空間轉(zhuǎn)錄組數(shù)據(jù)分析軟件推薦---SpaCET

作者，Evil Genius

這一篇給大家推薦一個空轉(zhuǎn)分析的優(yōu)秀軟件---SpaCET，文章在Estimation of cell lineages in tumors from spatial transcriptomics data，2023年1月發(fā)表于NC，個人感覺軟件集中了空轉(zhuǎn)分析的重要功能，包括細(xì)胞互作和共定位，軟件在SpaCET。

SpaCET是一個R包，用于分析癌癥空間轉(zhuǎn)錄組學(xué)(ST)數(shù)據(jù)集，以估計腫瘤微環(huán)境中的細(xì)胞譜系和細(xì)胞間相互作用。簡單地說，SpaCET首先通過整合常見惡性腫瘤的基因模式來估計癌細(xì)胞的豐度。SpaCET隨后使用約束回歸模型校準(zhǔn)局部組織密度，并確定基質(zhì)細(xì)胞和免疫細(xì)胞譜系分?jǐn)?shù)。此外，SpaCET可以揭示腫瘤微環(huán)境中假定的細(xì)胞-細(xì)胞相互作用。此外，SpaCET可以整合匹配的scRNA-seq數(shù)據(jù)集作為自定義參考來進(jìn)行細(xì)胞類型反卷積。

安裝
# install.packages("devtools")
devtools::install_github("data2intelligence/SpaCET")

示例一、依據(jù)內(nèi)置數(shù)據(jù)集分析空間數(shù)據(jù)

創(chuàng)建對象（10X數(shù)據(jù)）
library(SpaCET)

# set the path to the in-house breast cancer ST data. User can set the paths to their own data.
visiumPath <- file.path(system.file(package = "SpaCET"), "extdata/Visium_BC")

# load ST data to create an SpaCET object.
SpaCET_obj <- create.SpaCET.object.10X(visiumPath = visiumPath)

# show this object.
str(SpaCET_obj)
# show this object.
SpaCET_obj@input$counts[1:8,1:6]
## 8 x 6 sparse Matrix of class "dgCMatrix"
##             50x102 59x19 14x94 47x13 73x43 61x97
## MIR1302-2HG      .     .     .     .     .     .
## FAM138A          .     .     .     .     .     .
## OR4F5            .     .     .     .     .     .
## AL627309.1       .     .     .     .     .     1
## AL627309.3       .     .     .     .     .     .
## AL627309.2       .     .     .     .     .     .
## AL627309.4       .     .     .     .     .     .
## AL732372.1       .     .     .     .     .     .
展示基礎(chǔ)信息
# calculate the QC metrics
SpaCET_obj <- SpaCET.quality.control(SpaCET_obj)

# plot the QC metrics
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "QualityControl", 
  spatialFeatures=c("UMI","Gene")
)
圖片.png
Deconvolve ST data

通過兩個階段，SpaCET.deconvolution將所有ST spot的混合物分解為惡性細(xì)胞、免疫細(xì)胞和基質(zhì)細(xì)胞。SpaCET首先通過拷貝數(shù)改變(CNA)的基因模式和常見惡性腫瘤的表達(dá)變化來估計惡性細(xì)胞的比例。隨后，基于來自不同癌癥類型的單細(xì)胞RNA-seq數(shù)據(jù)集的內(nèi)部分層細(xì)胞譜系，使用約束回歸模型來確定免疫和基質(zhì)細(xì)胞分?jǐn)?shù)，并包括一個無法識別的組件來校準(zhǔn)跨組織區(qū)域的細(xì)胞密度變化。

具體來說，需要使用cancerType參數(shù)指定該腫瘤ST數(shù)據(jù)集中的癌癥類型，利用該參數(shù)選擇癌癥類型特異性的CNA或表達(dá)特征來推斷惡性細(xì)胞分?jǐn)?shù)。癌癥類型的代碼可以在下拉列表中找到。當(dāng)將ST點的表達(dá)譜與癌癥類型特異性標(biāo)記相關(guān)聯(lián)時，將癌癥類型特異性CNA標(biāo)記作為第一選項，因為染色體不穩(wěn)定性被廣泛認(rèn)為是人類腫瘤的一個一致特征?；蛘撸绻麤]有點與CNA特征強(qiáng)相關(guān)，癌癥類型特異性表達(dá)特征將被激活。這種情況可能是由于低CNA的染色體穩(wěn)定的癌細(xì)胞。對于數(shù)據(jù)集中未包含的癌癥類型，通過平均所有癌癥類型特異性表達(dá)特征來創(chuàng)建泛癌癥表達(dá)特征。

How many caner types are in the gene pattern dictionary?
# deconvolve ST data
SpaCET_obj <- SpaCET.deconvolution(SpaCET_obj, cancerType="BRCA", coreNo=8)

# show the ST deconvolution results
SpaCET_obj@results$deconvolution$propMat[1:13,1:6]

##                   50x102 59x19        14x94        47x13        73x43
## Malignant   2.860636e-01     1 6.845966e-02 3.899756e-01 9.608802e-01
## CAF         3.118545e-01     0 3.397067e-01 1.111980e-01 3.372692e-02
## Endothelial 5.510895e-02     0 1.427060e-01 3.080531e-02 5.263544e-03
## Plasma      2.213392e-02     0 1.507382e-02 1.183170e-02 9.071809e-06
## B cell      3.885793e-03     0 9.271616e-02 1.406470e-01 1.329085e-06
## T CD4       1.344389e-01     0 1.554305e-02 1.249414e-01 1.112392e-05
## T CD8       7.578696e-03     0 2.514558e-07 1.379856e-03 1.123043e-06
## NK          7.104005e-04     0 1.670019e-06 4.890387e-08 3.562557e-07
## cDC         1.421632e-07     0 8.278023e-02 7.584295e-02 2.851146e-07
## pDC         1.606443e-06     0 2.283754e-02 1.805671e-02 3.878344e-07
## Macrophage  1.703304e-01     0 5.021248e-02 9.531511e-02 9.253645e-07
## Mast        7.905067e-08     0 1.621498e-05 1.333430e-07 1.162099e-07
## Neutrophil  1.380073e-05     0 9.528996e-07 1.167503e-08 9.908635e-05
可視化
# show the spatial distribution of malignant cells and macrophages.
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "CellFraction", 
  spatialFeatures=c("Malignant","Macrophage")
)

# show the spatial distribution of all cell types.
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "CellFraction", 
  spatialFeatures="All", 
  pointSize = 0.1, 
  nrow=5
)
圖片.png
細(xì)胞互作
識別細(xì)胞共定位

SpaCET provides SpaCET.CCI.colocalization and SpaCET.visualize.colocalization to compute and visualize the co-localized cell-type pairs.

# calculate the cell-cell colocalization.
SpaCET_obj <- SpaCET.CCI.colocalization(SpaCET_obj)

# visualize the cell-cell colocalization.
SpaCET.visualize.colocalization(SpaCET_obj)
Analyze the L-R network enrichment within ST spots

細(xì)胞共定位并不直接表明物理相互作用。因此，我們通過分析ST點內(nèi)配體-受體(L-R)的相互作用來尋求細(xì)胞-細(xì)胞相互作用的進(jìn)一步證據(jù)。

# calculate the L-R network score across ST spots.
SpaCET_obj <- SpaCET.CCI.LRNetworkScore(SpaCET_obj,coreNo=8)

# visualize the L-R network score.
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "LRNetworkScore", 
  spatialFeatures=c("Network_Score","Network_Score_pv")
)

每個ST點的L-R網(wǎng)絡(luò)評分表明了每個位置配體-受體相互作用的總體強(qiáng)度，而不是兩種細(xì)胞類型之間的具體相互作用。因此，SpaCET進(jìn)一步對每個細(xì)胞類型對進(jìn)行了L-R網(wǎng)絡(luò)評分的富集分析。例如，對于CAF和M2細(xì)胞在乳腺腫瘤組織中的共定位，SpaCET將所有ST點分為四類:CAF-M2共定位，CAF或M2占主導(dǎo)。分析發(fā)現(xiàn)，CAF- m2共定位點比CAF/ m2主導(dǎo)點具有更實質(zhì)性的L-R交互網(wǎng)絡(luò)評分。


Enrich cell-cell interactions at the tumor-immune interface
# Identify the Tumor-Stroma Interface
SpaCET_obj <- SpaCET.identify.interface(SpaCET_obj)

# Visualize the Interface
SpaCET.visualize.spatialFeature(SpaCET_obj, spatialType = "Interface")

# Compute the distance of CAF-M2 to tumor border
SpaCET.distance.to.interface(SpaCET_obj, cellTypePair=c("CAF", "Macrophage M2"))
Explore cancer cell states

腫瘤細(xì)胞的基因表達(dá)譜(即癌細(xì)胞狀態(tài))由腫瘤細(xì)胞的遺傳背景和周圍環(huán)境的細(xì)胞-細(xì)胞相互作用共同決定。作為探索性擴(kuò)展，SpaCET可以自動探索不同癌細(xì)胞狀態(tài)的空間分布。

# further deconvolve malignant cell states
SpaCET_obj <- SpaCET.deconvolution.malignant(SpaCET_obj, malignantCutoff = 0.7, coreNo = 8)

# show cancer cell state fraction of the first five spots
SpaCET_obj@results$deconvolution$propMat[c("Malignant cell state A","Malignant cell state B"),1:6]

##                           50x102        59x19        14x94     47x13        73x43     61x97
## Malignant cell state A 0.2295498 9.999900e-01 6.845962e-02 0.2038680 9.608802e-01 0.6517794
## Malignant cell state B 0.0565137 1.239573e-11 3.921715e-08 0.1861075 2.340661e-09 0.2675332

SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "CellFraction", 
  spatialFeatures=c("Malignant","Malignant cell state A","Malignant cell state B"), 
  nrow=1
)

示例二、依據(jù)匹配的單細(xì)胞數(shù)據(jù)集分析空間數(shù)據(jù)

library(SpaCET)

PDAC_Path <- system.file("extdata", 'oldST_PDAC', package = 'SpaCET')
load(paste0(PDAC_Path,"/st_PDAC.rda"))

# show count matrix
counts[1:6,1:5]

##        10x10 10x13 10x14 10x15 10x16
## A1CF       0     0     0     0     0
## A2M       13     0     4     0     0
## A4GALT     1     0     0     0     0
## A4GNT      0     0     1     0     0
## AAAS       0     0     0     0     0
## AACS       0     0     0     0     0

# show coordinate matrix
spotCoordinates[1:5,]

##        X  Y
## 10x10 10 10
## 10x13 10 13
## 10x14 10 14
## 10x15 10 15
## 10x16 10 16

# load ST data to create an SpaCET object.
SpaCET_obj <- create.SpaCET.object(
  counts=counts,
  spotCoordinates=spotCoordinates,
  imagePath=NA,
  platform = "oldST"
)

# show this object.
str(SpaCET_obj)
解卷積
# load sc data
PDAC_Path <- system.file("extdata", 'oldST_PDAC', package = 'SpaCET')
load(paste0(PDAC_Path,"/sc_PDAC.rda"))

# show count matrix
sc_counts[1:6,1:5]

##         c1 c2 c3 c4 c5
## A1BG     0  0  0  0  0
## A1CF     0  0  0  1  0
## A2M      0  0  0  0  0
## A2ML1    0  0  0  0  0
## A3GALT2  0  0  0  0  0
## A4GALT   0  0  0  0  0

# show cell annotation matrix
sc_annotation[1:6,]

##    cellID bio_celltype                            
## c1 "c1"   "Acinar cells"                          
## c2 "c2"   "Ductal - terminal ductal like"         
## c3 "c3"   "Ductal - terminal ductal like"         
## c4 "c4"   "Ductal - CRISP3 high/centroacinar like"
## c5 "c5"   "Cancer clone A"                        
## c6 "c6"   "Cancer clone A" 

# show cell type lineage tree
head(sc_lineageTree)

## $Cancer
## [1] "Cancer clone A" "Cancer clone B"
## 
## $Ductal
## [1] "Ductal - APOL1 high/hypoxic"            "Ductal - CRISP3 high/centroacinar like"
## [3] "Ductal - MHC Class II"                  "Ductal - terminal ductal like"         
## 
## $Macrophage
## [1] "Macrophages A" "Macrophages B"
## 
## $mDC
## [1] "mDCs A" "mDCs B"
## 
## $`Acinar cells`
## [1] "Acinar cells"
## 
## $`Endocrine cells`
## [1] "Endocrine cells"

SpaCET_obj <- SpaCET.deconvolution.matched.scRNAseq(
  SpaCET_obj, 
  sc_counts=sc_counts, 
  sc_annotation=sc_annotation, 
  sc_lineageTree=sc_lineageTree, 
  coreNo=8
)

SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "CellFraction",
  spatialFeatures = c("Cancer clone A","Cancer clone B","Acinar cells","Ductal - CRISP3 high/centroacinar like"),
  nrow=2
)
基因空間表達(dá)可視化
# Markers for cancer clone A and B, acinar cell, and centroacinar like ductal cell
SpaCET.visualize.spatialFeature(
  SpaCET_obj,
  spatialType = "GeneExpression",
  spatialFeatures = c("TM4SF1","S100A4","PRSS1","CRISP3"),
  nrow=2
)
可以依據(jù)以上操作進(jìn)一步分析空間轉(zhuǎn)錄組的共定位及通訊分析。

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