Journal club.

diff --git a/biblio/25032992.mdwn b/biblio/25032992.mdwn
new file mode 100644 (file)
index 0000000..9f3e465
--- /dev/null
+++ b/biblio/25032992.mdwn
@@ -0,0 +1,10 @@
+[[!meta title="Modeling bi-modality improves characterization of cell cycle on gene expression in single cells."]]
+[[!tag single_cell cell_cycle journal_club]]
+
+PLoS Comput Biol. 2014 Jul 17;10(7):e1003696. doi:10.1371/journal.pcbi.1003696
+
+McDavid A, Dennis L, Danaher P, Finak G, Krouse M, Wang A, Webster P, Beechem J, Gottardo R
+
+Modeling bi-modality improves characterization of cell cycle on gene expression in single cells.
+
+[[!pmid 25032992 desc="333 genes studied in 930 cells with the Hurdle model. Nanostring technology.  “the cell cycle explains only 5%-17% of expression variability”"]]

diff --git a/biblio/25599176.mdwn b/biblio/25599176.mdwn
new file mode 100644 (file)
index 0000000..2e9e1df
--- /dev/null
+++ b/biblio/25599176.mdwn
@@ -0,0 +1,10 @@
+[[!meta title="Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells."]]
+[[!tag single_cell cell_cycle C1 journal_club]]
+
+Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC, Stegle O.
+
+Nat Biotechnol. 2015 Jan 19. doi:10.1038/nbt.3102
+
+Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.
+
+[[!pmid 25599176 desc="scLVM (single-cell latent variable model) normalises data for cell cycle or other sources of variation."]]