MKMEP {cglasso}

Megakaryocyte-Erythroid Progenitors

Description

In a study about the formation of blood cells, Psaila and others (2016) have recently identified three distinct sub-populations of cells, which are all derived from hematopoietic stem cells through cell differentiation. One of these sub-populations, denoted by MK-MEP, is a previously unknown, rare population of cells that are bipotent but primarily generate megakaryocytic progeny.

Multiplex RT-qPCR has been used to profile 63 genes and 48 single human MK-MEP cells. RT-qPCR data are typically right-censored with a limit of detection fixed by the manufacturer to 40. Raw data have been mean normalized using the method proposed in Pipelers and others (2017). See Section 5 in Augugliaro and others (2018) for more details.

‘MKMEP’ is a matrix containing a subset of the data available from Psaila and others (2016).

Usage

data("MKMEP")

References

Augugliaro L., Sottile G., Wit E.C., and Vinciotti V. (2023) <doi:10.18637/jss.v105.i01>. cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values. Journal of Statistical Software 105(1), 1–58.

Augugliaro, L., Sottile, G., and Vinciotti, V. (2020) <doi:10.1007/s11222-020-09945-7>. The conditional censored graphical lasso estimator. Statistics and Computing 30, 1273–1289.

Augugliaro, L., Abbruzzo, A., and Vinciotti, V. (2020) <doi:10.1093/biostatistics/kxy043>. \ell_1-Penalized censored Gaussian graphical model. Biostatistics 21, e1–e16.

Pipelers, P., Clement, L., Vynck, M., Hellemans, J., Vandesompele, J. and Thas, O. (2017) <doi: 10.1371/journal.pone.0182832>. A unified censored normal regression model for qPCR differential gene expression analysis. PLoS One 12, e0182832.

Psaila, B., Barkas, N., Iskander, D., Roy, A., Anderson, S., Ashley, N., Caputo, V. S., Lichtenberg, J., Loaiza, S., Bodine, D. M. and others. (2016) <doi: 10.1186/s13059-016-0939-7> Single-cell profiling of human megakaryocyte-erythroid progenitors identifies distinct megakaryocyte and erythroid differentiation pathways. Genome Biology 17, 83–102.

See Also

cglasso, to_graph, and the method functions summary, coef, plot, AIC.cglasso and BIC.cglasso.

Examples

data("MKMEP")
is.matrix(MKMEP)
Z <- datacggm(Y = MKMEP, up = 40)
str(Z)
is.datacggm(Z)
print(Z, width = 80L)