Liu et al, Genome biology, 2020, Genome-wide studies reveal the essential and opposite roles of ARID1A in controlling human cardiogenesis and neurogenesis from pluripotent stem cells

 in virto human embryonic stem cell study. hESC

ARID1A in H9 hESC were deleted using dual guided-RNA mediated CRISP-Cas9 method. 

" Mutations in 4 different SWI/SNF subunits including ARID1A/B were identified in three congenital syndromes that include both neural and cardiac defects: Coffin-Siris syndrome (CSS), Nicolaides- Baraitser syndrome (NCBRS), and ARID1B-related intellectual disability (ID) syndrome  Patients with these syndromes show severe intellectual deficits as well as cardiac defects such as atrial/ventricular septal defects, patent ductus

arteriosus (PDA), mitral and pulmonary atresia, aortic stenosis, and single right ventricle. These data indicate that abnormal ARID1A activity can lead to defective  formation of both the heart and brain in humans. However, the molecular mechanisms  by which ARID1A controls human cardiogenesis and neurogenesis still remain. elusive." 

in hESC, Surprisingly, knockout-of-ARID1A in hESCs (ARID1A−/−) led to spontaneous neural. differentiation even under pluripotent stem cell culture conditions. Additionally, under conditions of targeted cardiac differentiation, ARID1A−/− hESCs gave rise to robustly increased numbers of neural cells, including neural stem cells and neurons, whereas cardiac differentiation was significantly suppressed

single-cell RNA reveals spontaneous differentiation neural differentiation in ARIA-/- cells. 

So, scRNA data are available for WT and KO in cardiac differential and neural differentiation. Based on my understanding of Liu, GB, 2020, there are WT and ARID1A-/- hESC cells, and the hESC cells are induced for cardiac differentation in CDM3 and neural differentation in N2B27 medium. The scRNA results show ARIDA-/- lead to different clusters of ScRNA in WT and KO in both differentation conditions. So, it seems to me that these data sets can be used to contruct weight single-cell gene network to study network control.

The noise levels seem to be good testing data sets on noises and weight in controllability analysis. 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02082-4