomics ngs rnaseq
Use this command to install FlashDEG:
winget install --id=WinNGS.FlashDEG -e

FlashDEG is a fast, lightweight implementation of the DESeq2 algorithm designed for bulk RNA-seq differential expression analysis. It provides a command-line engine and desktop GUI for x86_64 Windows systems, optimized for speed and memory efficiency.

Key Features:

  • Up to 50x faster than DESeq2 in benchmarked workloads.
  • Memory efficient, using substantially less peak memory; e.g., ~180 MB compared to ~1.1 GB for DESeq2 in a typical run.
  • Lightweight deployment with native executables under 2 MB.
  • Near-exact emulation of DESeq2 behavior for standard bulk RNA-seq workflows.
  • Cross-platform support for Windows, macOS, and Linux.

Audience & Benefit: Ideal for bioinformaticians and researchers in genomics who prioritize speed and resource efficiency. FlashDEG enables faster workflows with reduced memory usage, making it suitable for desktops, servers, and workflow systems where performance and deployment size are critical.

README

FlashDEG

FlashDEG is a fast, lightweight implementation of the DESeq2 algorithm for bulk RNA-seq differential expression analysis. It is designed for local desktops, servers, and workflow systems where runtime, memory use, and deployment size matter. In benchmarked workloads, FlashDEG can run up to 50x faster than DESeq2 and use substantially less memory.

Highlights

  • Fast: up to 50x faster than DESeq2 in benchmarked workloads.
  • Memory efficient: uses substantially less peak memory than DESeq2 in benchmarked workloads; in one approximately 58,000-gene x 17-sample run, FlashDEG used about 180 MB compared with about 1.1 GB for DESeq2.
  • Lightweight: native executable deployment, under 2 MB for the binary alone.
  • Nearly exact DESeq2 emulation: closely matches DESeq2 behavior for standard bulk RNA-seq workflows.
  • Cross-platform: supports Windows/Mac/Linux.

Installation

Pre-Built Binaries

Pre-built releases for Windows, macOS, and Linux are available on the GitHub Releases page. If a pre-built binary is not available for your platform, build from source using one of the build paths below.

Verify Installation

After building or installing, verify the executable:

flashdeg --version

The version command prints only the FlashDEG version. For build provenance, including the git revision, build date, numerical backends, and build flags, use:

flashdeg --build-info

Development builds with uncommitted changes append -dirty to the git revision shown by --build-info.

Quick Start

flashdeg run \
  --counts counts.csv \
  --metadata metadata.csv \
  --design "~ condition" \
  --ref-level "condition=control" \
  --contrast "condition" "treated" "control" \
  --out results.csv

--ref-level and --contrast are both standard — include them together in every invocation. --contrast controls the report direction (log2(treated/control)); --ref-level controls which level is the design-matrix intercept. Omitting --ref-level falls back to alphabetical factor ordering, which is a different (though mathematically equivalent) parameterization and can shift extreme-asymmetry genes near the padj boundary.

Input Files

Versions
1.1.0
Website
github.com
License
Last updated
6/26/2026
Download latest version

Counts

Counts are provided as CSV with feature IDs and sample IDs. By default, FlashDEG expects genes/features as rows and samples as columns:

counts.csv

gene_id,sample1,sample2,sample3,sample4,sample5,sample6
gene_001,10,12,9,28,31,27
gene_002,0,1,0,3,4,2
gene_003,55,48,52,50,47,53
gene_004,4,5,6,18,21,19
gene_005,120,115,130,62,58,65
:

If your file has samples as rows and genes/features as columns, use the advanced --features-as-cols option.

Counts must be non-negative integers by default. For tximport-style estimated counts generated with countsFromAbundance="scaledTPM" or "lengthScaledTPM", add --tximport-round to round non-integer counts with R-compatible rounding before analysis. This option does not read or apply tximport transcript-length offsets.

Metadata

Metadata must contain sample rows whose names match the count sample names. Factors referenced in --design, --contrast, and --ref-level are read from this table:

metadata.csv

sample_id,condition
sample1,control
sample2,control
sample3,control
sample4,treated
sample5,treated
sample6,treated

Output

The main output CSV contains one row per gene:

gene_id,baseMean,log2FoldChange,lfcSE,stat,pvalue,padj
gene_001,19.47,1.69,0.42,4.02,5.80e-05,1.45e-04
gene_002,1.18,2.31,1.12,2.06,0.0394,0.0493
gene_003,50.98,-0.02,0.21,-0.10,0.919,
gene_004,10.12,2.29,0.55,4.16,3.16e-05,1.05e-04
gene_005,87.36,-1.03,0.31,-3.32,0.00090,0.00150
:

Empty padj values mean the gene did not receive an adjusted p-value, usually because of independent filtering or Cook outlier filtering.

LFC Shrinkage (optional)

To compute apeGLM-style shrunken LFC for a single coefficient:

flashdeg run \
  --counts counts.csv \
  --metadata metadata.csv \
  --design "~ condition" \
  --ref-level "condition=control" \
  --contrast "condition" "treated" "control" \
  --out results.csv \
  --lfc-shrink \
  --lfc-shrink-coef "condition[T.treated]" \
  --write-shrunken-lfc shrunken_lfc.csv

--write-shrunken-lfc writes plot-ready columns:

gene_id,baseMean,shrunkenLog2FoldChange,shrunkenLfcSE,pvalue,padj,converged

Use the unshrunken Wald pvalue / padj for DEG calls. The shrunken LFC is intended for effect-size visualization and ranking. FlashDEG emits warnings for extreme shrinkage-prior settings and for suspicious cases where many significant genes are shrunk close to zero.

Likelihood-Ratio Test (optional)

By default FlashDEG runs a Wald test. Use --test LRT with a nested --reduced model to test whether a set of terms (for example all levels of a factor, or a whole interaction) improves the model:

flashdeg run \
  --counts counts.csv \
  --metadata metadata.csv \
  --design "~ batch + condition" \
  --test LRT \
  --reduced "~ batch" \
  --contrast "condition" "treated" "control" \
  --out lrt_results.csv

The output columns are unchanged. Under LRT, stat is the likelihood-ratio statistic and pvalue is its chi-square survival probability; log2FoldChange / lfcSE report the single --contrast for display only (the test itself compares the full design against the reduced one). See docs/commands.md for details.

Status

FlashDEG is designed for common bulk RNA-seq differential expression analysis. It reads a count matrix and sample metadata, compares groups, and reports log2 fold changes, p-values, and adjusted p-values. It can also write VST-normalized values and shrunken LFC values for downstream visualization. For DESeq2 feature compatibility, see docs/deseq2_compatibility.md.

Supported features include:

  • count CSV validation and orientation handling
  • design formulas such as ~ condition and ~ batch + condition
  • group comparisons with --contrast and --ref-level
  • ratio and poscounts size-factor normalization
  • dispersion trend fitting with parametric, mean, and approximate local
  • Wald-test results with BH-adjusted p-values
  • likelihood-ratio tests against a nested reduced model (--test LRT)
  • Cook outlier filtering, replacement, and refit
  • VST output
  • apeGLM-style LFC shrinkage output

Command-Line Options

Use flashdeg run --help to see the options accepted by your installed binary. flashdeg --help shows the top-level commands, flashdeg --version prints the version, and flashdeg --build-info prints build provenance that is useful when reporting results. For the complete CLI reference, see docs/commands.md.

Input And Output

OptionDescription
--counts Count CSV. Required.
--metadata Sample metadata CSV. Required.
--out Output path for the main result CSV.
--tximport-roundRound tximport-style non-integer estimated counts before analysis.

Note: FlashDEG never chooses default output paths. If --out is omitted, no main Wald result CSV is written. Runs without --out only write files explicitly requested with options such as --write-vst-counts, --write-shrunken-lfc, or other --write-* options.

Standard Design And Group Comparison

Use these options for ordinary RNA-seq differential expression analyses. This is the recommended path for most users. See docs/beginner_contrast_guide.md for a beginner-friendly explanation.

OptionDescription
--design Formula subset such as "~ condition" or "~ batch + condition".
--contrast Compare two groups from a metadata column. For example, --contrast "condition" "treated" "control" reports log2(treated/control).
--ref-level Reference group for the design-matrix intercept, e.g. "condition=control". Repeat for multiple factors. Include this flag in every standard invocation alongside --contrast — they are orthogonal: --contrast picks the report direction (numerator / denominator of the Wald test), --ref-level picks the model-matrix parameterization. If omitted, FlashDEG falls back to alphabetical factor ordering (matching the DESeq2 R default for an unrelevelled factor()), which is a mathematically equivalent but numerically distinct parameterization — and at extreme-asymmetry boundary genes the fitted log2FC / pvalue can differ materially.

For advanced options, output controls, profiling options, and the complete CLI reference, see docs/commands.md.

Build

Linux / WSL

Install the system dependencies, then use the Linux preset:

sudo apt-get install -y cmake ninja-build g++ libeigen3-dev libboost-math-dev libopenblas-dev
cmake --preset linux-system
cmake --build --preset linux-system -j
ctest --preset linux-system

For WSL/Linux oracle or debugging runs, use:

cmake --preset linux-dev-system
cmake --build --preset linux-dev-system -j
ctest --preset linux-dev-system

macOS

For a release-oriented macOS build, install the numerical dependencies with Homebrew and configure manually:

xcode-select --install  # only needed if Apple Clang is not installed yet
brew install cmake ninja eigen boost openblas

cmake -S . -B build-macos -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -DFLASHDEG_BUILD_TESTS=ON \
  -DCMAKE_PREFIX_PATH="$(brew --prefix eigen);$(brew --prefix boost);$(brew --prefix openblas)"

cmake --build build-macos
ctest --test-dir build-macos --output-on-failure

The numerical backend options (FLASHDEG_USE_EIGEN, FLASHDEG_USE_BOOST_MATH, FLASHDEG_USE_BLAS, and FLASHDEG_USE_SCIPY_LBFGSB) default to ON; pass them explicitly only when overriding a stale CMake cache or making a developer-only fallback build.

If CMake cannot find Homebrew's OpenBLAS on your machine, use vcpkg instead.

git clone https://github.com/microsoft/vcpkg "$HOME/vcpkg"
"$HOME/vcpkg/bootstrap-vcpkg.sh"
export VCPKG_ROOT="$HOME/vcpkg"

"$VCPKG_ROOT/vcpkg" install eigen3 boost-math openblas
cmake -S . -B build-macos-vcpkg -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -DFLASHDEG_BUILD_TESTS=ON \
  -DCMAKE_TOOLCHAIN_FILE="$VCPKG_ROOT/scripts/buildsystems/vcpkg.cmake"

cmake --build build-macos-vcpkg
ctest --test-dir build-macos-vcpkg --output-on-failure

Windows With vcpkg

Get the FlashDEG source code:

winget install --id Git.Git -e  # skip if git is already installed
cd "$env:USERPROFILE"
git clone https://github.com/tus-kondolab/flashdeg.git
cd flashdeg

Install the required command-line build tools and vcpkg dependencies:

winget install --id Kitware.CMake -e
winget install --id Ninja-build.Ninja -e

cd "$env:SystemDrive\"
git clone https://github.com/microsoft/vcpkg.git vcpkg
.\vcpkg\bootstrap-vcpkg.bat
$env:VCPKG_ROOT = "$env:SystemDrive\vcpkg"
& "$env:VCPKG_ROOT\vcpkg.exe" install eigen3 boost-math openblas

Open Developer PowerShell for Visual Studio so the MSVC compiler environment is active, then configure, build, and test:

cd "$env:USERPROFILE\flashdeg"
cmake --preset vcpkg-ninja
cmake --build --preset vcpkg-ninja
ctest --preset vcpkg-ninja --output-on-failure

If Visual Studio is not installed, install either Visual Studio Build Tools or Visual Studio Community with the Desktop development with C++ workload, then rerun the build from Developer PowerShell.

Release-oriented builds hide and reject developer-only --write-* intermediate outputs. Maintainers who need oracle/debug outputs can use the developer preset:

cmake --preset dev-vcpkg-ninja
cmake --build --preset dev-vcpkg-ninja
ctest --preset dev-vcpkg-ninja --output-on-failure

Use the developer-only fallback below only for FlashDEG development or CI portability checks; it is not a public release build.

Self-contained static build (Windows, no runtime DLLs)

The default vcpkg-ninja build links OpenBLAS dynamically, so the resulting flashdeg.exe needs openblas.dll beside it and the Visual C++ Redistributable on the target machine. For a flashdeg.exe that runs on a clean Windows with no openblas.dll and no VC++ Redistributable, build with the static preset. It uses the x64-windows-static triplet (static OpenBLAS) and /MT (static CRT), so first install the dependencies for that triplet:

& "$env:VCPKG_ROOT\vcpkg.exe" install eigen3 boost-math openblas --triplet x64-windows-static
cmake --preset vcpkg-ninja-static
cmake --build --preset vcpkg-ninja-static
ctest --preset vcpkg-ninja-static --output-on-failure

This is Windows-only (the preset is guarded to the Windows host). The runtime-DLL concern does not apply the same way elsewhere:

  • macOS: link Apple's Accelerate framework (a system framework, always present) and no BLAS library needs shipping; the documented Homebrew path links OpenBLAS as a .dylib that you would otherwise bundle.
  • Linux: glibc is always present, so dynamic linking is normal; vcpkg's x64-linux triplet already builds OpenBLAS statically, while the system (apt) path links libopenblas.so from the distribution.

Developer-Only Fallback Build

The dependency-free no-deps build is for maintainers and CI portability testing. It is not a public release target and should not be used for published analysis results.

Numerical Backends

FlashDEG uses external numerical libraries for reliable and portable numerical calculation:

  • Eigen for linear algebra
  • OpenBLAS / BLAS / LAPACK for accelerated numerical kernels
  • Boost.Math for special functions and statistical distributions
  • a vendored BSD-licensed SciPy L-BFGS-B C translation for bounded optimization

See THIRD_PARTY_NOTICES.md for license and provenance details.

Acknowledgments

FlashDEG implements statistical methods established by the following projects and papers. Please cite the relevant upstream methods in publications that use FlashDEG results:

  • DESeq2: Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550.
  • apeGLM: Zhu, A., Ibrahim, J.G., and Love, M.I. (2019). Heavy-tailed prior distributions for sequence count data. Bioinformatics 35:2084-2092.
  • PyDESeq2: Muzellec, B. et al. (2023). PyDESeq2: a python package for bulk RNA-seq differential expression analysis. Bioinformatics 39:btad547.

See CITATION.cff for machine-readable citation metadata for FlashDEG itself.

License

FlashDEG-owned source code is distributed under the MIT License. See LICENSE.

Bundled and optional third-party components are documented in THIRD_PARTY_NOTICES.md.

Citation

Citation metadata is provided in CITATION.cff.