11 Trajectory Inference and RNA Velocity
Static clustering tells us which cell states are present, but many biological systems are dynamic. Development, activation, differentiation, and response to perturbation all involve cells moving through transitional states rather than staying in discrete categories.
This chapter therefore shifts from classification to dynamics. We begin with trajectory inference based on transcriptional similarity, then move to RNA velocity and related methods that add directional information to the analysis. Together, these approaches help answer not only what a cell is, but also where it may be going next.
11.1 Slingshot: cell lineage and pseudotime inference
panc <- readRDS("data/pancreas_sub_sce.rds")
panc <- RunSlingshot(sce = panc, group = "SubCellType", reduction = "UMAP")RunSlingshot() writes its result back into the unified state layer, so downstream helpers such as lineage_plot() and pseudo_plot() can consume the recorded trajectory object directly. If needed, you can also check has_trajectory(panc) or inspect get_trajectory(panc).
If you keep multiple trajectory runs in the same object, give them different name values and inspect them explicitly with get_trajectory(panc, id = "your_id").
11.1.1 Lineage plot
p1 <- lineage_plot(panc, group = "SubCellType") +
sc_dim_geom_label(
geom = shadowtext::geom_shadowtext,
color='black',
bg.color='white'
)
## displayed selected lineages
p2 <- lineage_plot(panc, group = "SubCellType", lineages = c("Lineage1", "Lineage2")) +
sc_dim_geom_label(
geom = shadowtext::geom_shadowtext,
color='black',
bg.color='white'
)
plot_list(`All Lineages` = p1, `Selected Lineages` = p2, ncol=1)
11.1.2 Pseudotime plot
pp1 <- pseudo_plot(panc, reduction = "UMAP", lineage = "Lineage1")
pp2 <- pseudo_plot(panc, reduction = "UMAP", lineage = "Lineage2")
pp_all <- pseudo_plot(panc, reduction = "UMAP")
plot_list(pp1, pp2, pp_all, design="AABB\nCCCC")
11.1.3 Expression trends in different cell trajectories
curve_genes <- c("Mrpl15", "Tram1", "Ncoa2")
## 默认选取smooth方法直接拟合得到的基因表达值结果来画图
p_curve <- genecurve_plot(panc, features = curve_genes, method = "smooth")
## 也可以用GAM计算预测的基因表达值来画图
p_curve1 <- genecurve_plot(panc, features = curve_genes, method = "gam")
plot_list(p_curve, p_curve1, ncol=1)
11.1.4 Heatmap
heatmap_genes <- head(unique(rownames(panc)), 10)
## 默认按照拟时间排序
hmp1 <- pseudo_heatmap(panc, features = heatmap_genes, lineage = "Lineage2")
## 可以手动对基因或细胞设置聚类(cluster = TRUE)，此时会打乱pseudotime的排序
hmp2 <- pseudo_heatmap(panc, features = heatmap_genes, lineage = "Lineage2", cluster_columns = TRUE)
## 对基因聚类
hmp3 <- pseudo_heatmap(panc, features = heatmap_genes, lineage = "Lineage2", cluster_rows = TRUE, sort = FALSE)
## 默认排序
hmp4 <- pseudo_heatmap(panc, features = heatmap_genes, lineage = "Lineage2", cluster_rows = FALSE, sort = TRUE)
## 组合起来看就能看到两个参数的设定对行顺序的影响
plot_list(hmp1, hmp2, hmp3, hmp4, ncol=1)
11.2 RNA velocity
The velocity and CellRank examples in this section are executed during book rendering when the required Python environment can be provisioned through basilisk. This means the documentation now also exercises the cross-language trajectory stack in CI instead of only describing it conceptually.
RNA metabolic process:
- RNA is synthesized at a rate of alpha
- RNA is spliced to remove introns and form mature mRNA at rate of beta
- Mature mRNA is degraded after functioning at rate of gamma
The disruption of the equilibrium between unspliced and spliced RNA indicates whether a gene is in an induced or repressed state.
With RNA-seq, especially the Poly-A enrichment method, the propotion of unspliced RNA obtained is relatively small. Currently, there are also some techinques capable of enriching nascent RNA in single cells, such as scSLAM-seq and scNT-seq.
Although some researchers are skeptical about RNA velocity, it has received significant attention because it can automatically detect the direction of trajectories.
scVelo supports a full dynamical model and various of utility functions (Bergen et al. 2020). It only supports Python and can be run in R using velociraptor.
11.2.1 Example data
This example is adopted from https://www.bioconductor.org/packages/devel/bioc/vignettes/velociraptor/inst/doc/velociraptor.html.
## class: SingleCellExperiment
## dim: 54448 2325
## metadata(0):
## assays(2): spliced unspliced
## rownames(54448): ENSMUSG00000102693.1
## ENSMUSG00000064842.1 ... ENSMUSG00000064369.1
## ENSMUSG00000064372.1
## rowData names(0):
## colnames(2325): CCCATACTCCGAAGAG AATCCAGTCATCTGCC ...
## ATCCACCCACCACCAG ATTGGTGGTTACCGAT
## colData names(1): celltype## reducedDimNames(0):
## mainExpName: NULL## altExpNames(0):library(sclet)
sce <- NormalizeData(sce, assay = "spliced")
sce <- FindVariableFeatures(sce, method="scran")
hvgs <- VariableFeatures(sce, method="scran")
sce <- RunPCA(sce, subset_row = hvgs, layer = "logcounts")
sce <- FindNeighbors(sce, dims = 1:20)
sce <- FindClusters(sce)
sce$cluster <- as.character(SingleCellExperiment::colLabels(sce))
sce <- RunUMAP(sce, dims = 1:20)11.2.2 Run velocity analysis
sclet wraps the RNA velocity inference via RunVelocity(). Under the hood, it delegates the heavy lifting to scVelo (managed safely via the basilisk/velociraptor Python sandbox) and smoothly registers the outputs to the velocity analysis state. This eliminates the need for manual environment switching.
# Run Velocity (Requires 'spliced' and 'unspliced' assays to be present)
sce <- RunVelocity(sce, subset_row = hvgs, assay.X = "spliced", use.dimred = "PCA")## Using Python: /home/runner/.pyenv/versions/3.12.10/bin/python3.12
## Creating virtual environment '/home/runner/.cache/R/basilisk/1.24.0/velociraptor/1.22.0/env' ...## Done!
## Installing packages: pip, wheel, setuptools## Installing packages: 'anndata==0.11.4', 'array-api-compat==1.12.0', 'click==8.2.1', 'contourpy==1.3.2', 'cycler==0.12.1', 'fonttools==4.58.5', 'h5py==3.14.0', 'joblib==1.5.1', 'kiwisolver==1.4.8', 'legacy-api-wrap==1.4.1', 'llvmlite==0.44.0', 'loompy==3.0.8', 'matplotlib==3.10.3', 'natsort==8.4.0', 'networkx==3.5', 'numba==0.61.2', 'numpy==2.2.6', 'numpy-groupies==0.11.3', 'packaging==25.0', 'pandas==2.3.1', 'patsy==1.0.1', 'pillow==11.3.0', 'pip==25.1.1', 'pynndescent==0.5.13', 'pyparsing==3.2.3', 'python-dateutil==2.9.0.post0', 'pytz==2025.2', 'scanpy==1.11.3', 'scikit-learn==1.7.0', 'scipy==1.15.3', 'scvelo==0.3.3', 'seaborn==0.13.2', 'session-info2==0.1.2', 'setuptools==80.9.0', 'six==1.17.0', 'statsmodels==0.14.5', 'threadpoolctl==3.6.0', 'tqdm==4.67.1', 'typing_extensions==4.14.1', 'tzdata==2025.2', 'umap-learn==0.5.9.post2', 'wheel==0.45.1'## Virtual environment '/home/runner/.cache/R/basilisk/1.24.0/velociraptor/1.22.0/env' successfully created.## $mode
## [1] "deterministic"
##
## $use.dimred
## [1] "PCA"
##
## $subset.row
## [1] "ENSMUSG00000047995.7" "ENSMUSG00000048686.4"
## [3] "ENSMUSG00000064351.1" "ENSMUSG00000117819.1"
## [5] "ENSMUSG00000078554.2" "ENSMUSG00000038994.6"
## [7] "ENSMUSG00000027564.4" "ENSMUSG00000027505.2"
## [9] "ENSMUSG00000043050.8" "ENSMUSG00000060860.8"
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## [17] "ENSMUSG00000026182.6" "ENSMUSG00000008482.9"
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## [23] "ENSMUSG00000031991.10" "ENSMUSG00000081984.3"
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## [95] "ENSMUSG00000095384.1" "ENSMUSG00000047150.8"
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## [99] "ENSMUSG00000022148.16" "ENSMUSG00000050685.5"
## [101] "ENSMUSG00000095789.6" "ENSMUSG00000026226.16"
## [103] "ENSMUSG00000073731.12" "ENSMUSG00000055553.16"
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## [107] "ENSMUSG00000028938.9" "ENSMUSG00000046755.5"
## [109] "ENSMUSG00000035522.3" "ENSMUSG00000021838.17"
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## [113] "ENSMUSG00000031452.15" "ENSMUSG00000058935.14"
## [115] "ENSMUSG00000099863.1" "ENSMUSG00000020945.13"
## [117] "ENSMUSG00000030859.8" "ENSMUSG00000016559.14"
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## [121] "ENSMUSG00000066500.5" "ENSMUSG00000100862.1"
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## [169] "ENSMUSG00000095869.1" "ENSMUSG00000091396.2"
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## [173] "ENSMUSG00000005803.14" "ENSMUSG00000101894.1"
## [175] "ENSMUSG00000035085.5" "ENSMUSG00000100282.1"
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## [181] "ENSMUSG00000019876.15" "ENSMUSG00000041540.16"
## [183] "ENSMUSG00000101968.6" "ENSMUSG00000010435.7"
## [185] "ENSMUSG00000037307.11" "ENSMUSG00000042200.3"
## [187] "ENSMUSG00000055692.21" "ENSMUSG00000021552.7"
## [189] "ENSMUSG00000009549.14" "ENSMUSG00000043633.6"
## [191] "ENSMUSG00000097075.3" "ENSMUSG00000020234.10"
## [193] "ENSMUSG00000048655.17" "ENSMUSG00000047518.3"
## [195] "ENSMUSG00000049154.12" "ENSMUSG00000101959.1"
## [197] "ENSMUSG00000020270.9" "ENSMUSG00000050141.13"
## [199] "ENSMUSG00000050621.7" "ENSMUSG00000109864.1"
## [201] "ENSMUSG00000036924.3" "ENSMUSG00000002308.16"
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## [205] "ENSMUSG00000067702.5" "ENSMUSG00000048573.9"
## [207] "ENSMUSG00000071359.13" "ENSMUSG00000061633.3"
## [209] "ENSMUSG00000094445.7" "ENSMUSG00000052920.16"
## [211] "ENSMUSG00000001558.5" "ENSMUSG00000046173.3"
## [213] "ENSMUSG00000020622.17" "ENSMUSG00000064354.1"
## [215] "ENSMUSG00000026827.12" "ENSMUSG00000086361.1"
## [217] "ENSMUSG00000060491.14" "ENSMUSG00000027674.16"
## [219] "ENSMUSG00000090202.1" "ENSMUSG00000078127.3"
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## [341] "ENSMUSG00000095597.2" "ENSMUSG00000097522.1"
## [343] "ENSMUSG00000041068.6" "ENSMUSG00000040576.8"
## [345] "ENSMUSG00000039555.14" "ENSMUSG00000074734.3"
## [347] "ENSMUSG00000095533.7" "ENSMUSG00000029909.6"
## [349] "ENSMUSG00000002240.12" "ENSMUSG00000024223.3"
## [351] "ENSMUSG00000028523.14" "ENSMUSG00000029182.10"
## [353] "ENSMUSG00000020048.13" "ENSMUSG00000026466.16"
## [355] "ENSMUSG00000023873.13" "ENSMUSG00000045835.5"
## [357] "ENSMUSG00000015962.5" "ENSMUSG00000026940.3"
## [359] "ENSMUSG00000054909.11" "ENSMUSG00000085940.1"
## [361] "ENSMUSG00000028332.13" "ENSMUSG00000039742.15"
## [363] "ENSMUSG00000043036.13" "ENSMUSG00000070999.2"
## [365] "ENSMUSG00000003153.10" "ENSMUSG00000062075.13"
## [367] "ENSMUSG00000024510.6" "ENSMUSG00000030887.4"
## [369] "ENSMUSG00000039183.6" "ENSMUSG00000038165.8"
## [371] "ENSMUSG00000029766.7" "ENSMUSG00000095546.7"
## [373] "ENSMUSG00000025289.15" "ENSMUSG00000090273.3"
## [375] "ENSMUSG00000021771.14" "ENSMUSG00000056987.8"
## [377] "ENSMUSG00000059970.7" "ENSMUSG00000027708.14"
## [379] "ENSMUSG00000003518.13" "ENSMUSG00000042708.12"
## [381] "ENSMUSG00000063129.1" "ENSMUSG00000089989.10"
## [383] "ENSMUSG00000092074.2" "ENSMUSG00000097638.1"
## [385] "ENSMUSG00000029678.8" "ENSMUSG00000007591.15"
## [387] "ENSMUSG00000056665.2" "ENSMUSG00000086046.1"
## [389] "ENSMUSG00000030077.11" "ENSMUSG00000058173.12"
## [391] "ENSMUSG00000040919.13" "ENSMUSG00000021476.9"
## [393] "ENSMUSG00000037058.16" "ENSMUSG00000026473.16"
## [395] "ENSMUSG00000017195.15" "ENSMUSG00000050471.17"
## [397] "ENSMUSG00000021499.12" "ENSMUSG00000081406.4"
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## [1193] "ENSMUSG00000032405.10" "ENSMUSG00000082652.1"
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## [1557] "ENSMUSG00000118332.1" "ENSMUSG00000087574.1"
## [1559] "ENSMUSG00000049902.6" "ENSMUSG00000028328.13"
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## [1563] "ENSMUSG00000053868.4" "ENSMUSG00000055436.18"
## [1565] "ENSMUSG00000031270.1" "ENSMUSG00000113179.1"
## [1567] "ENSMUSG00000091422.8" "ENSMUSG00000037822.12"
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## [1581] "ENSMUSG00000022619.5" "ENSMUSG00000020268.14"
## [1583] "ENSMUSG00000028639.14" "ENSMUSG00000053719.10"
## [1585] "ENSMUSG00000026729.9" "ENSMUSG00000028630.9"
## [1587] "ENSMUSG00000091803.7" "ENSMUSG00000086841.1"
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## [1591] "ENSMUSG00000062773.6" "ENSMUSG00000059042.3"
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## [1595] "ENSMUSG00000066278.6" "ENSMUSG00000074063.10"
## [1597] "ENSMUSG00000114441.1" "ENSMUSG00000078451.5"
## [1599] "ENSMUSG00000027099.9" "ENSMUSG00000052584.10"
## [1601] "ENSMUSG00000109293.1" "ENSMUSG00000091243.2"
## [1603] "ENSMUSG00000063447.7" "ENSMUSG00000039067.5"
## [1605] "ENSMUSG00000026484.13" "ENSMUSG00000018415.14"
## [1607] "ENSMUSG00000078139.3" "ENSMUSG00000061928.6"
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## [1611] "ENSMUSG00000024661.7" "ENSMUSG00000043535.13"
## [1613] "ENSMUSG00000100559.1" "ENSMUSG00000107031.1"
## [1615] "ENSMUSG00000028059.16" "ENSMUSG00000025050.8"
## [1617] "ENSMUSG00000025413.13" "ENSMUSG00000030465.19"
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## [1651] "ENSMUSG00000060161.9" "ENSMUSG00000054091.9"
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## [1663] "ENSMUSG00000021610.8" "ENSMUSG00000031516.11"
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## [1725] "ENSMUSG00000099722.3" "ENSMUSG00000099630.1"
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## [1745] "ENSMUSG00000072188.4" "ENSMUSG00000022185.19"
## [1747] "ENSMUSG00000056537.11" "ENSMUSG00000037247.3"
## [1749] "ENSMUSG00000028314.6" "ENSMUSG00000115286.1"
## [1751] "ENSMUSG00000009647.13" "ENSMUSG00000073725.8"
## [1753] "ENSMUSG00000038324.13" "ENSMUSG00000021620.4"
## [1755] "ENSMUSG00000049932.3" "ENSMUSG00000022946.10"
## [1757] "ENSMUSG00000036918.16" "ENSMUSG00000093568.1"
## [1759] "ENSMUSG00000030138.8" "ENSMUSG00000056515.9"
## [1761] "ENSMUSG00000050526.5" "ENSMUSG00000090012.1"
## [1763] "ENSMUSG00000024259.9" "ENSMUSG00000018925.3"
## [1765] "ENSMUSG00000084811.2" "ENSMUSG00000093923.7"
## [1767] "ENSMUSG00000001416.13" "ENSMUSG00000028688.13"
## [1769] "ENSMUSG00000020372.15" "ENSMUSG00000021086.5"
## [1771] "ENSMUSG00000038510.12" "ENSMUSG00000021221.15"
## [1773] "ENSMUSG00000026301.16" "ENSMUSG00000050299.8"
## [1775] "ENSMUSG00000052738.14" "ENSMUSG00000027131.3"
## [1777] "ENSMUSG00000029168.14" "ENSMUSG00000020780.15"
## [1779] "ENSMUSG00000094437.2" "ENSMUSG00000029681.12"
## [1781] "ENSMUSG00000113087.1" "ENSMUSG00000064129.4"
## [1783] "ENSMUSG00000015443.9" "ENSMUSG00000070336.3"
## [1785] "ENSMUSG00000074178.3" "ENSMUSG00000037594.11"
## [1787] "ENSMUSG00000045248.6" "ENSMUSG00000036411.10"
## [1789] "ENSMUSG00000095807.2" "ENSMUSG00000100175.6"
## [1791] "ENSMUSG00000039632.13" "ENSMUSG00000095324.2"
## [1793] "ENSMUSG00000024728.13" "ENSMUSG00000091122.8"
## [1795] "ENSMUSG00000111830.1" "ENSMUSG00000037266.18"
## [1797] "ENSMUSG00000099662.1" "ENSMUSG00000048439.15"
## [1799] "ENSMUSG00000020660.6" "ENSMUSG00000051355.18"
## [1801] "ENSMUSG00000022314.10" "ENSMUSG00000084875.2"
## [1803] "ENSMUSG00000087544.1" "ENSMUSG00000027620.16"
## [1805] "ENSMUSG00000057134.6" "ENSMUSG00000050912.15"
## [1807] "ENSMUSG00000034723.11" "ENSMUSG00000030549.6"
## [1809] "ENSMUSG00000096527.5" "ENSMUSG00000025027.18"
## [1811] "ENSMUSG00000037253.8" "ENSMUSG00000087360.2"
## [1813] "ENSMUSG00000047554.14" "ENSMUSG00000086954.1"
## [1815] "ENSMUSG00000046886.6" "ENSMUSG00000115335.1"
## [1817] "ENSMUSG00000058076.12" "ENSMUSG00000028102.15"
## [1819] "ENSMUSG00000075070.6" "ENSMUSG00000031843.2"
## [1821] "ENSMUSG00000020124.10" "ENSMUSG00000004285.12"
## [1823] "ENSMUSG00000040163.14" "ENSMUSG00000086277.2"
## [1825] "ENSMUSG00000024207.8" "ENSMUSG00000085286.1"
## [1827] "ENSMUSG00000040367.2" "ENSMUSG00000018559.16"
## [1829] "ENSMUSG00000040195.11" "ENSMUSG00000084984.8"
## [1831] "ENSMUSG00000055148.7" "ENSMUSG00000032101.6"
## [1833] "ENSMUSG00000005131.4" "ENSMUSG00000022388.14"
## [1835] "ENSMUSG00000020986.13" "ENSMUSG00000100274.1"
## [1837] "ENSMUSG00000022710.17" "ENSMUSG00000028522.16"
## [1839] "ENSMUSG00000099872.1" "ENSMUSG00000069633.12"
## [1841] "ENSMUSG00000029660.10" "ENSMUSG00000079006.3"
## [1843] "ENSMUSG00000023403.14" "ENSMUSG00000045064.5"
## [1845] "ENSMUSG00000078129.2" "ENSMUSG00000069565.12"
## [1847] "ENSMUSG00000112785.1" "ENSMUSG00000021850.14"
## [1849] "ENSMUSG00000112337.1" "ENSMUSG00000039652.16"
## [1851] "ENSMUSG00000090118.1" "ENSMUSG00000098144.8"
## [1853] "ENSMUSG00000028809.16" "ENSMUSG00000033882.15"
## [1855] "ENSMUSG00000022683.13" "ENSMUSG00000040600.9"
## [1857] "ENSMUSG00000069049.11" "ENSMUSG00000022685.9"
## [1859] "ENSMUSG00000007029.16" "ENSMUSG00000028284.13"
## [1861] "ENSMUSG00000116491.1" "ENSMUSG00000110275.1"
## [1863] "ENSMUSG00000027255.14" "ENSMUSG00000035179.4"
## [1865] "ENSMUSG00000099508.1" "ENSMUSG00000025373.14"
## [1867] "ENSMUSG00000036822.6" "ENSMUSG00000020455.16"
## [1869] "ENSMUSG00000027379.13" "ENSMUSG00000027404.15"
## [1871] "ENSMUSG00000025790.14" "ENSMUSG00000022664.11"
## [1873] "ENSMUSG00000025234.11" "ENSMUSG00000086112.1"
## [1875] "ENSMUSG00000108354.1" "ENSMUSG00000090141.1"
## [1877] "ENSMUSG00000005763.15" "ENSMUSG00000032254.10"
## [1879] "ENSMUSG00000035206.10" "ENSMUSG00000061451.13"
## [1881] "ENSMUSG00000022103.10" "ENSMUSG00000062438.4"
## [1883] "ENSMUSG00000113786.1" "ENSMUSG00000029634.15"
## [1885] "ENSMUSG00000039176.17" "ENSMUSG00000030096.8"
## [1887] "ENSMUSG00000044306.4" "ENSMUSG00000099735.1"
## [1889] "ENSMUSG00000030345.16" "ENSMUSG00000058586.12"
## [1891] "ENSMUSG00000005609.16" "ENSMUSG00000026941.16"
## [1893] "ENSMUSG00000027613.15" "ENSMUSG00000110717.1"
## [1895] "ENSMUSG00000041716.7" "ENSMUSG00000100911.2"
## [1897] "ENSMUSG00000063900.5" "ENSMUSG00000099794.1"
## [1899] "ENSMUSG00000062647.16" "ENSMUSG00000022487.14"
## [1901] "ENSMUSG00000038498.4" "ENSMUSG00000024160.16"
## [1903] "ENSMUSG00000034303.7" "ENSMUSG00000029474.7"
## [1905] "ENSMUSG00000021577.14" "ENSMUSG00000110310.1"
## [1907] "ENSMUSG00000075420.6" "ENSMUSG00000014351.12"
## [1909] "ENSMUSG00000054362.9" "ENSMUSG00000048174.2"
## [1911] "ENSMUSG00000031847.8" "ENSMUSG00000030611.10"
## [1913] "ENSMUSG00000048222.3" "ENSMUSG00000079702.5"
## [1915] "ENSMUSG00000026857.9" "ENSMUSG00000115139.1"
## [1917] "ENSMUSG00000070544.6" "ENSMUSG00000038909.16"
## [1919] "ENSMUSG00000053205.9" "ENSMUSG00000021758.14"
## [1921] "ENSMUSG00000024826.15" "ENSMUSG00000052273.4"
## [1923] "ENSMUSG00000027940.18" "ENSMUSG00000078919.10"
## [1925] "ENSMUSG00000110929.1" "ENSMUSG00000092192.7"
## [1927] "ENSMUSG00000020463.15" "ENSMUSG00000028567.8"
## [1929] "ENSMUSG00000031620.16" "ENSMUSG00000028799.15"
## [1931] "ENSMUSG00000019373.11" "ENSMUSG00000086366.1"
## [1933] "ENSMUSG00000030922.12" "ENSMUSG00000022089.9"
## [1935] "ENSMUSG00000099792.1" "ENSMUSG00000095135.7"
## [1937] "ENSMUSG00000005982.14" "ENSMUSG00000101314.1"
## [1939] "ENSMUSG00000030741.15" "ENSMUSG00000008129.14"
## [1941] "ENSMUSG00000021916.16" "ENSMUSG00000001018.15"
## [1943] "ENSMUSG00000031330.8" "ENSMUSG00000047407.17"
## [1945] "ENSMUSG00000096901.7" "ENSMUSG00000037364.12"
## [1947] "ENSMUSG00000019878.8" "ENSMUSG00000032187.16"
## [1949] "ENSMUSG00000025288.1" "ENSMUSG00000106938.1"
## [1951] "ENSMUSG00000005103.12" "ENSMUSG00000047187.9"
## [1953] "ENSMUSG00000025134.2" "ENSMUSG00000028333.10"
## [1955] "ENSMUSG00000051586.17" "ENSMUSG00000033276.18"
## [1957] "ENSMUSG00000096898.2" "ENSMUSG00000115245.1"
## [1959] "ENSMUSG00000029138.4" "ENSMUSG00000048406.5"
## [1961] "ENSMUSG00000027306.15" "ENSMUSG00000037720.16"
## [1963] "ENSMUSG00000100668.1" "ENSMUSG00000099681.1"
## [1965] "ENSMUSG00000002205.16" "ENSMUSG00000015120.15"
## [1967] "ENSMUSG00000062203.15" "ENSMUSG00000075232.6"
## [1969] "ENSMUSG00000052833.9" "ENSMUSG00000086703.1"
## [1971] "ENSMUSG00000105978.1" "ENSMUSG00000032802.8"
## [1973] "ENSMUSG00000116585.1" "ENSMUSG00000112497.1"
## [1975] "ENSMUSG00000079701.7" "ENSMUSG00000045391.4"
## [1977] "ENSMUSG00000075571.8" "ENSMUSG00000028557.10"
## [1979] "ENSMUSG00000117229.1" "ENSMUSG00000095028.6"
## [1981] "ENSMUSG00000117666.1" "ENSMUSG00000048520.16"
## [1983] "ENSMUSG00000075054.5" "ENSMUSG00000029402.8"
## [1985] "ENSMUSG00000084780.7" "ENSMUSG00000037638.5"
## [1987] "ENSMUSG00000025532.13" "ENSMUSG00000031556.6"
## [1989] "ENSMUSG00000024491.16" "ENSMUSG00000021615.13"
## [1991] "ENSMUSG00000045983.16" "ENSMUSG00000054160.2"
## [1993] "ENSMUSG00000117752.1" "ENSMUSG00000038374.10"
## [1995] "ENSMUSG00000018449.12" "ENSMUSG00000090764.8"
## [1997] "ENSMUSG00000043015.15" "ENSMUSG00000031158.11"
## [1999] "ENSMUSG00000034563.16" "ENSMUSG00000061887.14"
##
## $timestamp
## [1] "2026-05-31 04:45:20 UTC"
##
## $results
## class: SingleCellExperiment
## dim: 2000 500
## metadata(4): neighbors velocity_params velocity_graph
## velocity_graph_neg
## assays(6): X spliced ... Mu velocity
## rownames(2000): ENSMUSG00000047995.7
## ENSMUSG00000048686.4 ... ENSMUSG00000034563.16
## ENSMUSG00000061887.14
## rowData names(4): velocity_gamma velocity_qreg_ratio
## velocity_r2 velocity_genes
## colnames(500): CCCATACTCCGAAGAG AATCCAGTCATCTGCC ...
## CACCTTGTCGTAGGAG TTCCCAGAGACTAAGT
## colData names(7): velocity_self_transition root_cells
## ... velocity_confidence
## velocity_confidence_transition## reducedDimNames(1): X_pca
## mainExpName: NULL## altExpNames(0):
11.3 CellRank: Advanced Fate Mapping
When standard trajectory tools (like Slingshot) struggle with complex, cyclic, or highly heterogeneous developmental processes, CellRank shines. CellRank elegantly combines directed RNA velocity data with undirected transcriptomic similarity to robustly infer terminal states and cellular fate probabilities.
sclet brings the full power of CellRank directly to your SCE object, again utilizing the isolated basilisk Python environment to prevent dependency conflicts.
11.3.1 Run CellRank
RunCellRank() is designed to automatically sense the upstream velocity data generated by RunVelocity(). It reads the state, computes the transition matrix, and identifies the terminal states.
# Compute transition matrix, terminal states, and absorption probabilities
sce <- RunCellRank(sce, reduction = "PCA", cluster_key = "cluster")## Using Python: /home/runner/.pyenv/versions/3.12.13/bin/python3.12
## Creating virtual environment '/home/runner/.cache/R/basilisk/1.24.0/sclet/0.99.5/sclet_cellrank_env' ...## Done!
## Installing packages: pip, wheel, setuptools## Installing packages: 'scanpy==1.10.1', 'anndata==0.10.7', 'numpy==1.26.4', 'pandas==2.2.2', 'scvelo==0.3.2', 'cellrank==2.0.7'## Virtual environment '/home/runner/.cache/R/basilisk/1.24.0/sclet/0.99.5/sclet_cellrank_env' successfully created.## $method
## [1] "CellRank"
##
## $cluster_key
## [1] "cluster"
##
## $terminal_states
## [1] NA "1" NA "1" NA NA "4" NA NA "1" NA NA NA
## [14] NA "1" "1" NA NA NA NA NA NA NA NA NA "4"
## [27] NA "1" "4" NA "1" NA "1" NA "1" NA NA NA NA
## [40] NA "1" NA NA NA NA NA NA NA NA "1" "1" NA
## [53] NA "1" NA NA NA NA NA NA NA NA NA "4" NA
## [66] NA "1" NA NA NA NA NA NA NA NA NA NA NA
## [79] NA NA NA NA "4" NA "1" NA NA NA NA "1" NA
## [92] "1" NA "1" NA NA NA NA NA NA NA NA NA NA
## [105] NA NA NA "4" NA NA NA "4" NA NA NA "4" NA
## [118] "4" NA "1" NA NA NA "1" NA NA NA NA NA NA
## [131] NA NA NA NA "4" NA NA NA NA NA NA NA NA
## [144] "4" NA NA NA NA NA "4" NA NA NA NA NA "4"
## [157] NA NA NA NA NA "4" "1" NA NA NA NA NA NA
## [170] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [183] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [196] NA NA NA NA NA NA NA NA NA NA NA "1" "1"
## [209] "4" NA NA NA "1" NA NA NA NA NA NA NA NA
## [222] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [235] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [248] NA NA NA NA NA NA NA NA NA NA NA NA "1"
## [261] NA NA NA NA NA NA NA NA NA NA NA "4" NA
## [274] NA NA NA NA NA NA NA NA NA NA NA NA "1"
## [287] NA NA NA NA NA NA "1" NA NA NA NA NA NA
## [300] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [313] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [326] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [339] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [352] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [365] "1" NA NA "4" NA NA NA NA NA NA NA NA NA
## [378] NA NA NA NA NA NA "4" NA NA NA NA NA NA
## [391] "1" NA NA NA NA NA NA NA NA NA NA NA NA
## [404] NA NA NA NA NA "4" NA NA NA NA NA NA NA
## [417] NA NA NA NA "4" NA NA NA NA NA NA NA NA
## [430] NA NA NA NA NA NA NA NA NA NA NA NA "4"
## [443] NA NA NA "4" NA NA NA NA NA NA "4" NA NA
## [456] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [469] NA NA NA NA NA NA "4" NA NA NA NA NA NA
## [482] NA "4" "4" NA NA NA "4" NA NA NA NA "4" NA
## [495] NA NA "1" "4" "4" NA
##
## $absorption_probs
## prob_1 prob_2
## [1,] 0.984497658 0.01550066
## [2,] 1.000000000 0.00000000
## [3,] 0.975219398 0.02477908
## [4,] 1.000000000 0.00000000
## [5,] 0.948881966 0.05096147
## [6,] 0.948882048 0.05096155
## [7,] 0.000000000 1.00000000
## [8,] 0.948881967 0.05096160
## [9,] 0.948882093 0.05096152
## [10,] 1.000000000 0.00000000
## [11,] 0.948885129 0.05096051
## [12,] 0.948881924 0.05096143
## [13,] 0.948881570 0.05096194
## [14,] 0.948881977 0.05096148
## [15,] 1.000000000 0.00000000
## [16,] 1.000000000 0.00000000
## [17,] 0.948881839 0.05096160
## [18,] 0.948881972 0.05096151
## [19,] 0.948881960 0.05096152
## [20,] 0.949073385 0.05089207
## [21,] 0.945682914 0.05416127
## [22,] 0.948881978 0.05096145
## [23,] 0.948881796 0.05096171
## [24,] 0.948881867 0.05096170
## [25,] 0.948881318 0.05096217
## [26,] 0.000000000 1.00000000
## [27,] 0.948881967 0.05096152
## [28,] 1.000000000 0.00000000
## [29,] 0.000000000 1.00000000
## [30,] 0.948892115 0.05095808
## [31,] 1.000000000 0.00000000
## [32,] 0.948881034 0.05096244
## [33,] 1.000000000 0.00000000
## [34,] 0.948881831 0.05096168
## [35,] 1.000000000 0.00000000
## [36,] 0.948880251 0.05096364
## [37,] 0.948888870 0.05095985
## [38,] 0.936366246 0.06363336
## [39,] 0.948882134 0.05096151
## [40,] 0.948881900 0.05096164
## [41,] 1.000000000 0.00000000
## [42,] 0.948882012 0.05096148
## [43,] 0.948881913 0.05096152
## [44,] 0.948882010 0.05096152
## [45,] 0.948881913 0.05096145
## [46,] 0.952322401 0.04767725
## [47,] 0.948882153 0.05096149
## [48,] 0.948866467 0.05097712
## [49,] 0.948881998 0.05096146
## [50,] 1.000000000 0.00000000
## [51,] 1.000000000 0.00000000
## [52,] 0.948882027 0.05096147
## [53,] 0.948881940 0.05096149
## [54,] 1.000000000 0.00000000
## [55,] 0.948881999 0.05096144
## [56,] 0.948881997 0.05096152
## [57,] 0.948881833 0.05096169
## [58,] 0.955028302 0.04497109
## [59,] 0.972405670 0.02759385
## [60,] 0.948882101 0.05096137
## [61,] 0.948881686 0.05096188
## [62,] 0.948878178 0.05096680
## [63,] 0.948885330 0.05096053
## [64,] 0.000000000 1.00000000
## [65,] 0.948881894 0.05096159
## [66,] 0.084780904 0.91521900
## [67,] 1.000000000 0.00000000
## [68,] 0.948882096 0.05096146
## [69,] 0.916018107 0.08398119
## [70,] 0.948892115 0.05095808
## [71,] 0.737140773 0.26285649
## [72,] 0.948882006 0.05096159
## [73,] 0.948881941 0.05096150
## [74,] 0.948881915 0.05096165
## [75,] 0.902439372 0.09741173
## [76,] 0.948881802 0.05096170
## [77,] 0.921598258 0.07840144
## [78,] 0.948881978 0.05096150
## [79,] 0.957917331 0.04208116
## [80,] 0.948882285 0.05096186
## [81,] 0.948881946 0.05096149
## [82,] 0.948890624 0.05095946
## [83,] 0.000000000 1.00000000
## [84,] 0.948882039 0.05096148
## [85,] 1.000000000 0.00000000
## [86,] 0.948325018 0.05151863
## [87,] 0.971160297 0.02883929
## [88,] 0.418956375 0.58104315
## [89,] 0.948881993 0.05096147
## [90,] 1.000000000 0.00000000
## [91,] 0.948882053 0.05096144
## [92,] 1.000000000 0.00000000
## [93,] 0.948881931 0.05096146
## [94,] 1.000000000 0.00000000
## [95,] 0.948881907 0.05096147
## [96,] 0.948882004 0.05096147
## [97,] 0.948881876 0.05096160
## [98,] 0.948881889 0.05096142
## [99,] 0.948881937 0.05096150
## [100,] 0.948881793 0.05096170
## [101,] 0.948884223 0.05096076
## [102,] 0.968207760 0.03179206
## [103,] 0.948881987 0.05096147
## [104,] 0.948889228 0.05095971
## [105,] 0.007567747 0.99243225
## [106,] 0.948881970 0.05096153
## [107,] 0.347455591 0.65254411
## [108,] 0.000000000 1.00000000
## [109,] 0.976969817 0.02302780
## [110,] 0.969027511 0.03097198
## [111,] 0.948882178 0.05096142
## [112,] 0.000000000 1.00000000
## [113,] 0.948882027 0.05096147
## [114,] 0.948881854 0.05096166
## [115,] 0.948882033 0.05096149
## [116,] 0.000000000 1.00000000
## [117,] 0.262852817 0.73714686
## [118,] 0.000000000 1.00000000
## [119,] 0.948883432 0.05096317
## [120,] 1.000000000 0.00000000
## [121,] 0.948881819 0.05096168
## [122,] 0.947314728 0.05252914
## [123,] 0.948882028 0.05096148
## [124,] 1.000000000 0.00000000
## [125,] 0.954801133 0.04519850
## [126,] 0.948889537 0.05095959
## [127,] 0.948881987 0.05096150
## [128,] 0.948861735 0.05098185
## [129,] 0.948881999 0.05096147
## [130,] 0.069132141 0.93086777
## [131,] 0.948881794 0.05096163
## [132,] 0.948881977 0.05096147
## [133,] 0.883707874 0.11614616
## [134,] 0.323349977 0.67664967
## [135,] 0.000000000 1.00000000
## [136,] 0.872309102 0.12768959
## [137,] 0.948881852 0.05096155
## [138,] 0.948882048 0.05096149
## [139,] 0.948881746 0.05096174
## [140,] 0.948880293 0.05096391
## [141,] 0.948879875 0.05096407
## [142,] 0.417461453 0.58253785
## [143,] 0.948881805 0.05096167
## [144,] 0.000000000 1.00000000
## [145,] 0.948881788 0.05096170
## [146,] 0.945324697 0.05467452
## [147,] 0.152733081 0.84726676
## [148,] 0.948892643 0.05095806
## [149,] 0.948881797 0.05096178
## [150,] 0.000000000 1.00000000
## [151,] 0.948900029 0.05095902
## [152,] 0.522300930 0.47769828
## [153,] 0.938303739 0.06154169
## [154,] 0.948881964 0.05096145
## [155,] 0.973298018 0.02670144
## [156,] 0.000000000 1.00000000
## [157,] 0.948874111 0.05096948
## [158,] 0.948881768 0.05096172
## [159,] 0.948881820 0.05096169
## [160,] 0.948881793 0.05096169
## [161,] 0.628156244 0.37184305
## [162,] 0.000000000 1.00000000
## [163,] 1.000000000 0.00000000
## [164,] 0.948892816 0.05095790
## [165,] 0.948892193 0.05095811
## [166,] 0.948860252 0.05098330
## [167,] 0.948881946 0.05096155
## [168,] 0.948881920 0.05096152
## [169,] 0.948881883 0.05096166
## [170,] 0.948881969 0.05096151
## [171,] 0.948893650 0.05095845
## [172,] 0.935536906 0.06446275
## [173,] 0.948881932 0.05096148
## [174,] 0.948881985 0.05096151
## [175,] 0.948889537 0.05095959
## [176,] 0.870961404 0.12903801
## [177,] 0.948882017 0.05096153
## [178,] 0.938367444 0.06147798
## [179,] 0.948878178 0.05096680
## [180,] 0.948892621 0.05095807
## [181,] 0.948881522 0.05096193
## [182,] 0.948888870 0.05095985
## [183,] 0.926361201 0.07363782
## [184,] 0.948892745 0.05095794
## [185,] 0.948881565 0.05096193
## [186,] 0.948881985 0.05096151
## [187,] 0.953700301 0.04629934
## [188,] 0.949032660 0.05096466
## [189,] 0.948882062 0.05096152
## [190,] 0.948883348 0.05096099
## [191,] 0.948881919 0.05096147
## [192,] 0.948875300 0.05096824
## [193,] 0.948880270 0.05096315
## [194,] 0.948882027 0.05096176
## [195,] 0.947473730 0.05237006
## [196,] 0.948883588 0.05096148
## [197,] 0.948873487 0.05097738
## [198,] 0.948881639 0.05096186
## [199,] 0.948881327 0.05096209
## [200,] 0.948881815 0.05096168
## [201,] 0.944129619 0.05571479
## [202,] 0.948881024 0.05096240
## [203,] 0.948888245 0.05096006
## [204,] 0.948883721 0.05096093
## [205,] 0.948892395 0.05095806
## [206,] 0.948882086 0.05096153
## [207,] 1.000000000 0.00000000
## [208,] 1.000000000 0.00000000
## [209,] 0.000000000 1.00000000
## [210,] 0.948892644 0.05095793
## [211,] 0.948887174 0.05096048
## [212,] 0.948882002 0.05096147
## [213,] 1.000000000 0.00000000
## [214,] 0.948881967 0.05096150
## [215,] 0.948883110 0.05096103
## [216,] 0.948892096 0.05095809
## [217,] 0.689442370 0.31055718
## [218,] 0.948881904 0.05096162
## [219,] 0.948881927 0.05096148
## [220,] 0.130625438 0.86937443
## [221,] 0.948881817 0.05096171
## [222,] 0.948881972 0.05096147
## [223,] 0.948881949 0.05096150
## [224,] 0.948882048 0.05096156
## [225,] 0.948881040 0.05096244
## [226,] 0.948892115 0.05095808
## [227,] 0.948881956 0.05096144
## [228,] 0.948881967 0.05096149
## [229,] 0.948892347 0.05095806
## [230,] 0.948881746 0.05096174
## [231,] 0.948881953 0.05096147
## [232,] 0.948881994 0.05096150
## [233,] 0.948882068 0.05096150
## [234,] 0.948881599 0.05096303
## [235,] 0.948881804 0.05096171
## [236,] 0.928287485 0.07155945
## [237,] 0.948881982 0.05096148
## [238,] 0.948883704 0.05096081
## [239,] 0.948881784 0.05096166
## [240,] 0.948892331 0.05095804
## [241,] 0.948883220 0.05096115
## [242,] 0.948892745 0.05095794
## [243,] 0.948882325 0.05096143
## [244,] 0.948882039 0.05096145
## [245,] 0.948881748 0.05096172
## [246,] 0.948880591 0.05096323
## [247,] 0.948881956 0.05096156
## [248,] 0.948888724 0.05095954
## [249,] 0.948882134 0.05096139
## [250,] 0.948881917 0.05096146
## [251,] 0.948892369 0.05095789
## [252,] 0.948892334 0.05095790
## [253,] 0.948881862 0.05096167
## [254,] 0.948882150 0.05096146
## [255,] 0.948881985 0.05096146
## [256,] 0.948882146 0.05096150
## [257,] 0.948883424 0.05096097
## [258,] 0.948881984 0.05096148
## [259,] 0.948881998 0.05096146
## [260,] 1.000000000 0.00000000
## [261,] 0.948879994 0.05096394
## [262,] 0.948881171 0.05096440
## [263,] 0.948881897 0.05096162
## [264,] 0.948881791 0.05096174
## [265,] 0.935604329 0.06439315
## [266,] 0.948863770 0.05097983
## [267,] 0.948892377 0.05095790
## [268,] 0.948881959 0.05096145
## [269,] 0.948875064 0.05096847
## [270,] 0.948881924 0.05096147
## [271,] 0.948881964 0.05096159
## [272,] 0.000000000 1.00000000
## [273,] 0.948882040 0.05096150
## [274,] 0.948881965 0.05096150
## [275,] 0.948883057 0.05096117
## [276,] 0.943914037 0.05593048
## [277,] 0.948881936 0.05096145
## [278,] 0.948883434 0.05096120
## [279,] 0.948892321 0.05095790
## [280,] 0.948881917 0.05096148
## [281,] 0.948882318 0.05096129
## [282,] 0.948881947 0.05096153
## [283,] 0.948881870 0.05096185
## [284,] 0.948881981 0.05096148
## [285,] 0.047878972 0.95212098
## [286,] 1.000000000 0.00000000
## [287,] 0.948879926 0.05096455
## [288,] 0.948882138 0.05096146
## [289,] 0.948856064 0.05098754
## [290,] 0.948882808 0.05096136
## [291,] 0.948881972 0.05096150
## [292,] 0.948893284 0.05095823
## [293,] 1.000000000 0.00000000
## [294,] 0.948883070 0.05096104
## [295,] 0.948881918 0.05096157
## [296,] 0.948882014 0.05096159
## [297,] 0.948877817 0.05096563
## [298,] 0.947319038 0.05268025
## [299,] 0.948603787 0.05124050
## [300,] 0.942076110 0.05792302
## [301,] 0.948881974 0.05096145
## [302,] 0.948844905 0.05099870
## [303,] 0.948881399 0.05096280
## [304,] 0.948882246 0.05096126
## [305,] 0.948882054 0.05096147
## [306,] 0.122839681 0.87716021
## [307,] 0.948881926 0.05096160
## [308,] 0.948881991 0.05096147
## [309,] 0.948881788 0.05096172
## [310,] 0.584382281 0.41561709
## [311,] 0.111360131 0.88863976
## [312,] 0.948892534 0.05095806
## [313,] 0.944154481 0.05568991
## [314,] 0.948881949 0.05096150
## [315,] 0.948881992 0.05096148
## [316,] 0.948881773 0.05096169
## [317,] 0.948881936 0.05096145
## [318,] 0.948881943 0.05096142
## [319,] 0.948881940 0.05096155
## [320,] 0.948881969 0.05096143
## [321,] 0.948893272 0.05095824
## [322,] 0.948881948 0.05096145
## [323,] 0.948881991 0.05096148
## [324,] 0.948892525 0.05095805
## [325,] 0.948881971 0.05096143
## [326,] 0.143406930 0.85659293
## [327,] 0.948881999 0.05096153
## [328,] 0.948881935 0.05096146
## [329,] 0.920020827 0.07997707
## [330,] 0.955400947 0.04459839
## [331,] 0.948881905 0.05096148
## [332,] 0.948881977 0.05096147
## [333,] 0.948892345 0.05095804
## [334,] 0.948882001 0.05096148
## [335,] 0.948882435 0.05096134
## [336,] 0.948881946 0.05096144
## [337,] 0.948882012 0.05096147
## [338,] 0.948882339 0.05096140
## [339,] 0.948882047 0.05096149
## [340,] 0.948882220 0.05096148
## [341,] 0.948881984 0.05096157
## [342,] 0.940795769 0.05904904
## [343,] 0.948882111 0.05096155
## [344,] 0.948881959 0.05096147
## [345,] 0.948881747 0.05096169
## [346,] 0.948881963 0.05096152
## [347,] 0.948879981 0.05096344
## [348,] 0.337353534 0.66264621
## [349,] 0.948883239 0.05096109
## [350,] 0.953559901 0.04643964
## [351,] 0.948881996 0.05096146
## [352,] 0.948881865 0.05096154
## [353,] 0.878597805 0.12125713
## [354,] 0.948878788 0.05096465
## [355,] 0.943124053 0.05672041
## [356,] 0.948881969 0.05096145
## [357,] 0.948880913 0.05096313
## [358,] 0.948882002 0.05096151
## [359,] 0.948892534 0.05095806
## [360,] 0.973579869 0.02641978
## [361,] 0.138114335 0.86188553
## [362,] 0.948883791 0.05096075
## [363,] 0.967472211 0.03252753
## [364,] 0.936269155 0.06357659
## [365,] 1.000000000 0.00000000
## [366,] 0.902029763 0.09796956
## [367,] 0.948889989 0.05095938
## [368,] 0.000000000 1.00000000
## [369,] 0.948880691 0.05096302
## [370,] 0.948882042 0.05096148
## [371,] 0.948882009 0.05096149
## [372,] 0.948880793 0.05096272
## [373,] 0.947977553 0.05186606
## [374,] 0.948882018 0.05096152
## [375,] 0.948881984 0.05096151
## [376,] 0.948882024 0.05096155
## [377,] 0.948882164 0.05096143
## [378,] 0.948892261 0.05095808
## [379,] 0.948881752 0.05096174
## [380,] 0.948892128 0.05095807
## [381,] 0.948882083 0.05096149
## [382,] 0.948881989 0.05096157
## [383,] 0.918600218 0.08139940
## [384,] 0.000000000 1.00000000
## [385,] 0.948881980 0.05096153
## [386,] 0.948864111 0.05097940
## [387,] 0.948885123 0.05096036
## [388,] 0.980515409 0.01948349
## [389,] 0.948882007 0.05096148
## [390,] 0.948892527 0.05095806
## [391,] 1.000000000 0.00000000
## [392,] 0.948881701 0.05096175
## [393,] 0.948881551 0.05096188
## [394,] 0.924592924 0.07540378
## [395,] 0.947692453 0.05215129
## [396,] 0.948876443 0.05096713
## [397,] 0.948881938 0.05096148
## [398,] 0.948881925 0.05096150
## [399,] 0.948882863 0.05096121
## [400,] 0.948881475 0.05096194
## [401,] 0.171522133 0.82847767
## [402,] 0.948881968 0.05096149
## [403,] 0.948865705 0.05097825
## [404,] 0.948881660 0.05096181
## [405,] 0.748783978 0.25121548
## [406,] 0.948882019 0.05096142
## [407,] 0.092440136 0.90755979
## [408,] 0.948881763 0.05096169
## [409,] 0.000000000 1.00000000
## [410,] 0.948882028 0.05096158
## [411,] 0.964863685 0.03513537
## [412,] 0.948852202 0.05099137
## [413,] 0.948878734 0.05096643
## [414,] 0.948881676 0.05096175
## [415,] 0.948883148 0.05096115
## [416,] 0.948881823 0.05096171
## [417,] 0.948882015 0.05096154
## [418,] 0.948881954 0.05096142
## [419,] 0.948881912 0.05096165
## [420,] 0.948881904 0.05096162
## [421,] 0.000000000 1.00000000
## [422,] 0.948881976 0.05096151
## [423,] 0.948888870 0.05095985
## [424,] 0.948881911 0.05096140
## [425,] 0.948893360 0.05095823
## [426,] 0.948881969 0.05096146
## [427,] 0.893865551 0.10613404
## [428,] 0.948881975 0.05096154
## [429,] 0.948882033 0.05096150
## [430,] 0.948882010 0.05096149
## [431,] 0.948881876 0.05096156
## [432,] 0.948882096 0.05096151
## [433,] 0.948881823 0.05096169
## [434,] 0.925777424 0.07422185
## [435,] 0.948881786 0.05096170
## [436,] 0.948881910 0.05096143
## [437,] 0.948892345 0.05095804
## [438,] 0.140203691 0.85979619
## [439,] 0.948881973 0.05096145
## [440,] 0.956601709 0.04339745
## [441,] 0.392096503 0.60790287
## [442,] 0.000000000 1.00000000
## [443,] 0.948881956 0.05096144
## [444,] 0.948882055 0.05096148
## [445,] 0.948882405 0.05096175
## [446,] 0.000000000 1.00000000
## [447,] 0.948882016 0.05096149
## [448,] 0.306654239 0.69334553
## [449,] 0.948892527 0.05095806
## [450,] 0.948883409 0.05096090
## [451,] 0.266675146 0.73332453
## [452,] 0.948881790 0.05096171
## [453,] 0.000000000 1.00000000
## [454,] 0.948881910 0.05096154
## [455,] 0.924765958 0.07508170
## [456,] 0.948881969 0.05096164
## [457,] 0.921526995 0.07832112
## [458,] 0.948881855 0.05096158
## [459,] 0.948881968 0.05096165
## [460,] 0.948881996 0.05096152
## [461,] 0.948882026 0.05096143
## [462,] 0.948881771 0.05096169
## [463,] 0.948882933 0.05096120
## [464,] 0.025216435 0.97478354
## [465,] 0.038621042 0.96137893
## [466,] 0.948881898 0.05096139
## [467,] 0.948881946 0.05096141
## [468,] 0.948881804 0.05096166
## [469,] 0.948882785 0.05096113
## [470,] 0.221271801 0.77872795
## [471,] 0.949278105 0.05072070
## [472,] 0.948881800 0.05096167
## [473,] 0.948881957 0.05096149
## [474,] 0.948882003 0.05096147
## [475,] 0.000000000 1.00000000
## [476,] 0.931932205 0.06806690
## [477,] 0.948881997 0.05096148
## [478,] 0.948851684 0.05099185
## [479,] 0.948881979 0.05096145
## [480,] 0.842761446 0.15723456
## [481,] 0.948892528 0.05095805
## [482,] 0.948881970 0.05096149
## [483,] 0.000000000 1.00000000
## [484,] 0.000000000 1.00000000
## [485,] 0.658609169 0.34138999
## [486,] 0.190052434 0.80994731
## [487,] 0.117278127 0.88272176
## [488,] 0.000000000 1.00000000
## [489,] 0.035360762 0.96463921
## [490,] 0.129997008 0.87000285
## [491,] 0.948881961 0.05096147
## [492,] 0.948881972 0.05096147
## [493,] 0.000000000 1.00000000
## [494,] 0.948881956 0.05096146
## [495,] 0.018775824 0.98122416
## [496,] 0.948790269 0.05105324
## [497,] 1.000000000 0.00000000
## [498,] 0.000000000 1.00000000
## [499,] 0.000000000 1.00000000
## [500,] 0.948881788 0.05096171
##
## $timestamp
## [1] "2026-05-31 04:46:45 UTC"The resulting terminal state assignments and directional fate probabilities are written directly into colData(sce). This means you can visualize these advanced metrics using standard sclet plotting functions like CellDimPlot or FeatureDimPlot.
# Plot the categorical terminal states assigned to the cells
CellDimPlot(sce, group.by = "cellrank_terminal_states", reduction = "UMAP")
# Inspect the first few absorption probabilities stored in the CellRank state
head(get_cellrank(sce)$absorption_probs[, seq_len(min(3, ncol(get_cellrank(sce)$absorption_probs))), drop = FALSE])## prob_1 prob_2
## [1,] 0.9844977 0.01550066
## [2,] 1.0000000 0.00000000
## [3,] 0.9752194 0.02477908
## [4,] 1.0000000 0.00000000
## [5,] 0.9488820 0.05096147
## [6,] 0.9488820 0.05096155References
Bergen, Volker, Marius Lange, Stefan Peidli, F. Alexander Wolf, and Fabian J. Theis. 2020. “Generalizing RNA Velocity to Transient Cell States Through Dynamical Modeling.” Nature Biotechnology 38 (12): 1408–14. https://doi.org/10.1038/s41587-020-0591-3.