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Single-cell RNA-seq (scRNA-seq) allows researchers to define cell types on the basis of unsupervised clustering of the transcriptome. However, differences in experimental methods and computational analyses make it challenging to compare data across experiments. Here we present scmap (http://bioconductor.org/packages/scmap; web version at http://www.sanger.ac.uk/science/tools/scmap), a method for projecting cells from an scRNA-seq data set onto cell types or individual cells from other experiments.

Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues1–9 since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome8,10. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. Here, we present scmap (http://bioconductor.org/packages/scmap), a method for projecting cells from a scRNA-seq experiment onto the cell-types or individual cells identified in other experiments (the application can be run for free, without restrictions, from http://www.hemberg-lab.cloud/scmap).

The vertical concentration profiles and source contributions of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes in respirable particle samples (PM4) collected at 10, 100, 200 and 300-m altitude from the Milad Tower of Tehran, Iran during fall and winter were investigated. The average concentrations of total PAHs and total n-alkanes were 16.7 and 591 ng/m(3), respectively. The positive matrix factorization (PMF) model was applied to the chemical composition and wind data to apportion the contributing sources. The five PAH source factors identified were: 'diesel' (56.3% of total PAHs on average), 'gasoline' (15.5%), 'wood combustion, and incineration' (13%), 'industry' (9.2%), and 'road soil particle' (6.0%). The four n-alkane source factors identified were: 'petrogenic' (65% of total n-alkanes on average), 'mixture of petrogenic and biomass burning' (15%), 'mixture of biogenic and fossil fuel' (11.5%), and 'biogenic' (8.5%). Source contributions by wind sector were also estimated based on the wind sector factor loadings from PMF analysis. Directional dependence of sources was investigated using the conditional probability function (CPF) and directional relative strength (DRS) methods. The calm wind period was found to contribute to 4.4% of total PAHs and 5.0% of total n-alkanes on average. Highest average concentrations of PAHs and n-alkanes were found in the 10 and 100 m samples, reflecting the importance of contributions from local sources. Higher average concentrations in the 300 m samples compared to those in the 200 m samples may indicate contributions from long-range transport. The vertical profiles of source factors indicate the gasoline and road soil particle-associated PAHs, and the mixture from biogenic and fossil fuel source-associated n-alkanes were mostly from local emissions. The smaller average contribution of diesel-associated PAHs in the lower altitude samples also indicates that the restriction of diesel-fueled vehicle use in the central area of Tehran has been effective in reducing the PAHs concentration.

Hyperparathyroidism-jaw tumor syndrome (HPT-JT) is an autosomal dominant disease characterized by the occurrences of parathyroid tumors and ossifying fibroma of maxilla/mandible. It is caused by mutations in CDC73 gene and mutation carriers are at increased risk of parathyroid carcinoma. Hyperparathyroidism could be the sole manifestation. We reported two Chinese patients having parathyroid neoplasm with equivocal malignant potential and parathyroid carcinoma respectively with both germline and somatic CDC73 mutations detected. Both of them presented with severe hypercalcemia and primary hyperparathyroidism with no other HPT-JT associated tumors and negative family history. We identified one novel germline mutation CDC73 NM_024529.4: c.1475G > A; NP_078805.3: p.Trp492X and one novel somatic mutation CDC73 NM_024529.4: c.142G > T; NP_078805.3: p.Glu48X. The other germline mutation CDC73 NM_024529.4: c.226C > T; NP_078805.3: p.Arg76X and somatic mutation CDC73 NM_024529.4: c.85delG; NP_078805.3: p.Glu29SerfsX8 were previously reported. This is the first report of CDC73 mutations in the Chinese population. Genetic analysis is reliable to confirm the underlying hereditary basis of hyperparathyroidism. By identification of mutations, the patient and the family members could benefit from regular surveillance for early detection of tumors.