The method, termed as targeting coalescent analysis (TCA), computes for all cells of a tissue the average coalescent rate at the monophyletic clades of the target tissue, the inverse of which then measures the progenitor number of the tissue. Any predefined population could be investigated with TCA, independent of pre-set markers.
* To achieve a higher computational efficiency, we rewrite the entire package in a more compact way. 😄
- Dependent packages: dplyr, tidyr, tibble, ggplot2, ggtree
- Require R (>= 3.5.0).
install.packages('devtools')
devtools::install_github('shadowdeng1994/TarCA.beta')
Installation would finish in about one minute.
library("TarCA.beta")
- The following files are needed for TarCA.
- A tree file of class "phylo" with node labels.
((Cell_1,((Cell_2,Cell_3)Node_4,(Cell_4,Cell_5)Node_5)Node_3)Node_2,(((Cell_6,Cell_7)Node_8,(Cell_8,Cell_9)Node_9)Node_7,Cell_10)Node_6)Node_1;
- A dataframe with columns TipLabel and TipAnn, representing tip labels on the tree file and corresponding cell annotations.
TipLabel TipAnn Cell_1 O1 Cell_2 O1 Cell_3 O1 Cell_4 O2 Cell_5 O2 Cell_6 O2 Cell_7 O3 Cell_8 O3 Cell_9 O3 Cell_10 O3
- Effective number of progenitor can be inferred with
Np_Estimator. - Modified algorithm for detection of lineage specific expression upregulation (LEU) can be called with
LEU_Estimator. - (optional) All intermediate data are stored in ExTree format (control with ReturnExTree, default FALSE).
- Load exemplar dataset.
load(system.file("Exemplar","Exemplar_TCA.RData",package = "TarCA.beta"))
tmp.tree <- ExemplarData_1$Tree
tmp.ann <- ExemplarData_1$Ann
- Inferring Np with
Np_Estimator.
tmp.result <- Np_Estimator(
Tree = tmp.tree,
Ann = tmp.ann
)
===> Checking input files.
===> Converting to ExTree.
===> Adding AllDescendants.
===> Adding MonoClades.
===> Estimating Np.
- Then return a dataframe containing the Np estimation.
TipAnn MonoInfo Total Np O0 1 (1), 2 (2) 5 5 O1 1 (6), 2 (11), 3 (1), 5 (1) 36 26.2 O2 1 (35), 2 (17), 3 (4), 4 (2), 8 (1) 97 67.5 O3 1 (66), 2 (38), 3 (11), 4 (4), 5 (2), 7 (1) 208 158 O4 1 (50), 2 (24), 3 (6), 4 (3), 5 (1) 133 125 O5 1 (71), 2 (38), 3 (13), 4 (5) 206 197 O6 1 (32), 2 (23), 3 (9), 7 (1) 112 87.5 O7 1 (50), 2 (37), 3 (10), 4 (3), 6 (1) 172 147 O8 1 (5), 2 (3) 11 18.3 O9 1 (12), 2 (1), 3 (2) 20 27.1
This process is estimated to be completed in about 3 seconds.
- Load exemplar dataset.
load(system.file("Exemplar","Exemplar_LEU.RData",package = "TarCA.beta"))
tmp.tree <- ExemplarData_2$Tree
tmp.ann <- ExemplarData_2$Ann
- Inferring Np with
LEU_Estimator.
tmp.result <- LEU_Estimator(
Tree = tmp.tree,
Ann = tmp.ann
)
===> Checking input files.
===> Converting to ExTree.
===> Adding AllDescendants.
===> Adding ExpreBias.
===> Adding FilterBiasParent.
===> Estimating Np.
- Then return a dataframe containing the Np estimation.
TipAnn MonoInfo Total Np TRUE 1 (23), 2 (5), 4 (2) 41 48.2
This process is estimated to be completed in about 2 seconds.
Shanjun Deng, shadowdeng1994@gmail.com.
When using TarCA please cite:
- Deng S, Gong H, Zhang D, et al. A statistical method for quantifying progenitor cells reveals incipient cell fate commitments[J]. Nature Methods, 2024: 1-12.