The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios.

Soil microbial processes are crucial for understanding the ecological functions of arid and semi-arid lands which occupy approximately 40% of the global terrestrial ecosystems. However, how soil microbial metabolic activities may change across a wide aridity gradient in drylands remains unclear. Here, we investigated three soil microbial metabolic indices (soil organic carbon (SOC)-based microbial respiration, metabolic quotient, and microbial biomass as a proportion of total SOC) and the degree of carbon limitation for microbial respiration along a 3,200 km transect with a wide aridity gradient. The aridity gradient was customarily expressed using the aridity index (AI) which was calculated as the ratio of mean annual precipitation to mean annual evaporation, therefore, a lower AI value indicated a higher degree of aridity. Our results showed non-linear relationships between AI values and the metabolic indices with a clear aridity threshold for each of the three metabolic indices along the aridity gradient, respectively (AI = 0.13 for basal respiration, AI = 0.17 for metabolic quotient, and AI = 0.17 for MBC:SOC ratio). These metabolic indices linearly declined when AI was above the thresholds, but did not show any clear patterns when AI was below the thresholds. We also found that soil microbial respiration was highly limited by available carbon substrates at locations with higher primary production and relatively lower level of water limitation when AI was above the threshold, a counter-intuitive pattern that microbes were more starved in ecosystems with more substrate input. However, the increasing level of carbon limitation did correspond to the declining trend of the three metabolic indices along the AI gradient, which indicates that the carbon limitation influences microbial metabolism. We also found that the ratio of microbial biomass carbon to SOC in arid regions (AI < 0.2) with extremely low precipitation and primary production were not quantitatively related to SOC content. Overall, our results imply that microbial metabolism is distinctively different in arid lands than in non-arid lands.

Soil organic carbon (SOC) is a major component in the global carbon cycle. Yet how input of plant litter may influence the loss of SOC through a phenomenon called priming effect remains highly uncertain. Most published results about the priming effect came from short-term investigations for a few weeks or at the most for a few months in duration. The priming effect has not been studied at the annual time scale. In this study for 815 days, we investigated the priming effect of added maize leaves on SOC decomposition of two soil types and two treatments (bare fallow for 23 years, and adjacent old-field, represent stable and relatively labile SOC, respectively) of SOC stabilities within each soil type, using a natural 13C-isotope method. Results showed that the variation of the priming effect through time had three distinctive phases for all soils: (1) a strong negative priming phase during the first period (≈0-90 days); (2) a pulse of positive priming phase in the middle (≈70-160 and 140-350 days for soils from Hailun and Shenyang stations, respectively); and (3) a relatively stabilized phase of priming during the last stage of the incubation (>160 days and >350 days for soils from Hailun and Shenyang stations, respectively). Because of major differences in soil properties, the two soil types produced different cumulative priming effects at the end of the experiment, a positive priming effect of 3-7% for the Mollisol and a negative priming effect of 4-8% for the Alfisol. Although soil types and measurement times modulated most of the variability of the priming effect, relative SOC stabilities also influenced the priming effect for a particular soil type and at a particular dynamic phase. The stable SOC from the bare fallow treatment tended to produce a narrower variability during the first phase of negative priming and also during the second phase of positive priming. Averaged over the entire experiment, the stable SOC (i.e., the bare fallow) was at least as responsive to priming as the relatively labile SOC (i.e., the old-field) if not more responsive. The annual time scale of our experiment allowed us to demonstrate the three distinctive phases of the priming effect. Our results highlight the importance of studying the priming effect by investigating the temporal dynamics over longer time scales.