今天是30號(hào)了，馬上過年了，不知道大家相親是否開心呢？？這一篇我們回顧一下10X空間轉(zhuǎn)錄組的分析軟件----Giotto，文章在Giotto, a toolbox for integrative analysis and visualization of spatial expression data，我們簡(jiǎn)單回顧一下軟件的原理和示例代碼
Overview of the Giotto toolbox
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Giotto 提供了一個(gè)全面的空間分析工具箱，其中包含兩個(gè)獨(dú)立但完全集成的模塊。 第一個(gè)模塊（Giotto Analyzer）提供有關(guān)分析空間單細(xì)胞表達(dá)數(shù)據(jù)的不同步驟的分析說明，而第二個(gè)模塊（Giotto Viewer）在用戶本地計(jì)算機(jī)上提供此類數(shù)據(jù)的響應(yīng)式和交互式查看器。 這兩個(gè)模塊可以獨(dú)立使用，也可以迭代使用。
Giotto Analyzer 需要一個(gè)基因的細(xì)胞計(jì)數(shù)矩陣和每個(gè)細(xì)胞質(zhì)心位置的空間坐標(biāo)作為最小輸入。 在基礎(chǔ)層面，Giotto Analyzer 可用于執(zhí)行通常類似于 scRNAseq 分析的常見步驟，例如預(yù)處理、特征選擇、降維和無監(jiān)督聚類； 另一方面，主要優(yōu)勢(shì)在于它整合基因表達(dá)和空間信息的能力，以便深入了解組織的結(jié)構(gòu)和功能組織及其表達(dá)模式。 Giotto Analyzer 創(chuàng)建了一個(gè)空間網(wǎng)格和鄰域網(wǎng)絡(luò)，將物理上彼此靠近的細(xì)胞連接起來。 這些對(duì)象作為執(zhí)行與細(xì)胞鄰域相關(guān)的分析的基礎(chǔ)。
Giotto Analyzer 是用流行的語言 R 編寫的，核心數(shù)據(jù)結(jié)構(gòu)是 giotto 對(duì)象，它是專為基于 R 中靈活的 S4 對(duì)象系統(tǒng)而設(shè)計(jì)的空間表達(dá)數(shù)據(jù)分析。 足以執(zhí)行所有計(jì)算和分析。 這允許用戶快速評(píng)估和創(chuàng)建自己的靈活管道，用于空間可視化和數(shù)據(jù)分析。 Giotto Viewer 模塊旨在以交互方式探索 Giotto Analyzer 的輸出并可視化其他信息，例如細(xì)胞形態(tài)和轉(zhuǎn)錄位置。總之，這兩個(gè)模塊為空間表達(dá)數(shù)據(jù)分析和可視化提供了一個(gè)集成工具箱。
示例代碼(小鼠腦)
library(Giotto)
# 1\. set working directory
#results_folder = '/path/to/directory/'
results_folder = '/Volumes/Ruben_Seagate/Dropbox (Personal)/Projects/GC_lab/Ruben_Dries/190225_spatial_package/Results/Visium/Brain/201226_results//'
# 2\. set giotto python path
# set python path to your preferred python version path
# set python path to NULL if you want to automatically install (only the 1st time) and use the giotto miniconda environment
python_path = NULL
if(is.null(python_path)) {
installGiottoEnvironment()
}
Dataset explanation（10X平臺(tái)數(shù)據(jù)為例）
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Part 1: Giotto global instructions and preparations
## create instructions
instrs = createGiottoInstructions(save_dir = results_folder,
save_plot = TRUE,
show_plot = FALSE)
## provide path to visium folder
#data_path = '/path/to/Brain_data/'
data_path = '/Volumes/Ruben_Seagate/Dropbox (Personal)/Projects/GC_lab/Ruben_Dries/190225_spatial_package/Data/Visium_data/Brain_data/'
part 2: Create Giotto object & process data
## directly from visium folder
visium_brain = createGiottoVisiumObject(visium_dir = data_path, expr_data = 'raw',
png_name = 'tissue_lowres_image.png',
gene_column_index = 2, instructions = instrs)
## update and align background image
# problem: image is not perfectly aligned
spatPlot(gobject = visium_brain, cell_color = 'in_tissue', show_image = T, point_alpha = 0.7,
save_param = list(save_name = '2_a_spatplot_image'))
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# check name
showGiottoImageNames(visium_brain) # "image" is the default name
# adjust parameters to align image (iterative approach)
visium_brain = updateGiottoImage(visium_brain, image_name = 'image',
xmax_adj = 1300, xmin_adj = 1200,
ymax_adj = 1100, ymin_adj = 1000)
# now it's aligned
spatPlot(gobject = visium_brain, cell_color = 'in_tissue', show_image = T, point_alpha = 0.7,
save_param = list(save_name = '2_b_spatplot_image_adjusted'))
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## check metadata
pDataDT(visium_brain)
## compare in tissue with provided jpg
spatPlot(gobject = visium_brain, cell_color = 'in_tissue', point_size = 2,
cell_color_code = c('0' = 'lightgrey', '1' = 'blue'),
save_param = list(save_name = '2_c_in_tissue'))
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## subset on spots that were covered by tissue
metadata = pDataDT(visium_brain)
in_tissue_barcodes = metadata[in_tissue == 1]$cell_ID
visium_brain = subsetGiotto(visium_brain, cell_ids = in_tissue_barcodes)
## filter
visium_brain <- filterGiotto(gobject = visium_brain,
expression_threshold = 1,
gene_det_in_min_cells = 50,
min_det_genes_per_cell = 1000,
expression_values = c('raw'),
verbose = T)
## normalize
visium_brain <- normalizeGiotto(gobject = visium_brain, scalefactor = 6000, verbose = T)
## add gene & cell statistics
visium_brain <- addStatistics(gobject = visium_brain)
## visualize
spatPlot2D(gobject = visium_brain, show_image = T, point_alpha = 0.7,
save_param = list(save_name = '2_d_spatial_locations'))
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spatPlot2D(gobject = visium_brain, show_image = T, point_alpha = 0.7,
cell_color = 'nr_genes', color_as_factor = F,
save_param = list(save_name = '2_e_nr_genes'))
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part 3: dimension reduction
## highly variable genes (HVG)
visium_brain <- calculateHVG(gobject = visium_brain,
save_param = list(save_name = '3_a_HVGplot'))
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## run PCA on expression values (default)
gene_metadata = fDataDT(visium_brain)
featgenes = gene_metadata[hvg == 'yes' & perc_cells > 3 & mean_expr_det > 0.4]$gene_ID
visium_brain <- runPCA(gobject = visium_brain,
genes_to_use = featgenes,
scale_unit = F, center = T,
method="factominer")
screePlot(visium_brain, ncp = 30, save_param = list(save_name = '3_b_screeplot'))
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plotPCA(gobject = visium_brain,
save_param = list(save_name = '3_c_PCA_reduction'))
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## run UMAP and tSNE on PCA space (default)
visium_brain <- runUMAP(visium_brain, dimensions_to_use = 1:10)
plotUMAP(gobject = visium_brain,
save_param = list(save_name = '3_d_UMAP_reduction'))
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visium_brain <- runtSNE(visium_brain, dimensions_to_use = 1:10)
plotTSNE(gobject = visium_brain,
save_param = list(save_name = '3_e_tSNE_reduction'))
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part 4: cluster
## sNN network (default)
visium_brain <- createNearestNetwork(gobject = visium_brain, dimensions_to_use = 1:10, k = 15)
## Leiden clustering
visium_brain <- doLeidenCluster(gobject = visium_brain, resolution = 0.4, n_iterations = 1000)
plotUMAP(gobject = visium_brain,
cell_color = 'leiden_clus', show_NN_network = T, point_size = 2.5,
save_param = list(save_name = '4_a_UMAP_leiden'))
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part 5: co-visualize
# expression and spatial
spatDimPlot(gobject = visium_brain, cell_color = 'leiden_clus',
dim_point_size = 2, spat_point_size = 2.5,
save_param = list(save_name = '5_a_covis_leiden'))
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spatDimPlot(gobject = visium_brain, cell_color = 'nr_genes', color_as_factor = F,
dim_point_size = 2, spat_point_size = 2.5,
save_param = list(save_name = '5_b_nr_genes'))
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DG_subset = subsetGiottoLocs(visium_brain,
x_max = 6500, x_min = 3000,
y_max = -2500, y_min = -5500,
return_gobject = TRUE)
spatDimPlot(gobject = DG_subset,
cell_color = 'leiden_clus', spat_point_size = 5,
save_param = list(save_name = '5_c_DEG_subset'))
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part 6: cell type marker gene detection
gini_markers_subclusters = findMarkers_one_vs_all(gobject = visium_brain,
method = 'gini',
expression_values = 'normalized',
cluster_column = 'leiden_clus',
min_genes = 20,
min_expr_gini_score = 0.5,
min_det_gini_score = 0.5)
topgenes_gini = gini_markers_subclusters[, head(.SD, 2), by = 'cluster']$genes
# violinplot
violinPlot(visium_brain, genes = unique(topgenes_gini), cluster_column = 'leiden_clus',
strip_text = 8, strip_position = 'right',
save_param = list(save_name = '6_a_violinplot_gini', base_width = 5, base_height = 10))
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# cluster heatmap
plotMetaDataHeatmap(visium_brain, selected_genes = topgenes_gini,
metadata_cols = c('leiden_clus'),
x_text_size = 10, y_text_size = 10,
save_param = list(save_name = '6_b_metaheatmap_gini'))
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scran_markers_subclusters = findMarkers_one_vs_all(gobject = visium_brain,
method = 'scran',
expression_values = 'normalized',
cluster_column = 'leiden_clus')
topgenes_scran = scran_markers_subclusters[, head(.SD, 2), by = 'cluster']$genes
# violinplot
violinPlot(visium_brain, genes = unique(topgenes_scran), cluster_column = 'leiden_clus',
strip_text = 10, strip_position = 'right',
save_param = list(save_name = '6_d_violinplot_scran', base_width = 5))
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# cluster heatmap
plotMetaDataHeatmap(visium_brain, selected_genes = topgenes_scran,
metadata_cols = c('leiden_clus'),
save_param = list(save_name = '6_e_metaheatmap_scran'))
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part 7: cell-type annotation
# 1.1 create binary matrix of cell signature genes
# small example #
gran_markers = c("Nr3c2", "Gabra5", "Tubgcp2", "Ahcyl2",
"Islr2", "Rasl10a", "Tmem114", "Bhlhe22",
"Ntf3", "C1ql2")
oligo_markers = c("Efhd1", "H2-Ab1", "Enpp6", "Ninj2",
"Bmp4", "Tnr", "Hapln2", "Neu4",
"Wfdc18", "Ccp110")
di_mesench_markers = c("Cartpt", "Scn1a", "Lypd6b", "Drd5",
"Gpr88", "Plcxd2", "Cpne7", "Pou4f1",
"Ctxn2", "Wnt4")
signature_matrix = makeSignMatrixPAGE(sign_names = c('Granule_neurons',
'Oligo_dendrocytes',
'di_mesenchephalon'),
sign_list = list(gran_markers,
oligo_markers,
di_mesench_markers))
# 1.2 [shortcut] fully pre-prepared matrix for all cell types
sign_matrix_path = system.file("extdata", "sig_matrix.txt", package = 'Giotto')
brain_sc_markers = data.table::fread(sign_matrix_path)
sig_matrix = as.matrix(brain_sc_markers[,-1]); rownames(sig_matrix) = brain_sc_markers$Event
# 1.3 enrichment test with PAGE
# runSpatialEnrich() can also be used as a wrapper for all currently provided enrichment options
visium_brain = runPAGEEnrich(gobject = visium_brain, sign_matrix = sig_matrix)
# 1.4 heatmap of enrichment versus annotation (e.g. clustering result)
cell_types = colnames(sig_matrix)
plotMetaDataCellsHeatmap(gobject = visium_brain,
metadata_cols = 'leiden_clus',
value_cols = cell_types,
spat_enr_names = 'PAGE',
x_text_size = 8,
y_text_size = 8,
save_param = list(save_name="7_a_metaheatmap"))
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# 1.5 visualizations
cell_types_subset = colnames(sig_matrix)[1:10]
spatCellPlot(gobject = visium_brain,
spat_enr_names = 'PAGE',
cell_annotation_values = cell_types_subset,
cow_n_col = 4,coord_fix_ratio = NULL, point_size = 0.75,
save_param = list(save_name="7_b_spatcellplot_1"))
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cell_types_subset = colnames(sig_matrix)[11:20]
spatCellPlot(gobject = visium_brain, spat_enr_names = 'PAGE',
cell_annotation_values = cell_types_subset, cow_n_col = 4,
coord_fix_ratio = NULL, point_size = 0.75,
save_param = list(save_name="7_c_spatcellplot_2"))
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spatDimCellPlot(gobject = visium_brain,
spat_enr_names = 'PAGE',
cell_annotation_values = c('Cortex_hippocampus', 'Granule_neurons',
'di_mesencephalon_1', 'Oligo_dendrocyte','Vascular'),
cow_n_col = 1, spat_point_size = 1,
plot_alignment = 'horizontal',
save_param = list(save_name="7_d_spatDimCellPlot", base_width=7, base_height=10))
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part 8: spatial grid
visium_brain <- createSpatialGrid(gobject = visium_brain,
sdimx_stepsize = 400,
sdimy_stepsize = 400,
minimum_padding = 0)
spatPlot(visium_brain, cell_color = 'leiden_clus', show_grid = T,
grid_color = 'red', spatial_grid_name = 'spatial_grid',
save_param = list(save_name = '8_grid'))
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part 9: spatial network
visium_brain <- createSpatialNetwork(gobject = visium_brain,
method = 'kNN', k = 5,
maximum_distance_knn = 400,
name = 'spatial_network')
showNetworks(visium_brain)
spatPlot(gobject = visium_brain, show_network = T,
network_color = 'blue', spatial_network_name = 'spatial_network',
save_param = list(save_name = '9_a_knn_network'))
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part 10: spatial genes
## kmeans binarization
kmtest = binSpect(visium_brain, calc_hub = T, hub_min_int = 5,
spatial_network_name = 'spatial_network')
spatGenePlot(visium_brain, expression_values = 'scaled',
genes = kmtest$genes[1:6], cow_n_col = 2, point_size = 1.5,
save_param = list(save_name = '10_a_spatial_genes_km'))
tupua
## rank binarization
ranktest = binSpect(visium_brain, bin_method = 'rank',
calc_hub = T, hub_min_int = 5,
spatial_network_name = 'spatial_network')
spatGenePlot(visium_brain, expression_values = 'scaled',
genes = ranktest$genes[1:6], cow_n_col = 2, point_size = 1.5,
save_param = list(save_name = '10_b_spatial_genes_rank'))
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Spatial patterns
# cluster the top 1500 spatial genes into 20 clusters
ext_spatial_genes = ranktest[1:1500,]$gene
# here we use existing detectSpatialCorGenes function to calculate pairwise distances between genes (but set network_smoothing=0 to use default clustering)
spat_cor_netw_DT = detectSpatialCorGenes(visium_brain,
method = 'network',
spatial_network_name = 'spatial_network',
subset_genes = ext_spatial_genes)
# cluster spatial genes
spat_cor_netw_DT = clusterSpatialCorGenes(spat_cor_netw_DT, name = 'spat_netw_clus', k = 20)
# visualize clusters
heatmSpatialCorGenes(visium_brain,
spatCorObject = spat_cor_netw_DT,
use_clus_name = 'spat_netw_clus',
heatmap_legend_param = list(title = NULL),
save_param = list(save_name="10_c_heatmap",
base_height = 6, base_width = 8, units = 'cm'))
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table(spat_cor_netw_DT$cor_clusters$spat_netw_clus)
coexpr_dt = data.table::data.table(genes = names(spat_cor_netw_DT$cor_clusters$spat_netw_clus),
cluster = spat_cor_netw_DT$cor_clusters$spat_netw_clus)
data.table::setorder(coexpr_dt, cluster)
top30_coexpr_dt = coexpr_dt[, head(.SD, 30) , by = cluster]
# do HMRF with different betas on 500 spatial genes
my_spatial_genes <- top30_coexpr_dt$genes
hmrf_folder = paste0(results_folder,'/','11_HMRF/')
if(!file.exists(hmrf_folder)) dir.create(hmrf_folder, recursive = T)
HMRF_spatial_genes = doHMRF(gobject = visium_brain,
expression_values = 'scaled',
spatial_genes = my_spatial_genes, k = 20,
spatial_network_name="spatial_network",
betas = c(0, 10, 5),
output_folder = paste0(hmrf_folder, '/', 'Spatial_genes/SG_topgenes_k20_scaled'))
visium_brain = addHMRF(gobject = visium_brain, HMRFoutput = HMRF_spatial_genes,
k = 20, betas_to_add = c(0, 10, 20, 30, 40),
hmrf_name = 'HMRF')
spatPlot(gobject = visium_brain, cell_color = 'HMRF_k20_b.40',
point_size = 2, save_param=c(save_name="10_d_spatPlot2D_HMRF"))
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Export and create Giotto Viewer
# check which annotations are available
combineMetadata(visium_brain, spat_enr_names = 'PAGE')
# select annotations, reductions and expression values to view in Giotto Viewer
viewer_folder = paste0(results_folder, '/', 'mouse_Visium_brain_viewer')
exportGiottoViewer(gobject = visium_brain,
output_directory = viewer_folder,
spat_enr_names = 'PAGE',
factor_annotations = c('in_tissue',
'leiden_clus',
'HMRF_k20_b.40'),
numeric_annotations = c('nr_genes',
'clus_25'),
dim_reductions = c('tsne', 'umap'),
dim_reduction_names = c('tsne', 'umap'),
expression_values = 'scaled',
expression_rounding = 2,
overwrite_dir = T)
生活很好，有你更好，我們2022年再見
