Create a “Structure plot” from a multinomial topic model fit or other model with “loadings” or “weights”. The Structure plot represents the estimated topic proportions of each sample in a stacked bar chart, with bars of different colors representing different topics. Consequently, samples that have similar topic proportions have similar amounts of each color.
structure_plot(
fit,
topics,
grouping,
loadings_order = "embed",
n = 2000,
colors,
gap = 1,
embed_method = structure_plot_default_embed_method,
ggplot_call = structure_plot_ggplot_call,
...
)
structure_plot_default_embed_method(fit, ...)
# S3 method for class 'poisson_nmf_fit'
plot(x, ...)
# S3 method for class 'multinom_topic_model_fit'
plot(x, ...)
structure_plot_ggplot_call(
dat,
colors,
ticks = NULL,
font.size = 9,
linewidth = 0
)Arguments
- fit
An object of class “poisson_nmf_fit” or “multinom_topic_model_fit”, or an n x k matrix of topic proportions, where k is the number of topics. (The elements in each row of this matrix should sum to 1.) If a Poisson NMF fit is provided as input, the corresponding multinomial topic model fit is automatically recovered using
poisson2multinom.- topics
Top-to-bottom ordering of the topics in the Structure plot;
topics[1]is shown on the top,topics[2]is shown next, and so on. If the ordering of the topics is not specified, the topics are automatically ordered so that the topics with the greatest total “mass” are at shown at the bottom of the plot. The topics may be specified by number or by name. Note that not all of the topics need to be included, so one may also use this argument to plot a subset of the topics.- grouping
Optional categorical variable (a factor) with one entry for each row of the loadings matrix
fit$Ldefining a grouping of the samples (rows). The samples (rows) are arranged along the horizontal axis according to this grouping, then within each group according toloadings_order. Ifgroupingis not a factor, an attempt is made to convert it to a factor usingas.factor. Note that ifloadings_orderis specified manually,groupingshould be the groups for the rows offit$Lbefore reordering.- loadings_order
Ordering of the rows of the loadings matrix
fit$Lalong the horizontal axis the Structure plot (after they have been grouped). Ifloadings_order = "embed", the ordering is generated automatically from a 1-d embedding, separately for each group. The rows may be specified by number or by name. Note thatloadings_ordermay include all the rows offit$L, or a subset.- n
The maximum number of samples (rows of the loadings matrix
fit$L) to include in the plot. Typically there is little to no benefit in including large number of samples in the Structure plot due to screen resolution limits. Ignored ifloadings_orderis provided.- colors
Colors used to draw topics in Structure plot.
- gap
The horizontal spacing between groups. Ignored if
groupingis not provided.- embed_method
The function used to compute an 1-d embedding from a loadings matrix
fit$L; only used ifloadings_order = "embed". The function must accept the multinomial topic model fit as its first input (“fit”) and additional arguments may be passed (...). The output should be a named numeric vector with one entry per row offit$L, and the names of the entries should be the same as the row names offit$L.- ggplot_call
The function used to create the plot. Replace
structure_plot_ggplot_callwith your own function to customize the appearance of the plot.- ...
Additional arguments passed to
structure_plot(for theplotmethod) orembed_method(for functionstructure_plot).- x
An object of class “poisson_nmf_fit” or “multinom_topic_model_fit”. If a Poisson NMF fit is provided as input, the corresponding multinomial topic model fit is automatically recovered using
poisson2multinom.- dat
A data frame passed as input to
ggplot, containing, at a minimum, columns “sample”, “topic” and “prop”: the “sample” column contains the positions of the samples (rows of the L matrix) along the horizontal axis; the “topic” column is a topic (a column of L); and the “prop” column is the topic proportion for the respective sample.- ticks
The placement of the group labels along the horizontal axis, and their names. For data that are not grouped, use
ticks = NULL.- font.size
Font size used in plot.
- linewidth
Passed as the “linewidth” argument to
geom_col.
Value
A ggplot object.
Details
The name “Structure plot” comes from its widespread use in population genetics to visualize the results of the Structure software (Rosenberg et al, 2002).
For most uses of the Structure plot in population genetics, there is usually some grouping of the samples (e.g., assignment to pre-defined populations) that guides arrangement of the samples along the horizontal axis in the bar chart. In other applications, such as analysis of gene expression data, a pre-defined grouping may not always be available. Therefore, a “smart” arrangement of the samples is, by default, generated automatically by performing a 1-d embedding of the samples.
References
Dey, K. K., Hsiao, C. J. and Stephens, M. (2017). Visualizing the structure of RNA-seq expression data using grade of membership models. PLoS Genetics 13, e1006599.
Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A. and Feldman, M. W. (2002). Genetic structure of human populations. Science 298, 2381–2385.
Examples
# Create a Structure plot to visualize topic modeling results
# from the 20 Newsgroups.
set.seed(1)
data(newsgroups)
structure_plot(newsgroups$L,grouping = newsgroups$topics,
topics = paste0("k",c(1,3,6:10)),gap = 10)
#> Running tsne on 94 x 10 matrix.
#> Read the 94 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 30.000000, and theta = 0.100000
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#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.987325)!
#> Learning embedding...
#> Iteration 50: error is 49.731144 (50 iterations in 0.00 seconds)
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#> Running tsne on 101 x 10 matrix.
#> Read the 101 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 32.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.986766)!
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#> Running tsne on 93 x 10 matrix.
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#> Using no_dims = 1, perplexity = 29.000000, and theta = 0.100000
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#> Done in 0.00 seconds (sparsity = 0.982773)!
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#> Running tsne on 113 x 10 matrix.
#> Read the 113 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 36.000000, and theta = 0.100000
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#> Done in 0.00 seconds (sparsity = 0.988801)!
#> Learning embedding...
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#> Running tsne on 103 x 10 matrix.
#> Read the 103 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 33.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.988595)!
#> Learning embedding...
#> Iteration 50: error is 46.671045 (50 iterations in 0.00 seconds)
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#> Running tsne on 101 x 10 matrix.
#> Read the 101 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 32.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.987648)!
#> Learning embedding...
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#> Fitting performed in 0.05 seconds.
#> Running tsne on 96 x 10 matrix.
#> Read the 96 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 30.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.984375)!
#> Learning embedding...
#> Iteration 50: error is 49.286750 (50 iterations in 0.00 seconds)
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#> Running tsne on 107 x 10 matrix.
#> Read the 107 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 34.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.986462)!
#> Learning embedding...
#> Iteration 50: error is 49.771912 (50 iterations in 0.00 seconds)
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#> Fitting performed in 0.06 seconds.
#> Running tsne on 120 x 10 matrix.
#> Read the 120 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 38.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.987222)!
#> Learning embedding...
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#> Fitting performed in 0.08 seconds.
#> Running tsne on 94 x 10 matrix.
#> Read the 94 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 30.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.986646)!
#> Learning embedding...
#> Iteration 50: error is 52.219513 (50 iterations in 0.00 seconds)
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#> Fitting performed in 0.05 seconds.
#> Running tsne on 103 x 10 matrix.
#> Read the 103 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 33.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.988595)!
#> Learning embedding...
#> Iteration 50: error is 50.321373 (50 iterations in 0.00 seconds)
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#> Fitting performed in 0.06 seconds.
#> Running tsne on 94 x 10 matrix.
#> Read the 94 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 30.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.986872)!
#> Learning embedding...
#> Iteration 50: error is 47.489528 (50 iterations in 0.00 seconds)
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#> Iteration 350: error is 0.426933 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.408158 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.408143 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.408144 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.408144 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.408163 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.408144 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.408144 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.408163 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.408144 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.408143 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.408144 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.408144 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.408143 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.05 seconds.
#> Running tsne on 96 x 10 matrix.
#> Read the 96 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 30.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.983073)!
#> Learning embedding...
#> Iteration 50: error is 50.816574 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 51.655822 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 51.973510 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 51.145553 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 53.630333 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 2.152957 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.668019 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.659799 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.659792 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.659792 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.659793 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.659803 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.659791 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.659792 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.659803 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.659792 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.659792 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.659792 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.659804 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.659803 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.05 seconds.
#> Running tsne on 94 x 10 matrix.
#> Read the 94 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 30.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.987551)!
#> Learning embedding...
#> Iteration 50: error is 48.913148 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 53.368158 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 49.262431 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 51.235837 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 47.273122 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 1.659131 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.710522 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.708295 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.708292 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.708294 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.708294 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.708284 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.708283 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.708291 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.708292 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.708294 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.708293 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.708291 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.708283 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.708283 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.05 seconds.
#> Running tsne on 127 x 10 matrix.
#> Read the 127 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 41.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.991196)!
#> Learning embedding...
#> Iteration 50: error is 47.651373 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 47.618166 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 50.118858 (50 iterations in 0.01 seconds)
#> Iteration 200: error is 47.811016 (50 iterations in 0.01 seconds)
#> Iteration 250: error is 46.667571 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 1.123237 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.761821 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.714716 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.714692 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.714693 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.714692 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.714692 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.714693 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.07 seconds.
#> Running tsne on 108 x 10 matrix.
#> Read the 108 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 34.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.986626)!
#> Learning embedding...
#> Iteration 50: error is 49.433925 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 51.420943 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 49.224432 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 51.837915 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 52.855516 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 0.761408 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.593530 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.593513 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.593517 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.593518 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.593516 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.593518 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.593518 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.593515 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.593516 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.593518 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.593515 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.593518 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.593516 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.593518 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.06 seconds.
#> Running tsne on 106 x 10 matrix.
#> Read the 106 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 34.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.989142)!
#> Learning embedding...
#> Iteration 50: error is 49.989419 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 49.267051 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 47.357236 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 52.743697 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 52.211427 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 1.339011 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.947959 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.818245 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.818149 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.818149 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.818150 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.818150 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.06 seconds.
#> Running tsne on 106 x 10 matrix.
#> Read the 106 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 34.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.989320)!
#> Learning embedding...
#> Iteration 50: error is 50.294820 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 51.432132 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 49.297750 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 48.526560 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 51.663951 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 1.284278 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.440363 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.435649 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.435658 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.435658 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.06 seconds.
#> Running tsne on 80 x 10 matrix.
#> Read the 80 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 25.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.983750)!
#> Learning embedding...
#> Iteration 50: error is 51.109518 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 52.614003 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 50.582699 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 52.128496 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 54.090108 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 1.673451 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.728739 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.725225 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.725255 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.725254 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.725255 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.04 seconds.
#> Running tsne on 64 x 10 matrix.
#> Read the 64 x 10 data matrix successfully!
#> Using no_dims = 1, perplexity = 20.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.979980)!
#> Learning embedding...
#> Iteration 50: error is 53.340269 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 51.898011 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 50.917750 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 52.219741 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 51.537589 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 2.318150 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.591885 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.524778 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.524724 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.524724 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.524724 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.524723 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.524724 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.524724 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.524725 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.524724 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.524724 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.524723 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.524723 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.524724 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.03 seconds.
# \donttest{
data(pbmc_facs)
# Get the multinomial topic model fitted to the
# PBMC data.
fit <- pbmc_facs$fit
# Create a Structure plot without labels. The samples (rows of L) are
# automatically arranged along the x-axis using t-SNE to highlight the
# structure in the data.
p1a <- structure_plot(fit)
#> Running tsne on 2000 x 6 matrix.
#> Read the 2000 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.28 seconds (sparsity = 0.183987)!
#> Learning embedding...
#> Iteration 50: error is 56.505122 (50 iterations in 0.15 seconds)
#> Iteration 100: error is 49.730430 (50 iterations in 0.14 seconds)
#> Iteration 150: error is 48.268413 (50 iterations in 0.14 seconds)
#> Iteration 200: error is 47.618307 (50 iterations in 0.14 seconds)
#> Iteration 250: error is 47.250062 (50 iterations in 0.14 seconds)
#> Iteration 300: error is 0.846879 (50 iterations in 0.14 seconds)
#> Iteration 350: error is 0.577612 (50 iterations in 0.14 seconds)
#> Iteration 400: error is 0.463012 (50 iterations in 0.14 seconds)
#> Iteration 450: error is 0.407599 (50 iterations in 0.14 seconds)
#> Iteration 500: error is 0.377741 (50 iterations in 0.15 seconds)
#> Iteration 550: error is 0.360246 (50 iterations in 0.14 seconds)
#> Iteration 600: error is 0.349612 (50 iterations in 0.14 seconds)
#> Iteration 650: error is 0.342569 (50 iterations in 0.14 seconds)
#> Iteration 700: error is 0.337884 (50 iterations in 0.14 seconds)
#> Iteration 750: error is 0.334676 (50 iterations in 0.14 seconds)
#> Iteration 800: error is 0.332405 (50 iterations in 0.14 seconds)
#> Iteration 850: error is 0.330907 (50 iterations in 0.14 seconds)
#> Iteration 900: error is 0.329539 (50 iterations in 0.14 seconds)
#> Iteration 950: error is 0.328665 (50 iterations in 0.14 seconds)
#> Iteration 1000: error is 0.327917 (50 iterations in 0.14 seconds)
#> Fitting performed in 2.77 seconds.
# The first argument to structure_plot may also be an "L" matrix.
# This call to structure_plot should produce the exact same plot as
# the previous call.
set.seed(1)
p1b <- structure_plot(fit$L)
#> Running tsne on 2000 x 6 matrix.
#> Read the 2000 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.28 seconds (sparsity = 0.184733)!
#> Learning embedding...
#> Iteration 50: error is 57.790679 (50 iterations in 0.14 seconds)
#> Iteration 100: error is 49.861301 (50 iterations in 0.14 seconds)
#> Iteration 150: error is 48.283680 (50 iterations in 0.14 seconds)
#> Iteration 200: error is 47.583292 (50 iterations in 0.14 seconds)
#> Iteration 250: error is 47.184974 (50 iterations in 0.14 seconds)
#> Iteration 300: error is 0.827698 (50 iterations in 0.14 seconds)
#> Iteration 350: error is 0.573090 (50 iterations in 0.14 seconds)
#> Iteration 400: error is 0.463332 (50 iterations in 0.14 seconds)
#> Iteration 450: error is 0.408558 (50 iterations in 0.14 seconds)
#> Iteration 500: error is 0.378760 (50 iterations in 0.14 seconds)
#> Iteration 550: error is 0.361389 (50 iterations in 0.14 seconds)
#> Iteration 600: error is 0.350594 (50 iterations in 0.14 seconds)
#> Iteration 650: error is 0.343521 (50 iterations in 0.14 seconds)
#> Iteration 700: error is 0.338752 (50 iterations in 0.14 seconds)
#> Iteration 750: error is 0.335426 (50 iterations in 0.14 seconds)
#> Iteration 800: error is 0.333039 (50 iterations in 0.14 seconds)
#> Iteration 850: error is 0.331320 (50 iterations in 0.14 seconds)
#> Iteration 900: error is 0.329978 (50 iterations in 0.14 seconds)
#> Iteration 950: error is 0.328926 (50 iterations in 0.14 seconds)
#> Iteration 1000: error is 0.328079 (50 iterations in 0.14 seconds)
#> Fitting performed in 2.78 seconds.
# There is no requirement than the rows of L sum up to 1. To
# illustrate, in this next example we have removed topic 5 from the a
# structure plot.
p2a <- structure_plot(fit$L[,-5])
#> Running tsne on 2000 x 5 matrix.
#> Read the 2000 x 5 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.30 seconds (sparsity = 0.184456)!
#> Learning embedding...
#> Iteration 50: error is 57.692237 (50 iterations in 0.15 seconds)
#> Iteration 100: error is 50.412918 (50 iterations in 0.15 seconds)
#> Iteration 150: error is 48.992493 (50 iterations in 0.14 seconds)
#> Iteration 200: error is 48.281585 (50 iterations in 0.14 seconds)
#> Iteration 250: error is 47.808715 (50 iterations in 0.14 seconds)
#> Iteration 300: error is 0.837576 (50 iterations in 0.14 seconds)
#> Iteration 350: error is 0.580283 (50 iterations in 0.14 seconds)
#> Iteration 400: error is 0.473248 (50 iterations in 0.14 seconds)
#> Iteration 450: error is 0.421704 (50 iterations in 0.14 seconds)
#> Iteration 500: error is 0.393696 (50 iterations in 0.14 seconds)
#> Iteration 550: error is 0.377134 (50 iterations in 0.14 seconds)
#> Iteration 600: error is 0.366762 (50 iterations in 0.14 seconds)
#> Iteration 650: error is 0.359971 (50 iterations in 0.14 seconds)
#> Iteration 700: error is 0.355096 (50 iterations in 0.14 seconds)
#> Iteration 750: error is 0.351997 (50 iterations in 0.14 seconds)
#> Iteration 800: error is 0.349703 (50 iterations in 0.14 seconds)
#> Iteration 850: error is 0.348012 (50 iterations in 0.14 seconds)
#> Iteration 900: error is 0.346726 (50 iterations in 0.14 seconds)
#> Iteration 950: error is 0.345763 (50 iterations in 0.14 seconds)
#> Iteration 1000: error is 0.345015 (50 iterations in 0.14 seconds)
#> Fitting performed in 2.78 seconds.
# This is perhaps a more elegant way to remove topic 5 from the
# structure plot:
p2b <- structure_plot(fit,topics = c(1:4,6))
#> Running tsne on 2000 x 6 matrix.
#> Read the 2000 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.27 seconds (sparsity = 0.184641)!
#> Learning embedding...
#> Iteration 50: error is 61.524597 (50 iterations in 0.14 seconds)
#> Iteration 100: error is 50.175128 (50 iterations in 0.14 seconds)
#> Iteration 150: error is 48.374069 (50 iterations in 0.14 seconds)
#> Iteration 200: error is 47.613631 (50 iterations in 0.14 seconds)
#> Iteration 250: error is 47.194635 (50 iterations in 0.14 seconds)
#> Iteration 300: error is 0.843921 (50 iterations in 0.14 seconds)
#> Iteration 350: error is 0.577251 (50 iterations in 0.14 seconds)
#> Iteration 400: error is 0.464073 (50 iterations in 0.16 seconds)
#> Iteration 450: error is 0.409479 (50 iterations in 0.14 seconds)
#> Iteration 500: error is 0.380062 (50 iterations in 0.14 seconds)
#> Iteration 550: error is 0.362996 (50 iterations in 0.14 seconds)
#> Iteration 600: error is 0.352449 (50 iterations in 0.14 seconds)
#> Iteration 650: error is 0.345561 (50 iterations in 0.14 seconds)
#> Iteration 700: error is 0.340884 (50 iterations in 0.14 seconds)
#> Iteration 750: error is 0.337720 (50 iterations in 0.14 seconds)
#> Iteration 800: error is 0.335461 (50 iterations in 0.14 seconds)
#> Iteration 850: error is 0.333799 (50 iterations in 0.14 seconds)
#> Iteration 900: error is 0.332553 (50 iterations in 0.14 seconds)
#> Iteration 950: error is 0.331661 (50 iterations in 0.14 seconds)
#> Iteration 1000: error is 0.330859 (50 iterations in 0.14 seconds)
#> Fitting performed in 2.79 seconds.
# Create a Structure plot with the FACS cell-type labels. Within each
# group (cell-type), the cells (rows of L) are automatically arranged
# using t-SNE.
subpop <- pbmc_facs$samples$subpop
p3 <- structure_plot(fit,grouping = subpop)
#> Running tsne on 392 x 6 matrix.
#> Read the 392 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.05 seconds (sparsity = 0.912081)!
#> Learning embedding...
#> Iteration 50: error is 44.987943 (50 iterations in 0.02 seconds)
#> Iteration 100: error is 44.987953 (50 iterations in 0.02 seconds)
#> Iteration 150: error is 44.987957 (50 iterations in 0.02 seconds)
#> Iteration 200: error is 44.987958 (50 iterations in 0.02 seconds)
#> Iteration 250: error is 44.987959 (50 iterations in 0.02 seconds)
#> Iteration 300: error is 0.400161 (50 iterations in 0.02 seconds)
#> Iteration 350: error is 0.399697 (50 iterations in 0.02 seconds)
#> Iteration 400: error is 0.399704 (50 iterations in 0.02 seconds)
#> Iteration 450: error is 0.399666 (50 iterations in 0.02 seconds)
#> Iteration 500: error is 0.399707 (50 iterations in 0.02 seconds)
#> Iteration 550: error is 0.399706 (50 iterations in 0.02 seconds)
#> Iteration 600: error is 0.399707 (50 iterations in 0.02 seconds)
#> Iteration 650: error is 0.399707 (50 iterations in 0.02 seconds)
#> Iteration 700: error is 0.399707 (50 iterations in 0.02 seconds)
#> Iteration 750: error is 0.399707 (50 iterations in 0.02 seconds)
#> Iteration 800: error is 0.399707 (50 iterations in 0.02 seconds)
#> Iteration 850: error is 0.399666 (50 iterations in 0.02 seconds)
#> Iteration 900: error is 0.399706 (50 iterations in 0.02 seconds)
#> Iteration 950: error is 0.399707 (50 iterations in 0.02 seconds)
#> Iteration 1000: error is 0.399707 (50 iterations in 0.02 seconds)
#> Fitting performed in 0.39 seconds.
#> Running tsne on 88 x 6 matrix.
#> Read the 88 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 28.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.986312)!
#> Learning embedding...
#> Iteration 50: error is 50.402092 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 48.312876 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 51.224970 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 49.298197 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 50.201813 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 1.493906 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.582714 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.574940 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.574931 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.574931 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.574932 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.574929 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.574931 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.574931 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.574931 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.574932 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.574931 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.574929 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.574931 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.574945 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.05 seconds.
#> Running tsne on 362 x 6 matrix.
#> Read the 362 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.05 seconds (sparsity = 0.954626)!
#> Learning embedding...
#> Iteration 50: error is 43.780238 (50 iterations in 0.02 seconds)
#> Iteration 100: error is 43.692080 (50 iterations in 0.02 seconds)
#> Iteration 150: error is 43.022438 (50 iterations in 0.02 seconds)
#> Iteration 200: error is 42.982378 (50 iterations in 0.02 seconds)
#> Iteration 250: error is 42.981766 (50 iterations in 0.02 seconds)
#> Iteration 300: error is 0.277373 (50 iterations in 0.02 seconds)
#> Iteration 350: error is 0.276494 (50 iterations in 0.02 seconds)
#> Iteration 400: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 450: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 500: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 550: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 600: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 650: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 700: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 750: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 800: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 850: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 900: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 950: error is 0.276493 (50 iterations in 0.02 seconds)
#> Iteration 1000: error is 0.276493 (50 iterations in 0.02 seconds)
#> Fitting performed in 0.34 seconds.
#> Running tsne on 376 x 6 matrix.
#> Read the 376 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.05 seconds (sparsity = 0.922306)!
#> Learning embedding...
#> Iteration 50: error is 44.769006 (50 iterations in 0.02 seconds)
#> Iteration 100: error is 43.660439 (50 iterations in 0.02 seconds)
#> Iteration 150: error is 43.624798 (50 iterations in 0.02 seconds)
#> Iteration 200: error is 43.624893 (50 iterations in 0.02 seconds)
#> Iteration 250: error is 43.624954 (50 iterations in 0.02 seconds)
#> Iteration 300: error is 0.339878 (50 iterations in 0.02 seconds)
#> Iteration 350: error is 0.339492 (50 iterations in 0.02 seconds)
#> Iteration 400: error is 0.339481 (50 iterations in 0.02 seconds)
#> Iteration 450: error is 0.339483 (50 iterations in 0.02 seconds)
#> Iteration 500: error is 0.339481 (50 iterations in 0.02 seconds)
#> Iteration 550: error is 0.339481 (50 iterations in 0.02 seconds)
#> Iteration 600: error is 0.339483 (50 iterations in 0.02 seconds)
#> Iteration 650: error is 0.339481 (50 iterations in 0.02 seconds)
#> Iteration 700: error is 0.339482 (50 iterations in 0.02 seconds)
#> Iteration 750: error is 0.339481 (50 iterations in 0.02 seconds)
#> Iteration 800: error is 0.339483 (50 iterations in 0.02 seconds)
#> Iteration 850: error is 0.339482 (50 iterations in 0.02 seconds)
#> Iteration 900: error is 0.339483 (50 iterations in 0.02 seconds)
#> Iteration 950: error is 0.339483 (50 iterations in 0.02 seconds)
#> Iteration 1000: error is 0.339481 (50 iterations in 0.02 seconds)
#> Fitting performed in 0.35 seconds.
#> Running tsne on 782 x 6 matrix.
#> Read the 782 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.10 seconds (sparsity = 0.503146)!
#> Learning embedding...
#> Iteration 50: error is 53.432527 (50 iterations in 0.05 seconds)
#> Iteration 100: error is 47.850065 (50 iterations in 0.05 seconds)
#> Iteration 150: error is 47.671560 (50 iterations in 0.05 seconds)
#> Iteration 200: error is 47.667774 (50 iterations in 0.04 seconds)
#> Iteration 250: error is 47.667691 (50 iterations in 0.05 seconds)
#> Iteration 300: error is 0.375500 (50 iterations in 0.05 seconds)
#> Iteration 350: error is 0.335605 (50 iterations in 0.05 seconds)
#> Iteration 400: error is 0.332115 (50 iterations in 0.05 seconds)
#> Iteration 450: error is 0.331893 (50 iterations in 0.05 seconds)
#> Iteration 500: error is 0.331872 (50 iterations in 0.05 seconds)
#> Iteration 550: error is 0.331870 (50 iterations in 0.05 seconds)
#> Iteration 600: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 650: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 700: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 750: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 800: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 850: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 900: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 950: error is 0.331867 (50 iterations in 0.05 seconds)
#> Iteration 1000: error is 0.331867 (50 iterations in 0.05 seconds)
#> Fitting performed in 0.93 seconds.
# Next, we apply some customizations to improve the plot: (1) use the
# "topics" argument to specify the order in which the topic
# proportions are stacked on top of each other; (2) use the "gap"
# argrument to increase the whitespace between the groups; (3) use "n"
# to decrease the number of rows of L included in the Structure plot;
# and (4) use "colors" to change the colors used to draw the topic
# proportions.
topic_colors <- c("skyblue","forestgreen","darkmagenta",
"dodgerblue","gold","darkorange")
p4 <- structure_plot(fit,grouping = pbmc_facs$samples$subpop,gap = 20,
n = 1500,topics = c(5,6,1,4,2,3),colors = topic_colors)
#> Running tsne on 293 x 6 matrix.
#> Read the 293 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 96.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.03 seconds (sparsity = 0.996214)!
#> Learning embedding...
#> Iteration 50: error is 42.025867 (50 iterations in 0.02 seconds)
#> Iteration 100: error is 42.042972 (50 iterations in 0.02 seconds)
#> Iteration 150: error is 42.031156 (50 iterations in 0.02 seconds)
#> Iteration 200: error is 42.025939 (50 iterations in 0.02 seconds)
#> Iteration 250: error is 42.030010 (50 iterations in 0.02 seconds)
#> Iteration 300: error is 0.318882 (50 iterations in 0.01 seconds)
#> Iteration 350: error is 0.312290 (50 iterations in 0.01 seconds)
#> Iteration 400: error is 0.312274 (50 iterations in 0.01 seconds)
#> Iteration 450: error is 0.312274 (50 iterations in 0.01 seconds)
#> Iteration 500: error is 0.312274 (50 iterations in 0.01 seconds)
#> Iteration 550: error is 0.312274 (50 iterations in 0.01 seconds)
#> Iteration 600: error is 0.312274 (50 iterations in 0.01 seconds)
#> Iteration 650: error is 0.312216 (50 iterations in 0.01 seconds)
#> Iteration 700: error is 0.312101 (50 iterations in 0.01 seconds)
#> Iteration 750: error is 0.311698 (50 iterations in 0.01 seconds)
#> Iteration 800: error is 0.312280 (50 iterations in 0.01 seconds)
#> Iteration 850: error is 0.312273 (50 iterations in 0.01 seconds)
#> Iteration 900: error is 0.312215 (50 iterations in 0.01 seconds)
#> Iteration 950: error is 0.312273 (50 iterations in 0.01 seconds)
#> Iteration 1000: error is 0.312503 (50 iterations in 0.01 seconds)
#> Fitting performed in 0.26 seconds.
#> Running tsne on 72 x 6 matrix.
#> Read the 72 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 22.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.00 seconds (sparsity = 0.976852)!
#> Learning embedding...
#> Iteration 50: error is 51.427256 (50 iterations in 0.00 seconds)
#> Iteration 100: error is 52.160293 (50 iterations in 0.00 seconds)
#> Iteration 150: error is 50.980663 (50 iterations in 0.00 seconds)
#> Iteration 200: error is 53.074444 (50 iterations in 0.00 seconds)
#> Iteration 250: error is 49.797773 (50 iterations in 0.00 seconds)
#> Iteration 300: error is 1.426600 (50 iterations in 0.00 seconds)
#> Iteration 350: error is 0.483151 (50 iterations in 0.00 seconds)
#> Iteration 400: error is 0.479463 (50 iterations in 0.00 seconds)
#> Iteration 450: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 500: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 550: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 600: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 650: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 700: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 750: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 800: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 850: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 900: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 950: error is 0.479465 (50 iterations in 0.00 seconds)
#> Iteration 1000: error is 0.479465 (50 iterations in 0.00 seconds)
#> Fitting performed in 0.03 seconds.
#> Running tsne on 274 x 6 matrix.
#> Read the 274 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 90.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.03 seconds (sparsity = 0.996137)!
#> Learning embedding...
#> Iteration 50: error is 41.606738 (50 iterations in 0.01 seconds)
#> Iteration 100: error is 41.611288 (50 iterations in 0.01 seconds)
#> Iteration 150: error is 41.605878 (50 iterations in 0.01 seconds)
#> Iteration 200: error is 41.599853 (50 iterations in 0.01 seconds)
#> Iteration 250: error is 41.604178 (50 iterations in 0.01 seconds)
#> Iteration 300: error is 0.367406 (50 iterations in 0.01 seconds)
#> Iteration 350: error is 0.366364 (50 iterations in 0.01 seconds)
#> Iteration 400: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 450: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 500: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 550: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 600: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 650: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 700: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 750: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 800: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 850: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 900: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 950: error is 0.366368 (50 iterations in 0.01 seconds)
#> Iteration 1000: error is 0.366368 (50 iterations in 0.01 seconds)
#> Fitting performed in 0.22 seconds.
#> Running tsne on 249 x 6 matrix.
#> Read the 249 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 81.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.02 seconds (sparsity = 0.995145)!
#> Learning embedding...
#> Iteration 50: error is 42.280916 (50 iterations in 0.01 seconds)
#> Iteration 100: error is 42.277288 (50 iterations in 0.01 seconds)
#> Iteration 150: error is 42.271199 (50 iterations in 0.01 seconds)
#> Iteration 200: error is 42.257083 (50 iterations in 0.01 seconds)
#> Iteration 250: error is 42.264906 (50 iterations in 0.01 seconds)
#> Iteration 300: error is 0.286084 (50 iterations in 0.01 seconds)
#> Iteration 350: error is 0.284738 (50 iterations in 0.01 seconds)
#> Iteration 400: error is 0.284736 (50 iterations in 0.01 seconds)
#> Iteration 450: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 500: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 550: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 600: error is 0.284736 (50 iterations in 0.01 seconds)
#> Iteration 650: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 700: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 750: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 800: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 850: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 900: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 950: error is 0.284737 (50 iterations in 0.01 seconds)
#> Iteration 1000: error is 0.284737 (50 iterations in 0.01 seconds)
#> Fitting performed in 0.19 seconds.
#> Running tsne on 612 x 6 matrix.
#> Read the 612 x 6 data matrix successfully!
#> Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
#> Computing input similarities...
#> Building tree...
#> Done in 0.08 seconds (sparsity = 0.639717)!
#> Learning embedding...
#> Iteration 50: error is 49.454618 (50 iterations in 0.04 seconds)
#> Iteration 100: error is 46.556934 (50 iterations in 0.03 seconds)
#> Iteration 150: error is 46.554507 (50 iterations in 0.03 seconds)
#> Iteration 200: error is 46.554507 (50 iterations in 0.03 seconds)
#> Iteration 250: error is 46.554507 (50 iterations in 0.03 seconds)
#> Iteration 300: error is 0.310433 (50 iterations in 0.04 seconds)
#> Iteration 350: error is 0.294343 (50 iterations in 0.03 seconds)
#> Iteration 400: error is 0.293892 (50 iterations in 0.03 seconds)
#> Iteration 450: error is 0.293885 (50 iterations in 0.03 seconds)
#> Iteration 500: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 550: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 600: error is 0.293887 (50 iterations in 0.03 seconds)
#> Iteration 650: error is 0.293886 (50 iterations in 0.04 seconds)
#> Iteration 700: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 750: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 800: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 850: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 900: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 950: error is 0.293886 (50 iterations in 0.03 seconds)
#> Iteration 1000: error is 0.293886 (50 iterations in 0.03 seconds)
#> Fitting performed in 0.69 seconds.
# In this example, we use UMAP instead of t-SNE to arrange the
# cells in the Structure plot. Note that this can be accomplished in
# a different way by overriding the default setting of
# "embed_method".
y <- drop(umap_from_topics(fit,dims = 1))
#> 09:41:09 UMAP embedding parameters a = 1.896 b = 0.8006
#> 09:41:09 Read 3774 rows and found 6 numeric columns
#> 09:41:09 Using FNN for neighbor search, n_neighbors = 30
#> 09:41:09 Commencing smooth kNN distance calibration using 4 threads
#> with target n_neighbors = 30
#> 09:41:09 111 smooth knn distance failures
#> 09:41:09 Initializing from normalized Laplacian + noise (using irlba)
#> 09:41:09 Commencing optimization for 500 epochs, with 138012 positive edges
#> 09:41:09 Using rng type: pcg
#> 09:41:12 Optimization finished
p5 <- structure_plot(fit,loadings_order = order(y),grouping = subpop,
gap = 40,colors = topic_colors)
# We can also use PCA to arrange the cells.
y <- drop(pca_from_topics(fit,dims = 1))
p6 <- structure_plot(fit,loadings_order = order(y),grouping = subpop,
gap = 40,colors = topic_colors)
# In this final example, we plot a random subset of 400 cells, and
# arrange the cells randomly along the horizontal axis of the
# Structure plot.
p7 <- structure_plot(fit,loadings_order = sample(3744,400),gap = 10,
grouping = subpop,colors = topic_colors)
# }