大家好，這一次我們來分享一下有關(guān)做10X空間轉(zhuǎn)錄組的分析軟件SPATA，其實關(guān)于10X空間轉(zhuǎn)錄組做軌跡分析的文章之前分享過一個，文章在10X空間轉(zhuǎn)錄組的軌跡分析，其中用到的軟件是stlearn，我們放一張stlearn分析出來的圖
今天我們來分享另外一個軟件，SPATA，文章在Inferring spatially transient gene expression pattern from spatial transcriptomic studies，我們還是先分享文獻，最后分享示例代碼。
Abstract
Spatial transcriptomic is a technology to provide deep transcriptomic profiling by preserving the spatial organization.（這個老生常談了）。Here, we present a framework for SPAtial Transcriptomic Analysis (SPATA, https://themilolab.github.io/SPATA), to provide a comprehensive characterization of spatially resolved gene expression, regional adaptation of transcriptional programs and transient dynamics along spatial trajectories.（運用這個軟件來做空間軌跡分析，講道理，我很好奇！！）。
Brief Communication（這個地方我們總結(jié)一下）
Deep transcriptional profiling of single cells by RNA-sequencing maps the cellular composition of tissue specimens regarding cellular origin, developmental trajectories and transcriptional programs（這是單細胞的優(yōu)勢），However, information determining the spatial arrangement of specific cell types or transcriptional programs are lacking and thus can only be predicted indirectly（空間信息的缺失），which is a considerable drawback of this method.（也是眾所周知的缺點）。空間組織結(jié)構(gòu)傳統(tǒng)的方式是用成像技術(shù)提供高精度的信息，但是同時檢測的基因和蛋白的數(shù)量非常少，當然，已經(jīng)有了一些新的空間組織檢測技術(shù)，但受到空間分布細胞精度和深度的制約，Further, data integration, visualization and analysis of transcriptomic and spatial information remains challenging. Here, we present a software tool to provide a framework for integration of high-dimensional transcriptional data within a spatial context.這個軟件集友好的可視化界面，分割或軌跡分析，提供了廣泛了不同分析方法，In particular, we focus on transient changes of gene expression and aim to infer transcriptional programs that are dynamically regulated as a function of spatial organization.（這個軟件就是專門為10X空間轉(zhuǎn)錄組做準備的）。
In order to present an overview of possible analytic capabilities of the SPATA workflow,
generated spatial transcriptomic datasets from human cortex and human glioblastoma samples using the Visium technology (10X Genomics). The human cortex is separated into defined layers containing different types of neurons and cellular architecture（空間轉(zhuǎn)錄組數(shù)據(jù)是事先注釋好的），
- In a first step, we combine shared-nearest neighbor clustering and spatial pattern recognition by an external tool（SPARK）in order to determine genes with a defined spatially resolved expression pattern.（這個地方類似于Seurat計算空間差異基因）。We found that the cortical layering is accurately reflected by our clustering approach（這個很自然）。In order to gain insights into the spatial organization we provided a tool to compute the spatial distance within the defined layers or correspondent clusters. An increasing distance within individual clusters allows to differentiate between narrowly related or a widespread dispersion of spots within the cluster.(計算空間距離，這個在我分享的文章空間轉(zhuǎn)錄組細胞類型的距離分析之二---代碼實現(xiàn)，提到過,只不過注意，這里計算的是cluster內(nèi)部的距離)。
- Next, the spatial overlap of transcriptional programs or gene expression was analyzed using a Bayesian approach（有關(guān)貝葉斯的知識大家可以參考我的文章貝葉斯分類器(10X單細胞和10X空間轉(zhuǎn)錄組的基礎(chǔ)算法)）,resulting in an estimated correlation which quantifies the identical arrangement of expression in space.
- In a further step, we aimed to analyze dynamic changes, which were annotated using pseudotime estimation or RNA-velocity.(軌跡推斷)。We directly implemented the pseudotime inference from the monocle3 package, but also allow the integration of any other tool such as “l(fā)atent-time” extracted from RNA-velocity (scVelo). （看來這個軟件的軌跡推斷需要借助單細胞的軌跡分析軟件）。Another option for dynamic gene expression analysis is the detection of defined transcriptional programs along a defined trajectory.（基因的動態(tài)表達）。我們在方法中重點關(guān)注一下這個。
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Moreover, we provide the opportunity to screen for gene expression or transcriptional programs which transiently change along predefined trajectories by modelling gene expression changes in accordance to various biologically relevant behaviors.（軌跡分析的轉(zhuǎn)變基因）。
圖片.png
圖片.png
這個地方我們稍微討論一下 - 第一步，SPARK識別空間pattern，這個pattern需要人為指定，看來這個劃分了3個pattern
- 第二步，各個pattern在空間上的映射，這個地方主要對各個pattern做一下富集，看看區(qū)域分布
- 第三步，Copy-number analysis of the sample and cluster annotation（空間注釋）
- 第四步，Comparison of recognized pattern with known gene expression classification （識別模式與已知基因表達分類的比較 ，看看是否一致）
- 第五步，模式中顯著表達的途徑的表達（就是顯著富集的通路）
- 第六步，腫瘤區(qū)域的空間軌跡分析。
- 第七步，也是最為重要的一步，Change of z-scored geneset expression along the trajectory (i) and marker genes of microenvironmental alterations and inflammation（關(guān)鍵基因的變化）。
看來對空間上的動態(tài)變化基因還是要引起重視。不過這里空間SPOT做inferCNV有待商榷。
還有一個很有意思的地方，Thus, we were able to show 116 that immune related genes from myeloid cells and reactive astrocytes were localized in a “glial-scars” resembling structure, sharply separating normal brain from tumor regions Figure 2h. We observed a transient increase of macrophage and microglia activation directed towards the tumor boarder,這個方法是不是值得一試呢？。
Methods，我們關(guān)注一下其中的重點
Modeling of transient gene expression along spatial trajectories
Spatial distance measurement
Spatial overlap and correlation analysis
我們來分享一下示例代碼
這里的示例代碼很多，我們關(guān)注一下軌跡部分的代碼
Vsiualization
Spatial trajectories
Segmentation
DE - Analysis
空間軌跡推斷
With spatial trajectory analysis SPATA introduces a new approach to find, analyze and visualize differently expressed genes and gene-sets in a spatial context. While the classic differential gene expression analyzes this differences between experimental groups as a whole it neglects changes of expression levels that can only be seen while maintaining the spatial dimensions. Spatial trajectories allow to answer questions that include such a spatial component. E.g.:
- In how far do expression levels change the more we move towards a region of interest?
- Which genes follow the same pattern along these paths?
The spatial trajectory tools provided in SPATA enable new ways of visualization as well as new possibilities to screen for genes, gene-sets and patterns of interest.
load package
[library](https://rdrr.io/r/base/library.html)(SPATA)
[library](https://rdrr.io/r/base/library.html)(magrittr)
[library](https://rdrr.io/r/base/library.html)(ggplot2)
[library](https://rdrr.io/r/base/library.html)(patchwork)
load spata-object
spata_obj <- [loadSpataObject](https://themilolab.github.io/SPATA/reference/loadSpataObject.html)(input_path = "data/spata-obj-trajectory-analysis-tumor-example.RDS")
Draw trajectories
Spatial trajectories of a sample in a given spata-object can be drawn interactively using the function createTrajectories() as shown in the example below. createTrajectories() opens a mini-shiny application. This app allows one the one hand to investigate the sample with regards to spatial gene expression like plotSurfaceInteractive() does and on the other hand to draw trajectories through the areas of interest in four easy steps.
library(SPATA)
# open interactive application
spata_obj <- [createTrajectories](https://themilolab.github.io/SPATA/reference/createTrajectories.html)(object = spata_obj)
1. Drawing
Draw the trajectory interactively by clicking on the locations where you want the start and endpoints to be. If you click more than two times the trajectory is divided into sub-trajectory parts which will be displayed by subsequent plotting functions. Hence, it makes sense to stop everytime where you would expect a change as this will be highlighted by the transition from part to the subsequent one.
Figure 1.1. Interactive trajectory drawing
2. Highlighting
If start- and end-points are set adjust the trajectory width to and click on ‘Highlight’.
Figure 1.2. Highlight the trajectory
3. Naming
If you are satisfied with your trajectory enter a valid trajectory name and - if you want - a comment.
4. Saving
Click on ‘Save’ and on ‘Close application’ in order to return the updated spata-object.
Trajectories are stored inside the object itself. Therefore createTrajectories() returns an updated version of the object that was specified as the function’s input now containing the additional trajectories.
To obtain all trajectory names that have been drawn and saved in your spata-object use getTrajectoryNames(). Depending on the number of samples specified it returns a character vector or a list of such.
get trajectory names
getTrajectoryNames(object = spata_obj)
## [1] "tumor-layers"
Visualize trajectories
In order to visualize specific trajectories make use of plotTrajectory().
plotTrajectory(object = spata_obj,
trajectory_name = "tumor-layers",
color_to = "seurat_clusters",
pt_size = 1.7,
pt_clrp = "npg",
display_image = FALSE) +
theme(legend.position = "top") +
plotTrajectory(object = spata_obj,
trajectory_name = "tumor-layers",
color_to = "pseudotime",
pt_size = 1.7,
smooth = TRUE,
smooth_span = 0.0125,
display_image = FALSE) +
theme(legend.position = "top")
Figure 2.1. Surface related visualization of the trajectory
Visualize trajectory trends
Trajectory trends via lineplots
To display the trend of certain variables along a trajectory via lineplots use one of the following three functions:
A call to one of those three functions will return a smoothed lineplot where the direction of the trajectory is mapped onto the x-axis and the values of the chosen aspects are mapped onto the y-axis. All functions follow the same concept and share the majority of their arguments. Still, they feature slight differences given the nature of their aspects to display.
The following three arguments have to be set in order to specify the trajectory that is to be analyzed:
- object
- trajectory_name
- of_sample (if more than one sample exists in the object, else it can be neglected)
The way values are smoothed and displayed can be manipulated via:
- smooth_method
- smooth_span
- smooth_se
Features along trajectories
Specify the features of interest via the argument features. Note that the function will compute the mean of all points on the axis orthogonal to the trajectory’s direction. As only numeric variables allow for such computation displaying the trend of categorical variables (e.g. cluster) is not possible via lineplots.
plotTrajectoryFeatures(object = spata_obj,
trajectory_name = "tumor-layers",
features = "pseudotime",
smooth_method = "loess",
smooth_span = 0.2,
smooth_se = TRUE)</pre>
Figure 3.1. Visualization of ontinuous features along a trajectory
In order to plot discrete features use `plotTrajectoryFeaturesDiscrete().
plotTrajectoryFeaturesDiscrete(object = spata_obj,
trajectory_name = "tumor-layers",
discrete_feature = "seurat_clusters",
clrp = "npg",
display_trajectory_parts = FALSE)
Figure 3.2. Visualization of discrete features along a trajectory
Genes and gene-sets along trajectories
Specify the genes or gene-sets of interest by using the respective functions and arguments.
# gene-set names
genes_of_interest <- [c](https://rdrr.io/r/base/c.html)("CALM1", "VIM", "GFAP")
# plot lineplot
plotTrajectoryGenes(object = spata_obj,
trajectory_name = "tumor-layers",
genes = genes_of_interest,
smooth_span = 0.2,
smooth_se = TRUE)
Figure 3.3. Visualization of gene expression along a trajectory
plotTrajectoryGeneSets(
object = spata_obj,
trajectory_name = "tumor-layers",
gene_sets = "HM_HYPOXIA",
display_trajectory_parts = FALSE) + # results in missing vertical lines
ggplot2::theme(legend.position = "top")
Analyze & model trajectories
To find variables that follow a certain trend along the trajectory of interest use assessTrajectoryTrends(). It fits the trajectory-trends of all specified variables to a variety of patterns and assesses their fit by calculating the area under the curve of the residuals.
all_genes <- getGenes(spata_obj)
all_gene_sets <- getGeneSets(spata_obj)
# obtain an assessed trajectory data.frame for all genes
atdf_gs <- assessTrajectoryTrends(object = spata_obj,
trajectory_name = "tumor-layers",
variables = all_genes)
# obtain an assessed trajectory data.frame for all gene-sets
atdf_gene_sets <- assessTrajectoryTrends(object = spata_obj,
trajectory_name = "tumor-layers",
variables = all_gene-sets)
# output example
atdf_gs
The resulting data.frame contains the evaluation of all fits for all provided variables. plotTrajectoryFit() visualizes the fitting results for a particular variable.
# compare the trend of a variable to different models
plotTrajectoryFit(object = spata_obj,
trajectory_name = "tumor-layers",
variable = "GFAP",
display_residuals = TRUE) +
ggplot2::theme(legend.position = "top")
Figure 4.1. Visulization of the fitting results
To get a summary of your trajectory assessment use examineTrajectoryAssessment() which displays the distribution of all areas under the curve measured. Use filterTrend() or a combination of dplyr::filter() and dplyr::pull() on your assessed trajectory data.frame in order to extract the variables following a specific trend to a certain quality-degree.
# example 1: extract all variables that follow the linear descending trend while moving towards the hypoxic area (See Figure 2.1)
# with an auc-evaluation equal to or lower than 2
descending_gene_sets <-
filterTrends(atdf = atdf_gene_sets,
limit = 2,
trends = [c](https://rdrr.io/r/base/c.html)("Linear descending"),
variables_only = FALSE) # return a data.frame
descending_gene_sets
# example 2: extract variables that featured a peak along the trajectory
# with auc-evaluation equal to or lower than 4
peaking_genes <- filterTrends(atdf = atdf_gs,
limit = 4,
trends = c("Early peak", "Late peak", "One peak"),
variables_only = TRUE) # return a vector of variables
head(peaking_genes)
## [1] "CKB" "METRN" "TMSB4X" "LPL" "CALM1" "ITGA3"
Trajectory trends via heatmaps
Displaying the expression trends of too many variables in a lineplot gets unclear quite rapidly. In order to deal with several variables use plotTrajectoryHeatmap(). While the trajectory’s directory is still displayed from left to right the dynamic of the gene-sets’ expression values is now displayed by the gradual change of a row’s color instead of a line’s slope in a lineplot. The advantage of using heatmaps to display dynamic expression changes along trajectories becomes obvious when the number of variables to display rises.
descending_gene_sets <- dplyr::pull(descending_gene_sets, variables)
plotTrajectoryHeatmap(object = spata_obj,
trajectory_name = "tumor-layers",
variables = descending_gene_sets,
arrange_rows = "maxima",
show_row_names = FALSE, # gene set names are long...
split_columns = TRUE,
smooth_span = 0.5)
Figure 4.2) A heatmap displaying linear descending trends
plotTrajectoryHeatmap(object = spata_obj,
trajectory_name = "tumor-layers",
variables = peaking_genes,
arrange_rows = "maxima",
show_row_names = FALSE, # gene set names are long...
split_columns = TRUE, # splits the heatmap
smooth_span = 0.5)
Figure 4.3. A heatmap displaying the gradual change of peaking genes
這個方法光看示例好像沒什么優(yōu)勢，需要在實際中檢驗一下了，感覺整體效果不如stlearn
生活很好，有你更好
