Supplementary Figure 12: Monocle2 pseudotime assignment (cell state) of thymic hematopoietic cells observed with single-cell RNA-seq and pseudodynamics model fits to this Monocle2 cell state.
From: Inferring population dynamics from single-cell RNA-sequencing time series data
(a,b) Distribution of cells by sample across cell state (monocle2 pseudotime) on T-cell lineage. Colour: time point in days after fertilisation. (a) Kernel density estimate of union of all samples per time point (n = 442 at t = 12.5, n = 1795 at t = 13.5, n = 1052 at t = 14.5, n = 1013 at t = 15.5, n = 2616 at t = 16.5, n = 1966 at t = 17.5, n = 882 at t = 18.5, n = 939 at t = 19.5). (b) Box plot of each sample per time point (n = {152, 58, 232} at t = 12.5; n = {628, 476, 691} at t = 13.5; n = {508, 544} at t = 14.5; n = {437, 576} at t = 15.5; n = {870, 918, 828} at t = 16.5; n = {975, 991} at t = 17.5; n = {455, 427} at t = 18.5; n = {422, 517} at t = 19.5). Here, pseudotime coordinates are computed based on all replicates. Replicates are independent Drop-seq samples which are based on separate thymus samples, the two replicates at P0 are based on the two lobes of a single thymus. The center of each boxplots is the sample median, the whiskers extend from the upper (lower) hinge to the largest (smallest) data point no further than 1.5 times the interquantile range from the upper (lower) hinge. (c) Scatter plot of diffusion pseudotime cell state versus moncle2 cell state (pseudotime) on T-cell lineage cells. (d-f) Pseudodynamics parameter fits to monocle2 cell state: (d) Birth-death, (e) drift and (f) diffusion parameter estimates for three different regularisation parameters (rho).
