SIMLR 1.26.1
The external R package igraph is required for the computation of the normalized mutual information to assess the results of the clustering.
library(igraph)
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## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
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## decompose, spectrum
## The following object is masked from 'package:base':
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## union
library(SIMLR)
We now run SIMLR as an example on an input dataset from Buettner, Florian, et al. “Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.” Nature biotechnology 33.2 (2015): 155-160. For this dataset we have a ground true of 3 cell populations, i.e., clusters.
set.seed(11111)
data(BuettnerFlorian)
example = SIMLR(X = BuettnerFlorian$in_X, c = BuettnerFlorian$n_clust, cores.ratio = 0)
## Computing the multiple Kernels.
## Performing network diffiusion.
## Iteration: 1
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## Warning in SIMLR(X = BuettnerFlorian$in_X, c = BuettnerFlorian$n_clust, : Maybe
## you should set a larger value of c.
## Performing t-SNE.
## Epoch: Iteration # 100 error is: 0.1293406
## Epoch: Iteration # 200 error is: 0.07376446
## Epoch: Iteration # 300 error is: 0.05963893
## Epoch: Iteration # 400 error is: 0.05958424
## Epoch: Iteration # 500 error is: 0.05953258
## Epoch: Iteration # 600 error is: 0.05948551
## Epoch: Iteration # 700 error is: 0.05944139
## Epoch: Iteration # 800 error is: 0.05940026
## Epoch: Iteration # 900 error is: 0.05936119
## Epoch: Iteration # 1000 error is: 0.05932415
## Performing Kmeans.
## Performing t-SNE.
## Epoch: Iteration # 100 error is: 10.98628
## Epoch: Iteration # 200 error is: 0.8364971
## Epoch: Iteration # 300 error is: 0.6978426
## Epoch: Iteration # 400 error is: 0.5367967
## Epoch: Iteration # 500 error is: 0.419038
## Epoch: Iteration # 600 error is: 0.4244949
## Epoch: Iteration # 700 error is: 0.4438908
## Epoch: Iteration # 800 error is: 0.437931
## Epoch: Iteration # 900 error is: 0.383611
## Epoch: Iteration # 1000 error is: 0.5312541
We now compute the normalized mutual information between the inferred clusters by SIMLR and the true ones. This measure with values in [0,1], allows us to assess the performance of the clustering with higher values reflecting better performance.
nmi_1 = compare(BuettnerFlorian$true_labs[,1], example$y$cluster, method="nmi")
print(nmi_1)
## [1] 0.888298
As a further understanding of the results, we now visualize the cell populations in a plot.
plot(example$ydata,
col = c(topo.colors(BuettnerFlorian$n_clust))[BuettnerFlorian$true_labs[,1]],
xlab = "SIMLR component 1",
ylab = "SIMLR component 2",
pch = 20,
main="SIMILR 2D visualization for BuettnerFlorian")
We also run SIMLR feature ranking on the same inputs to get a rank of the key genes with the related pvalues.
set.seed(11111)
ranks = SIMLR_Feature_Ranking(A=BuettnerFlorian$results$S,X=BuettnerFlorian$in_X)
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head(ranks$pval)
## [1] 1.086748e-128 1.189327e-90 5.504924e-80 4.652359e-75 5.593957e-73
## [6] 3.373056e-69
head(ranks$aggR)
## [1] 5701 1689 7549 57 2653 8081
Similarly, we show an example for SIMLR large scale on an input dataset being a reduced version of the dataset provided in Buettner, Zeisel, Amit, et al. “Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.” Science 347.6226 (2015): 1138-1142. For this dataset we have a ground true of 9 cell populations, i.e., clusters. But we should notice that here we use for computational reasons a reduced version of the data, so please refer to the original publication for the full data.
set.seed(11111)
data(ZeiselAmit)
example_large_scale = SIMLR_Large_Scale(X = ZeiselAmit$in_X, c = ZeiselAmit$n_clust, kk = 10)
## Performing fast PCA.
## Performing k-nearest neighbour search.
## Computing the multiple Kernels.
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## Performing Kmeans.
## Performing t-SNE.
## The main loop will be now performed with a maximum of 300 iterations.
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We compute the normalized mutual information between the inferred clusters by SIMLR large scale and the true ones.
nmi_2 = compare(ZeiselAmit$true_labs[,1], example_large_scale$y$cluster, method="nmi")
print(nmi_2)
## [1] 0.04158302
As a further understanding of the results, also in this case we visualize the cell populations in a plot.
plot(example_large_scale$ydata,
col = c(topo.colors(ZeiselAmit$n_clust))[ZeiselAmit$true_labs[,1]],
xlab = "SIMLR component 1",
ylab = "SIMLR component 2",
pch = 20,
main="SIMILR 2D visualization for ZeiselAmit")