Comments (11)
sbslee
commented on August 22, 2024
Thanks for the suggestion! If it's a simple analysis that can be readily implemented, I'm glad to update Dokdo to support it.
That being said, to be honest, I'm still not 100% sure if I understand the type of analysis you're talking about ("correlation (spearman/Pearson) analysis with FDR-correction"). Could you please give me some concrete examples (e.g. figures from a published research article, cartoon drawings of yours)?
P.S. If you are a programmer yourself, you are more than welcome to open a PR :)
from dokdo.
khemlalnirmalkar
commented on August 22, 2024
Hi @sbslee,
Thanks,
This is what I use in R for correlation with FDR/p-correction https://microbiome.github.io/tutorials/Heatmap.html
Scatter plot for correlations (see fig.2) https://journals.asm.org/doi/pdf/10.1128/Spectrum.00034-21
(Fig. 7 & 8) https://rupress.org/jem/article-pdf/218/1/e20200606/1405371/jem_20200606.pdf
and attached a cartoon
FDR-correction for p-values https://en.wikipedia.org/wiki/False_discovery_rate (a common method to correct false positive p-values)
Sorry, i am a microbiologist, not a programmer. I just to implement from available tutorials.
Thought this could be a good thing to have in Dokdo,
Please let me know if you need something
Thanks
from dokdo.
sbslee
commented on August 22, 2024
Sorry, i am a microbiologist, not a programmer.
No worries at all. Your suggestions are already greatly appreciated.
As I understand it, it appears you want to perform cross-correlation analysis between microbiome vs. some matrices for shared samples, similar to the tutorial given by Leo Lahti and Sudarshan Shetty et al. So I looked at the tutorial and devised two methods: cross_association_table and cross_association_heatmap (see their documentations here and here).
Below are examples:
>>> import pandas as pd
>>> import dokdo
>>> otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
>>> lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
>>> df = dokdo.cross_association_table(
... otu, lipids, normalize='log10', nsig=1
... )
>>> df.head(10)
taxon target corr pval adjp
0 Ruminococcus gnavus et rel. TG(54:5).2 0.716496 4.516954e-08 0.002284
1 Uncultured Bacteroidetes TG(56:2).1 -0.698738 1.330755e-07 0.002345
2 Moraxellaceae PC(40:3e) -0.694186 1.733720e-07 0.002345
3 Ruminococcus gnavus et rel. TG(50:4) 0.691191 2.058030e-07 0.002345
4 Lactobacillus plantarum et rel. PC(40:3) -0.687798 2.493210e-07 0.002345
5 Ruminococcus gnavus et rel. TG(54:6).1 0.683580 3.153275e-07 0.002345
6 Ruminococcus gnavus et rel. TG(54:4).2 0.682030 3.434292e-07 0.002345
7 Ruminococcus gnavus et rel. TG(52:5) 0.680622 3.709485e-07 0.002345
8 Helicobacter PC(40:3) -0.673201 5.530595e-07 0.003108
9 Moraxellaceae PC(38:4).1 -0.670050 6.530463e-07 0.003302
>>> import pandas as pd
>>> import dokdo
>>> otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
>>> lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
>>> dokdo.cross_association_heatmap(
... otu, lipids, normalize='log10', nsig=1,
... figsize=(15, 15), cmap='vlag', marksig=True,
... annot_kws={'fontsize': 6, 'ha': 'center', 'va': 'center'}
... )
Compare above results with that from the tutorial:
If you want to give this a try, please install the development version of Dokdo:
$ git clone https://github.com/sbslee/dokdo
$ cd dokdo
$ git checkout 1.13.0-dev
$ pip install -e .
Please let me know if you have any questions/suggestions/concerns. Thanks for the feature request!
from dokdo.
khemlalnirmalkar
commented on August 22, 2024
@sbslee,
This is great, thank you so much for making this function available,
My apologies for the late response,
Is it possible to pick one bacteria and make a scatter plot with p-values on it? (i had added an image earlier)
e.g. for the first bacteria from above results
taxon target corr pval adjp
0 Ruminococcus gnavus et rel. TG(54:5).2 0.716496 4.516954e-08 0.002284
Thanks
from dokdo.
sbslee
commented on August 22, 2024
I added a new method cross_association_regplot that I think gives you what you want.
import pandas as pd
import dokdo
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
df = dokdo.cross_association_table(otu, lipids, normalize='log10', nsig=1)
df.head(10)
To output:
taxon target corr pval adjp
0 Ruminococcus gnavus et rel. TG(54:5).2 0.716496 4.516954e-08 0.002284
1 Uncultured Bacteroidetes TG(56:2).1 -0.698738 1.330755e-07 0.002345
2 Moraxellaceae PC(40:3e) -0.694186 1.733720e-07 0.002345
3 Ruminococcus gnavus et rel. TG(50:4) 0.691191 2.058030e-07 0.002345
4 Lactobacillus plantarum et rel. PC(40:3) -0.687798 2.493210e-07 0.002345
5 Ruminococcus gnavus et rel. TG(54:6).1 0.683580 3.153275e-07 0.002345
6 Ruminococcus gnavus et rel. TG(54:4).2 0.682030 3.434292e-07 0.002345
7 Ruminococcus gnavus et rel. TG(52:5) 0.680622 3.709485e-07 0.002345
8 Helicobacter PC(40:3) -0.673201 5.530595e-07 0.003108
9 Moraxellaceae PC(38:4).1 -0.670050 6.530463e-07 0.003302
Next, draw a scatter plot that shows association between Ruminococcus gnavus et rel. and TG(54:5).2:
ax = dokdo.cross_association_regplot(otu, lipids, 'Ruminococcus gnavus et rel.', 'TG(54:5).2')
ax.text(200, 1.7, 'P = 0.002284\nr = 0.72')
Let me know what you think!
from dokdo.
khemlalnirmalkar
commented on August 22, 2024
Hi @sbslee,
This is great, thank you so much for adding the method,
Do i need to install the new version or v1.13.0 will work?
from dokdo.
sbslee
commented on August 22, 2024
The latest official version is 1.12.0, and the new methods are implemented in the 1.13.0-dev branch. Therefore, you would need to install the development version:
$ git clone https://github.com/sbslee/dokdo
$ cd dokdo
$ git checkout 1.13.0-dev
$ pip install .
from dokdo.
khemlalnirmalkar
commented on August 22, 2024
The latest official version is
1.12.0, and the new methods are implemented in the1.13.0-devbranch. Therefore, you would need to install the development version:$ git clone https://github.com/sbslee/dokdo $ cd dokdo $ git checkout 1.13.0-dev $ pip install .
Okay, thanks @sbslee
from dokdo.
sbslee
commented on August 22, 2024
Have you had a chance to test the new methods (cross_association_table, cross_association_heatmap and cross_association_regplot)? I'd like to publish the official release for 1.13.0 soon and I just wanted to make sure the new methods behave as they should for end users. Please let me know :)
from dokdo.
khemlalnirmalkar
commented on August 22, 2024
@sbslee ,
Yes, i tried, and it looks great to me.
Sorry, i didn't mention it in my previous comment.
Thanks again for adding these new functions.
from dokdo.
sbslee
commented on August 22, 2024
Lovely, thanks for letting me know and for this PR!
from dokdo.
Related Issues (20)
- `alpha_rarefaction_plot` apparently ignores 'N/A' values when plotting
- alpha and beta diversity without qiime file HOT 7
- Plot options for `beta_2d/3d_plot` and `taxa_abundance_box_plot` methods HOT 13
- Implementing differential abundance analysis HOT 2
- dokdo `beta_parallel_plot` issue HOT 1
- taxa_abundance_bar_plot issue HOT 1
- Add the rank abundance curve plot
- exclude "others" from relative abundance and plot HOT 3
- ModuleNotFoundError: No module named 'skbio' HOT 8
- ImportError: libhdf5_cpp.so.200: cannot open shared object file: No such file or directory HOT 2
- Select samples for PCoA HOT 3
- Add taxon name in the pcoa biplot HOT 1
- Overlapping legends on clustermap HOT 3
- How to plot many species on a bar graph HOT 8
- [taxa_abundance_bar_plot] Customizing bar colors with using 'Seaborn palette' or 'Matplotlib colormap' HOT 2
- [taxa_abundance_box_plot] Deletion of notation of species not observed in samples HOT 2
- import process issue HOT 5
- ImportError: cannot import name 'diversity_lib' HOT 2
- KeyError: "['merged'] not in index", after running "dokdo.denoising_stats_plot" HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from dokdo.