Other cells are best fit by a mixture of several topics.įinally, the topic model captures interesting substructure within the T cells.
The mixture proportions uncover some surprises in the cells labeled as “CD34+”: while their expression is largely explained by topic 3 (purple), some cells appear to be mislabeled-they should have been labeled as B cells or CD14+ cells. NK cells show more heterogeneity in expression than the CD14+ and B cells. This arrangement is automated in structure_plot.įrom this Structure plot, it is evident that topics 1 (lighter blue), 2 (green) and 4 (darker blue) closely correspond to the NK, CD14+ and B cell types, respectively the expression in these cells is largely explained by a single topic. Patterns begin to emerge when the cells are arranged so that cells with similar mixture proportions are positioned close to each other. Being proportions, the mixture proportions for each cell must sum to 1, so the total height of the bars is the same for all cells, which makes it easier to compare across cells. The Structure plot is a simply stacked bar chart, in which each topic is represented as a bar of a different colour.
Structure_plot( fit, colors = topic_colors, topics = 1: 6, gap = 25, Topic_colors <- c( "skyblue", "forestgreen", "darkmagenta", "dodgerblue", Then we set the seed so that the results can be reproduced. We begin our analysis by loading the packages. This is in contrast with most methods that require careful pre-processing of the count data. One key difference is that a topic model is a model of count data, so the topic model is intended to be applied directly to the count data that arises from the RNA sequencing assay. Since a topic model analysis is quite different from conventional analyses of single-cell RNA-seq data, in this vignette we carefully point out key differences and clarify possible misconceptions. This first vignette is only intended to explain the topic model analysis at a high level-see Part 2 for additional explanations and guidance. We introduce the basic concepts and fastTopics interface through a simple example.
#Colors 1.1.0 coda 2 how to
(Similarly, the minimum value for a color component is 0.The aim of this vignette is to introduce the basic concepts behind an analysis of single-cell RNA-seq data using a topic model, and to show how to use fastTopics to implement a topic model analysis. Note that because the maximum value for a color component is 1, the red component in the second row does not change. The following table lists the color vectors for the four bars before and after the red translation. The following illustration shows the original image on the left and the transformed image on the right.
Rect(150, 10, width, height), // destination rectangleĠ, 0, // upper-left corner of source rectangle Graphics.DrawImage(&image, 10, 10, width, height) The original image is drawn alongside the transformed image.
#Colors 1.1.0 coda 2 code
Then the code adds 0.75 to the red component of each pixel in the image. The following example constructs an Image object from the file ColorBars.bmp. The color matrix entries that represent translations are given in the following table. A translation adds a value to one or more of the four color components.
0 kommentar(er)
