Comments (3)
Hi @lixin4306ren ,
The TSNE function runs a lot faster if you run it on PCA data instead of the expression data. This reduces the number of dimensions down to the number of genes.
If you wish to use TSNE directly on your expression data, you can extract the normalised counts using the normcounts or logcounts functions for use directly with the Rtsne function from the Rtsne package. You can leverage more cores and use the partial_pca argument with this function, that should hopefully speed up the processing time. You can then store it back into the EMSet using the reducedDim function.
from ascend.
Thank you for your prompt reply!
Is there any significant difference between tsne results generated directly from the expression matrix and that based on PCA data? Thanks.
from ascend.
The results will look different, as that's the nature of TSNE. Here's a comparison of a TSNE generated from PCA-reduced values:
And directly from expression data:
from ascend.
Related Issues (20)
- error when PlotGeneralQC(em.set) HOT 3
- A putative error in Adding additional metadata to the EMSet HOT 2
- Failed to convert em.set to SCESet or SingleCellExperiment HOT 1
- Error: BiocParallel errors HOT 11
- Clustering options HOT 1
- Pathway analysis
- Volcano plots
- Vignettes Not Found By R HOT 1
- Problem with "newEMSet" HOT 9
- Problem in running scranNormalise HOT 6
- which data type input to runCORE HOT 2
- error when running runDESeq if both condition.a and condition.b contain more than 1 groups HOT 1
- runCORE removes some cells HOT 2
- "Please ensure 'counts' are in your supplied assay list" HOT 4
- Installation problem HOT 1
- addGeneLabel function is nonexistent HOT 1
- Subset Cells with Gene Label HOT 3
- Using a already processed and normalized dataset.
- ascend EMSet object bug
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 ascend.