这个20G的单细胞项目数据和代码的压缩包有什么（python数据分析典范）

近日，微信群里的宝藏唐医生在群里甩了一个百度云链接，我瞅了一眼，居然是20G，而且命名超级诡异，是9044782e-cb96-4733-8a4f-bf42c21399e6_v1.0.zip。

差点误解为某种福利呢，聊了聊才分析是单细胞发一篇文章的全部代码和数据，在code ocean搬运过来的，文章链接是：https://elifesciences.org/articles/43803/figures，关于code ocean，见：代码海洋-你想模仿的这里都有啊

All of the analyzed real datasets are publicly available and the relevant GEO accession codes are included in the manuscript. All of the simulated and real data can be accessed through Code Ocean at the following URL: https://doi.org/10.24433/CO.9044782e-cb96-4733-8a4f-bf42c21399e6. cNMF code is available on Github https://github.com/dylkot/cNMF/ (copy archived at https://github.com/elifesciences-publications/cNMF).

反正电脑空间大（8T），网速也超级快（200M光纤），而且是会员，就顺手下载了，如果你也有这个条件，可以考虑下载哈。

链接: https://pan.baidu.com/s/1MObwTPXjDbPb3EU1alv-Wg  密码: ou19

下载后，就发现 D:\BaiduNetdiskDownload\9044782e-cb96-4733-8a4f-bf42c21399e6_v1.0.zip: 压缩文件已损坏

Part1_Simulations\deloc_0.50\Seed_27157\LDA_Spectra_IndvRefitZscore.npz 里出现校验和错误。该文件已损坏。
Part1_Simulations\deloc_0.50\Seed_9925\ICA_Spectra_IndvRefitTPM.npz 里出现校验和错误。该文件已损坏。
Part1_Simulations\deloc_0.75\Seed_20008\cLDA\cnmf_tmp\cLDA.spectra.k_14.merged.df.npz 里出现校验和错误。该文件已损坏。
Part1_Simulations\deloc_0.75\Seed_31986\NMF_Spectra_IndvRefitZscore.npz 里出现校验和错误。该文件已损坏。
Part1_Simulations\deloc_1.00\Seed_18839\LDA_Spectra_IndvRefitTPM.npz 里出现校验和错误。该文件已损坏。
Part1_Simulations\deloc_1.00\Seed_29975\cLDA\cnmf_tmp\cLDA.spectra.k_14.merged.df.npz 里出现校验和错误。该文件已损坏。
Part1_Simulations\deloc_1.00\Seed_9485\cICA\cnmf_tmp\cICA.spectra.k_14.merged.df.npz 里出现校验和错误。该文件已损坏。
Part2_Organoids\Organoid_cNMF\cnmf_tmp\Organoid_cNMF.spectra.k_31.merged.df.npz 里出现校验和错误。该文件已损坏。
Part3_VisualCortex\HrvatinEtAl_cNMF\cnmf_tmp\HrvatinEtAl_cNMF.spectra.k_28.merged.df.npz 里出现校验和错误。该文件已损坏。


但是诡异的是，并不影响它的其它部分内容的解压。

然后，我使用命令简单探索了一下这个压缩包的内容，可以看到，全部的20G，绝大部分都在data目录，详情如下:

$ du -h -d 2  data/
4.4G    data/Part1_Simulations/deloc_0.50
4.4G    data/Part1_Simulations/deloc_0.75
5.0G    data/Part1_Simulations/deloc_1.00
350M    data/Part1_Simulations/deloc_1.00_halfDoublets
5.0K    data/Part1_Simulations/runtime_evaluation
14G     data/Part1_Simulations
3.5M    data/Part2_Organoids/genesets
1.4G    data/Part2_Organoids/Organoid_cNMF
2.0G    data/Part2_Organoids
86M     data/Part3_VisualCortex/genesets
1.5G    data/Part3_VisualCortex/HrvatinEtAl_cNMF
1.3G    data/Part3_VisualCortex/TasicEtAl_cNMF
2.8G    data/Part3_VisualCortex
19G     data/


其中，Part1_Simulations的14G占大头，另外的 Part2_Organoids 和 Part3_VisualCortex里面内容也是数据文件罢了。

但是代码文件里面有非常信息的全部单细胞数据分析图表：

 

不过是python的jupyter notebook文件，会python的朋友肯定不怕啦！

pip install jupyter notebook
# 启动Jupyter notebook
jupyter notebook


全部的文献图表目录如下：

Part 1 simulations

• Step 0. Estimate the simulation parameters
• Step 1. Simulate the scRNA-seq data
• Step 2. Preprocess the scRNA-seq data
• Step 3a. Run cNMF
• Step 3b. Run cICA
• Step 3c. Run cLDA
• Step 3d. Run PCA
• Step 3e. Run clustering
• Step 3f. Refit to TPM and Z-scores for indivdiual factorizations
• Step 4. Compare robustness of matrix factorization methods
• Step 5. Compare accuracy of deconvolutions
• Step 6. Plot inferred usages for cells over tSNE plots
• Step 7. Evaluate cNMF inference of doublets and activity GEP usage
• Step 8. Compare matrix factorization runtimes
• Step 9. Analyze high doublet rate simulation

Part 2 organoids

• Step 1. Preprocess Quadrato Et Al organoid data
• Step 2. Run cNMF on Quadrato Et al organoid data and analyze the results

Part 3 visual cortex

• Step 1. Preprocess Hrvatin Et Al visual cortex data
• Step 2. Preprocess Tasic Et Al visual cortex data
• Step 3. Run cNMF on Hrvatin Et Al visual cortex data
• Step 4. Run cNMF on Tasic Et Al visual cortex data
• Step 5. Analyze cNMF results

比如单独查看 Part2_Organoids的Step2_Run_cNMF_QuadratoEtAl

Run cNMF on the Quadrato et al brain organoid data and analyze the cell-type identity programs (in particular the mesodermal programs) and the cell-cycle and hypoxia programs present in this data

Figures and calculations:

• Supplementary figure 8a: K selection plot
• Supplementary figure 8b: Replicate consensus plots before and after filtering
• Figure 2a: heatmap of GEP usages per cell
• Supplementary figure 9: Co-usage vs pearson correlation of the GEPs
• Supplementary figure 11: GEP usage overlap with published clusters
• Figure 2d: muscle cell marker genes
• Figure 2e: activity GEP marker genes
• Figure 2c: Gene-set enrichments
• Figure 2g: hypoxia GEP usage by cell-type
• Figure 2f: Cell cycle GEP usage by cell-type
• Co-usage of the proliferative precursor and immature muscle GEPs calculation
• Figure 2b: tSNE plot of GEP usage
• Supplementary figure 10: tSNE of GEP usage with separate panels for activity GEPs

基本上是python绘图的集大成者，还是蛮有意思的，虽然我们一直强调R和Linux才是生信工程师的基础，不过，学python的群体也不小：

 

如果你要学习python

其实B站已经有太多的大神了，简单的关键词搜索：https://search.bilibili.com/all?keyword=python&from_source=nav_suggest_new&order=dm&duration=0&tids_1=0

当然了，如果你做生信的要学习python，我觉得这个单细胞项目的代码，就非常值得你试试看！

 

如果你下载这20G的单细胞全部文件代码很困难，我也提供了仅仅是代码的打包文件：https://share.weiyun.com/mSLJxVcm 希望你学的开心