PyDESeq2
scanpy
| PyDESeq2 | scanpy | |
|---|---|---|
| 2 | 5 | |
| 515 | 1,757 | |
| 3.5% | 1.7% | |
| 8.4 | 9.3 | |
| 7 days ago | 6 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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PyDESeq2
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DESeq2 Normalization
https://github.com/owkin/PyDESeq2/blob/main/pydeseq2/preprocessing.py I'm not sure but only a normalized count matrix seems not enough to do differential expression analysis. For example, in this script, it gathers also the size factor from the raw read count matrix. Maybe you could find it somewhere or recalculate it.
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When would you use R instead of Python?
PyDESeq2? https://github.com/owkin/PyDESeq2
scanpy
- Renaming Genes for Scanpy Plot
- Useful Python Decorators for Data Scientists
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[Scanpy] Installation issues related to pytables
Upon searching stack, I still do not understand the exact issue, as the file "hdf5extension.cp38-win_amd64" is present within C:\Users\username\anaconda3\lib\site-packages\tables\. Would anyone be able to explain the problem and any potential circumventions?
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standardize/normalize seq data
I would suggest you explore with SCANPY and verify if your batch labels generate a strong separation in your samples (PCA, tSNE, UMAP). If you then need to correct for batches, according to how simple/complex they are, you can choose a tool from this benchmark.
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Flipping one histogram below the axis.
I am plotting 2 1-D histograms on top of one another using a hold on command. Is there a way to have one histogram be upside down, and then to flip the entire plot 90˚? I am looking to create a violin plot https://github.com/theislab/scanpy/issues/1448 on my own, having one histogram on the left, and one on the right.
What are some alternatives?
rnaseq - RNA-seq analyses.
scikit-learn - scikit-learn: machine learning in Python
diffexpr - Porting DESeq2 into python via rpy2
dash - Data Apps & Dashboards for Python. No JavaScript Required.
zarp - The Zavolab Automated RNA-seq Pipeline
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
py-shiny - Shiny for Python
deepvariant - DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
dash-cytoscape - Interactive network visualization in Python and Dash, powered by Cytoscape.js
pagoda2 - R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets
getting-started-with-genomics-tools-and-resources - Unix, R and python tools for genomics and data science
Keras - Deep Learning for humans