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This article mainly explains "how to use tsne_analysis.r in R language". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to use tsne_analysis.r in R language.

Cluster analysis of tsne_analysis.r expression

Instructions: $Rscript $scriptdir/tsne_analysis.r-husage: / work/my_stad_immu/scripts/tsne_analysis.r [- h]-I filepath-m metadata [- d dims] [- p perplexity] [--theta theta] [- M initial_dims] [- T top]-g group [- s size] [- an alpha] [- e] [- L] [- X x.lab] [- Y y.lab] [- t title] [- o outdir] [- n prefix] [- H height] [- W width] t-Distributed Stochastic Neighbor Embedding t-SNE analysis: https://www..com/article/1498optional arguments:-h -- help show this help message and exit-I filepath,-- input filepath input the dataset martix [required]-m metadata,-- metadata metadata input metadata filepath [required]-d dims,-- dims dims set Output dimensionality [default=2]-p perplexity -- perplexity perplexity Perplexity parameter (should not be bigger than 3 * perplexity < nrow (X)-1, see details for interpretation) [default 50]-- theta theta Speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE [default 0.5]-M initial_dims -- initial_dims initial_dims the number of dimensions that should be retained in the initial PCA step [default 50]-T top,-- top top select top gene to analysis [default NULL]-g group,-- group group input group id in metadata file to fill color [required]-s size,-- size size point size [optional Default: 3]-an alpha,-- alpha alpha point transparency [0-1] [optional, default: 1]-e,-- ellipse whether draw ellipse [optional, default: False]-L,-- label whether show pionts sample name [optional, default: False]-X x.lab -- x.lab x.lab the label for x axis [optional, default: t-SNE1]-- Y y.lab,-- y.lab y.lab the label for y axis [optional, default: t-SNE2]-t title,-- title title the label for main title [optional, default: t-SNE]-o outdir -- outdir outdir output file directory [default cwd]-n prefix,-- name prefix out file name prefix [default demo]-H height,-- height height the height of pic inches [default 5]-W width,-- width width the width of pic inches [default 5] parameters describe the dimension after the dims parameter is set and reduced. Default is 2perplexity

The degree of confusion, the parameter must be less than (sample number-1) / 3

The larger the theta parameter, the lower the accuracy of the result. The default is the maximum number of iterations of 0.5max_iter. Pca indicates whether to perform PCA analysis on the input original data, and then use the analyzed data for subsequent analysis. Default TRUE

Use example: # dimensionality reduction verification group # t-SNERscript $scriptdir/tsne_analysis.r-I immu/ssgsea.res.tsv-m metadata.group.tsv\-o tsne-g subtype.hclust-p 6-n tsne so far, I believe you have a deeper understanding of "how to use tsne_analysis.r in R language", you might as well do it in practice! Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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