TarCA

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.

Install

install.packages('devtools')
devtools::install_github('shadowdeng1994/TarCA')

Quickstart

library("TarCA")

• The following files are needed for TarCA.

1. 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;

2. 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

3. (optional) A path to output the result (e.g. NpResult.RData).

• 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.

Estimate Np with exemplar dataset.

• Load exemplar dataset.

load(system.file("Exemplar","Exemplar_TCA.RData",package = "TarCA"))
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,
  Fileout = NULL,
  ReturnNp = TRUE
)

**** 1. Check input data.

**** 2. Get treedata file.

**** 3. Get node2tip file.

**** 4. Get pureNode file.

**** 5. Get pureNode2organ file.

**** 6. Get CladeSizeDetail file.

**** 7. Get Np file.

• Then the Np estimation are stoarged in tmp.result[["EffN"]].

TipAnn	CladeSize	Total	EffN
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

Detect LEU with exemplar dataset.

• Load exemplar dataset.

load(system.file("Exemplar","Exemplar_LEU.RData",package = "TarCA"))
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,
  Fileout = NULL,
  ReturnNp = TRUE
)

**** 1. Check input data.

**** 2. Get BiasNode file.

**** 3. Filter BiasNode.

**** 4. Plot BiasNode.

**** 5. Get pureNode file.

**** 6. Get pureNode2organ file.

**** 7. Get CladeSizeDetail file.

**** 8. Get Np file.

• Then the Np estimation for subpopulation with expression upregulation are stoarged in tmp.result[["EffN"]].

TipAnn	CladeSize	Total	EffN
FALSE	1 (482)	482	Inf
TRUE	1 (23), 2 (5), 4 (2)	41	48.2

• Additionally, you can visualize the LEU on the phylogeny with tmp.result[["BiasFig"]].

Contributing

Contributors

Shanjun Deng, [email protected].

Citations

When using TarCA please cite: