import pandas as pd
import matplotlib.pyplot as plt
import scanpy as sc
sc.set_figure_params(dpi=100, frameon=False, color_map='Reds')
/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/scanpy/_settings.py:447: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()` IPython.display.set_matplotlib_formats(*ipython_format)
From scrna-tools.org.
df = pd.read_csv('perturbation-tools.tsv', sep='\t')
df
Tool | Platform | Code | Description | License | Added | Updated | PlatformR | PlatformPy | PlatformCPP | ... | GHLogins | GHCommits | GHIssues | GHClosedIssues | GHPctIssuesClosed | GHMedianResponseDays | GHMedianClosedDays | GHIssueActivity | GHIssueResponse | GHPopularity | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Augur | R | https://github.com/neurorestore/Augur | Augur is an R package to prioritize cell types... | MIT | 2020-01-02 | 2021-07-02 | True | False | False | ... | NaN | 14.0 | 12.0 | 3.0 | 25.000000 | 1.180486 | 4.585359 | 0.430587 | 3.169630 | 1.431245 |
1 | Beyondcell | R | https://gitlab.com/bu_cnio/beyondcell | Beyondcell is a computational methodology for ... | GPL-2.0 | 2021-04-12 | 2021-04-12 | True | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
2 | CellOracle | python | https://github.com/morris-lab/CellOracle | CellOracle integrates single-cell transcriptom... | Apache-2.0 | 2021-01-25 | 2021-01-25 | False | False | False | ... | NaN | 257.0 | 59.0 | 57.0 | 96.610169 | 0.385810 | 8.786991 | 1.472808 | 3.655317 | 1.723191 |
3 | CPA | Python | https://github.com/facebookresearch/CPA | CPA is a deep generative framework to learn ef... | MIT | 2021-04-16 | 2021-04-16 | False | True | False | ... | NaN | 7.0 | 4.0 | 2.0 | 50.000000 | 0.510862 | 3.944132 | 0.721092 | 3.533387 | 2.549845 |
4 | MELD | Python | https://github.com/KrishnaswamyLab/MELD | MELD (Manifold Enhancement of Latent Dimension... | GPL-3.0 | 2019-02-01 | 2021-03-19 | False | True | False | ... | NaN | 336.0 | 11.0 | 9.0 | 81.818182 | 0.703079 | 10.855683 | 0.525024 | 3.394687 | 1.307943 |
5 | MIMOSCA | Python | https://github.com/asncd/MIMOSCA | Multiple Input Multiple Output Single Cell Ana... | MIT | 2018-11-15 | 2021-01-12 | False | True | False | ... | NaN | 243.0 | 8.0 | 5.0 | 62.500000 | 0.072697 | 9.958241 | 0.306348 | 4.380176 | 1.441548 |
6 | MUSIC | R | https://github.com/bm2-lab/MUSIC | MUSIC: Model-based Understanding of SIngle-cel... | Apache-2.0 | 2019-05-31 | 2021-01-12 | True | False | False | ... | NaN | 114.0 | 2.0 | 0.0 | 0.000000 | 317.077650 | NaN | 0.000000 | 0.740525 | 1.175149 |
7 | PhEMD | R | https://github.com/KrishnaswamyLab/phemd | PhEMD (phenotypic earth mover's distance) iden... | NaN | 2021-01-25 | 2021-01-25 | True | False | False | ... | NaN | 10.0 | 5.0 | 5.0 | 100.000000 | 7.549664 | 16.165671 | 0.402925 | 2.363763 | 0.813173 |
8 | PopAlign | python | https://github.com/thomsonlab/popalign | PopAlign constructs a compressed representatio... | NaN | 2021-01-25 | 2021-01-25 | False | False | False | ... | NaN | 265.0 | 5.0 | 3.0 | 60.000000 | 0.679630 | 46.842801 | 0.326758 | 3.409418 | 0.844124 |
9 | PRESCIENT | Python | https://github.com/gifford-lab/prescient | PRESCIENT (Potential eneRgy undErlying Single ... | MIT | 2020-09-10 | 2021-06-04 | False | True | False | ... | NaN | 87.0 | 1.0 | 1.0 | 100.000000 | 2.081644 | 6.324039 | 0.283095 | 2.923284 | 1.196050 |
10 | SCATTome | R | https://github.com/bvnlab/SCATTome | SCATTome (Single Cell Analysis of Targeted Tra... | GPL-3.0 | 2021-01-25 | 2021-01-25 | True | False | False | ... | NaN | 26.0 | 1.0 | 1.0 | 100.000000 | 0.256736 | 83.250637 | 0.068492 | 3.832204 | 0.127640 |
11 | scDEAL | Python | https://github.com/OSU-BMBL/scDEAL | Deep Transfer Learning of Drug Sensitivity by ... | Apache-2.0 | 2021-08-08 | 2021-08-08 | False | True | False | ... | NaN | 2.0 | 0.0 | 0.0 | NaN | NaN | NaN | 0.000000 | NaN | 1.700971 |
12 | scGen | Python | https://github.com/theislab/scgen | scGen is a generative model to predict single-... | GPL-3.0 | 2019-08-05 | 2021-01-12 | False | True | False | ... | NaN | 361.0 | 42.0 | 42.0 | 100.000000 | 8.797106 | 37.129219 | 1.199783 | 2.297351 | 1.967889 |
13 | scMAGeCK | R/Python | https://bitbucket.org/weililab/scmageck | scMAGeCK is a computational model to identify ... | BSD-2-Clause | 2020-02-05 | 2021-01-12 | True | True | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
14 | scTenifoldKnk | R/Python/MATLAB | https://github.com/cailab-tamu/scTenifoldKnk | Perform virtual knockout experiments on single... | GPL-2.0-or-later | 2021-03-26 | 2021-07-27 | True | True | False | ... | NaN | 239.0 | 6.0 | 6.0 | 100.000000 | 0.185932 | 0.594566 | 0.694318 | 3.972337 | 1.061626 |
15 | trVAE | Python | https://github.com/theislab/trVAE | trVAE is a deep generative model which learns ... | MIT | 2019-11-03 | 2021-06-28 | False | True | False | ... | NaN | 843.0 | 10.0 | 8.0 | 80.000000 | 3.857726 | 18.720347 | 0.640489 | 2.655359 | 1.367855 |
16 rows × 46 columns
df.Platform.replace({'python':'Python'}).value_counts().plot(kind='pie', startangle=290)
<AxesSubplot:ylabel='Platform'>
df = pd.read_csv('personal.csv')
df
DOI | Treatment | Technique | Shorthand | Measurement | # perturbations | # cell types | # doses | # timepoints | Cell source | Availability | Author | Year | Raw | Processed | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 10.1016/j.cell.2016.11.038 | CRISPR | Perturb-seq | Dixit et al (2016) | RNA-seq | 10,24 | 1 | - | 1-2 | TFs followed by LPS treatment in BMDCs, TFs in... | SCP | Dixit | 2016 | https://ndownloader.figshare.com/files/34011689 | https://ndownloader.figshare.com/files/34014608 |
1 | 10.1126/science.1247651 | CRISPR | CRISP-seq | Jaitin et al (2016) | RNA-seq | 8-22 | 1 | - | 1 | TFs, in vitro hemato and in vivo data. CRISP-s... | processed | Jaitin | 2016 | NaN | NaN |
2 | 10.1038/nmeth.4177 | CRISPR | CROP-seq | Datlinger et al (2017) | RNA-seq | 29 | 1 | - | 1 | TFs and T-cell receptors pathway targets (3x g... | processed | Datlinger | 2017 | NaN | NaN |
3 | 10.1038/nmeth.4604 | CRISPR | CROP-seq | Hill et al (2018) | RNA-seq | 32 | 1 | 2 | 1 | targeting tumor surpressors in MCF10A with dox... | processed | Hill | 2018 | NaN | NaN |
4 | 10.1101/2020.11.16.383307 | CRISPR | Perturb-seq | Ursu et al (2020) | RNA-seq | 200 | 1 | - | 1 | 100 variants each for 2 genes | unavailable | Ursu | 2020 | NaN | NaN |
5 | 10.1126/science.aaz6063 | CRISPR | Perturb-seq | Jin et al (2020) | RNA-seq | 35 | - | - | 1 | in vivo mouse brain development (2x per, frame... | SCP | Jin | 2020 | NaN | NaN |
6 | 10.1038/s41588-021-00779-1 | CRISPR | Perturb-CITE-seq | Frangieh et al (2021) | RNA+protein | 248 | 1 | - | 1 | treatment resistant cancer samples, patient de... | SCP | Frangieh | 2021 | https://ndownloader.figshare.com/files/34012565 | https://ndownloader.figshare.com/files/34013717 |
7 | 10.1038/s41592-021-01153-z | CRISPR KO + antibody | scifi-RNA-seq | Datlinger et al (2021) | RNA-seq | 96 | 1 | 1 | 1 | 20 target genes (2x gRNA per) in Jurkat cells,... | GSE168620 | Datlinger | 2021 | NaN | NaN |
8 | 10.1126/science.aax4438 | CRISPRa | Perturb-seq | Norman et al (2019) | RNA-seq | 287 | 1 | - | 1 | induction of gene pair targets+single gene con... | processed | Norman | 2019 | https://ndownloader.figshare.com/files/34002548 | https://ndownloader.figshare.com/files/34027562 |
9 | 10.1016/j.cell.2016.11.048 | CRISPRi | Perturb-seq | Adamson et al (2016) | RNA-seq | 9-93 (sgRNA) | 1 | - | 1 | contains combinatorial guide delivery. Perturb... | processed | Adamson | 2016 | NaN | NaN |
10 | 10.1016/j.cell.2018.11.029 | CRISPRi | CROP-seq | Gasperini et al (2019) | RNA-seq | 1119, 5779 | 1 | - | 1 | 2 experiments, CRISPRi of enhancer region | processed | Gasperini | 2019 | NaN | NaN |
11 | 10.1038/s41592-020-0837-5 | CRISPRi | TAP-seq | Schraivogel et al (2020) | RNA-seq | 1778 (enhancers) | 1 | - | 1 | targeted enhancers on two chromosomes in K562 ... | processed | Schraivogel | 2020 | NaN | NaN |
12 | 10.1038/s41587-019-0387-5 | CRISPRi | Perturb-seq | Jost et al (2020) | RNA-seq | 25 | 2 | - | 1 | 4 experiments, sgRNA variants with mismatch | processed | Jost | 2020 | NaN | NaN |
13 | 10.1038/s41588-021-00778-2 | CRISPR | ECCITE-seq | Papalexi et al (2021) | RNA-seq | 111 (sgRNA) | 1 | 2 | - | IFNg, DAC, and TGFb induced THP-1 cells, analy... | processed | Papalexi | 2021 | NaN | NaN |
14 | 10.1016/j.cels.2020.06.004 | CRISPRa | - | Alda-Catalinas et al (2020) | RNA-seq | 230 | 1 | - | - | zygotic genomic activation factors in mouse ES... | GSE135621 | Alda-Catalinas | 2020 | NaN | NaN |
15 | 10.1101/2021.08.23.457400 | CRISPRi | CROP-seq | Leng et al (2021) | RNA-seq | 30 | 1 | 2 | - | IL-1α+TNF+C1q in human IPSC-derived astrocytes | GSE182308 | Leng | 2021 | NaN | NaN |
16 | NaN | genetic targets | NaN | Replogle et al (2020) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Replogle | 2020 | NaN | NaN |
17 | 10.1101/2021.12.16.473013v3 | genetic targets | Perturb-seq | Replogle et al (2021) | RNA-seq | >10000 | 2 | - | - | NaN | upon publication | Replogle | 2021 | NaN | NaN |
18 | 10.1126/science.aax6234 | small molecules | sci-Plex | Srivatsan et al (2019) | RNA-seq | 188 | 3 | 4 | 2 | in vitro cancer cell lines and small molecules | processed | Srivatsan | 2019 | https://ndownloader.figshare.com/files/33979517 | NaN |
19 | 10.1126/sciadv.aav2249 | small molecules | multiplexed | Shin et al (2019) | RNA-seq | 45 | 2 | 1 | 1 | transfected barcodes label perturbation condit... | unavailable | Shin | 2019 | NaN | NaN |
20 | 10.1038/s41467-020-17440-w | small molecules | MIX-seq | McFarland et al (2020) | RNA-seq | 1-13 | 24-99 | 1 | 1-5 | 4 small molecule experiments, one genetic | processed | McFarland | 2020 | NaN | NaN |
21 | 10.1038/s41592-019-0689-z | small molecules | CyTOF | Chen et al (2020) | protein | 300 | 1 | 1 | 1 | breast cancer cells undergoing TGF-β-induc... | - | Chen | 2020 | NaN | NaN |
22 | 10.1101/2020.04.22.056341 | small molecules | scRNA-seq | Zhao et al (2020) | RNA-seq | 2,6 | 6,1 | - | - | compounds applied to patient resections | processed | Zhao | 2020 | NaN | NaN |
df.Treatment.value_counts().plot(kind='pie', startangle=290)
<AxesSubplot:ylabel='Treatment'>
df.Technique.value_counts().plot(kind='pie', startangle=290)
<AxesSubplot:ylabel='Technique'>