6H,8H-3,4-dihydropyrimido(4,5-c)(1,2)oxazin-7-one

Calculation of the normalization factor NF for sample k based on the RQs of the reference genes p.

Applying a threshold on the p-value will identify barcodes that have count profiles that are significantly different from the ambient pool of RNA. We assume that this will be the case for most cell-containing droplets, as the ambient pool is formed from many (lysed) cells and is unlikely to be representative of any single cell. However, it is possible for some cell-containing droplets to have ambient-like expression profiles. This can occur if the cell population is highly homogeneous or if one cell subpopulation contributes disproportionately to the ambient pool, e.g., if it is more prone to lysis. Sequencing errors in the cell barcodes may also bias the estimates of the ambient proportions, by misassigning counts from cell-containing droplets to barcodes with low UMI totals. This may result in spurious similarities between cells and the estimated ambient profile.
To avoid incorrectly calling ambient-like cells as empty droplets, we combine our procedure with a conventional threshold on the total UMI count. We rank all barcodes in order of decreasing t_b, and consider log(t_b) as a function f(.) of the log-transformed rank, i.e., log(t_b)=f(logr_b) where r_b is the rank of b in the ordered sequence of barcodes. The first “knee” point in this function corresponds to a transition between a distinct subset of barcodes with large totals and the majority of barcodes with smaller totals. This is defined at the log-rank that minimizes the signed curvature
$\frac{f^{\mathrm{\prime \prime }}}{{(1+f^{\prime 2})}^{1.5}},$ and represents the point at which f(.) begins to drop rapidly, marking the start of the transition between large and small totals. In practice, we obtain f(.) by fitting a smooth spline to log(t_b) against the log-rank in the interval containing the knee point. The derivatives of f(.) are then obtained by differentiation of the spline basis functions. This avoids multiplication of errors during numerical differentiation, which would lead to instability in the curvature values and inaccurate estimates of the knee point.
Our assumption is that any barcode with a large total count must represent a cell-containing droplet, regardless of whether its count profile resembles the ambient pool. This is based on the expectation that the distribution of the sizes of empty droplets should be unimodal, with a monotonic decreasing probability density as t_b increases past the mode. A distinct peak of large totals would not be consistent with this expected distribution. We define the upper threshold U as the t_b at the knee point and retain all barcodes with t_b≥U, irrespective of their P_b. This guarantees recovery of any barcodes with large total counts that potentially represent cell-containing droplets. We use the knee point rather than the inflection point as the t_b of the former is larger, providing a more conservative threshold that avoids retention of empty droplets.
We stress that, despite the use of a threshold on t_b, our approach is different from existing methods due to the testing procedure. Barcodes with t_b below the knee point can still be retained if the count profile is significantly different from the ambient pool. This is not possible with existing methods that would simply discard these barcodes. Users can also set U manually if automatic detection of the knee point fails for complex f(.). Alternatively, this mechanism can be disabled completely in favor of detecting cells solely based on their p-values. This is more statistically rigorous as it avoids the selection of an ad hoc threshold, but may result in the failure to detect large cells.

FUMA uses input GWAS summary statistics to compute gene-based P-values (gene analysis) and gene set P-value (gene set analysis) using the MAGMA^{35 (link)} tool. For gene analysis, the gene-based P-value is computed for protein-coding genes by mapping SNPs to genes if SNPs are located within the genes. For gene set analysis, the gene set P-value is computed using the gene-based P-value for 4728 curated gene sets (including canonical pathways) and 6166 GO terms obtained from MsigDB v5.2. For both analyses, the default MAGMA setting (SNP-wise model for gene analysis and competitive model for gene set analysis) are used, and the Bonferroni correction (gene) or FDR (gene-set) was used to correct for multiple testing. 1000G phase 3^{27 (link)} is used as a reference panel to calculate LD across SNPs and genes.

Raw reads were checked for quality standards using FastQC (v. 0.11.9) (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and only high-quality read pairs (base score above Q30) were subject to downstream processing. Read pairs were aligned to the L. sativa cv. Salinas RefSeq genome assembly version 7 (genome ID: 5962908) or S. lycopersicum cv. Heinz 1706 RefSeq genome assembly (RefSeq GCF_000188115.4) using HISAT2 (v. 2.2.1) with default parameter settings (Kim et al., 2015 (link)). Genes with multiple copies undifferentiated in the genome annotation were assigned numbers in the order they are referred to in the text (e.g., LOC111908039 as HY5-1). Mapped reads were assigned to genomic features based on Lsat_Salinas_v7 or S. lycopersicum RefSeq assembly annotations using featureCounts (v. 2.0.1) (Liao et al., 2014 (link)). Read counts were summarized at the gene level and zero-count genes were removed prior to further analysis. Raw data and counts have been deposited in NCBI’s Gene Expression Omnibus (Edgar et al., 2002 (link)) and are accessible through GEO Series accession numbers GSE180179 and GSE200978 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180179; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE200978).
Differential expression analysis was performed independently for each species in R using the edgeR package (v. 3.34.0) (Robinson et al., 2010 (link); McCarthy et al., 2012 (link)). The estimateGLMCommonDisp function was used to estimate a common gene-wise dispersion parameter suitable for all genes and evaluated on an individual basis and likelihood ratio tests were performed to test for differential expression of genes within pairwise treatment groups. For each test, a single treatment (OSC filter) group was compared to the control (clear or shaded glass) treatment and significance was evaluated based on the Benjamini Hochberg adjusted p-value (threshold of FDR<0.05). A second round of analysis was performed by comparing each treatment with the corresponding treatment with variable light intensity.

The data entry was performed using Epi-Data version 4.6 and then exported into STATA 16 for analysis. Descriptive statistics like mean, median, frequency, and percentage were used to present variables using texts, tables, and graphs. A p-value of ≤ 0.05 was used as statistically significant. To identify the determinants of nosocomial infection, Cox proportional hazard model was used. To identify statistically significant variables, Cox regression (Bivariable and multivariable) was performed using a p value ≤ 0.2 in the univariable Cox regression analysis to identify candidate variables for multivariable Cox regression. To declare statistically significant variables, an adjusted hazard ratio with 95% CI was used, based on p value < 0.05 in the multivariable Cox regression analysis. The goodness of fit of the model was tested by using the Cox-Snell residuals together with Nelson Aalen's cumulative hazard function.

Three biological replicate samples were collected from the aerial portions of WT and BR1OE mutant plants 25 days post-planting. After snap freezing using liquid nitrogen, these samples were stored at −80°C. RNA extraction was performed as detailed previously (Jiang et al., 2018 (link)), and 1% agarose gel electrophoresis was used to detect any RNA contamination or degradation while a NanoPhotometer^® instrument (IMPLEN, CA, USA) was used to confirm RNA purity. A Qubit^® RNA Assay Kit and a Qubit^® 2.0 Fluorometer (Life Technologies, CA, USA) were used to quantify the RNA concentrations in individual samples, while an RNA Nano 6000 Assay Kit and a Bioanalyzer 2100 instrument (Agilent Technologies, CA, USA) were used to confirm RNA integrity. Sequencing libraries were prepared from 1 μg of RNA per sample with a NEBNext^® UltraTM RNA Library Prep Kit for Illumina^® (NEB, USA) based on provided directions. Library sequencing was then performed with an Illumina Hiseq platform to generate 125 bp/150 bp paired-end reads.
Initial data were filtered with Fastp (v0.19.3) to remove adapter-containing reads, reads containing > 10% N bases, and reads with > 50% low-quality (Q ≤ 20) bases. The clean reads were then compared to the Arabidopsis TAIR10 genome which was downloaded from The Arabidopsis Information Resource (TAIR) (https://www.arabidopsis.org/) using HISAT (v2.1.0). New gene predictions were made using StringTie (v1.3.4d), while gene alignment was calculated with FeatureCounts (v1.6.2), and fragments per kilobases of exons per million mapped reads (FPKM) expression values were then calculated for all transcripts. Differentially expressed genes (DEGs) were identified using DESeq2 (v1.22.1) based on Benjamini & Hochberg-corrected p-values, a |log2Fold Change| ≥ 1, and a false discovery rate (FDR) < 0.05. Hypergeometric tests were used for Gene Ontology (GO) term and KEGG pathway enrichment analyses. Gene expression was validated using primer pairs listed in Supplementary Table S1.