| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T432 |
0-181 |
Sentence |
denotes |
To ensure that each cell was both adequately sequenced and had a high signal-to-background ratio, we filtered cells with less than 1,000 unique fragments and enrichment at TSSs < 8. |
| T433 |
182-330 |
Sentence |
denotes |
To calculate TSS enrichment > 2, genome-wide Tn5-corrected insertions were aggregated ± 2,000 bp relative (TSS-strand-corrected) to each unique TSS. |
| T434 |
331-504 |
Sentence |
denotes |
This profile was normalized to the mean accessibility ± 1,900–2,000 bp from the TSS, smoothed every 51 bp and the maximum smoothed value was reported as TSS enrichment in R. |
| T435 |
505-635 |
Sentence |
denotes |
To construct a counts matrix for each cell by each feature (peaks), we read each fragment.tsv.gz fill into a GenomicRanges object. |
| T436 |
636-805 |
Sentence |
denotes |
For each Tn5 insertion, which can be thought of as the “start” and “end” of the ATAC fragments, we used findOverlaps to find all overlaps with the feature by insertions. |
| T437 |
806-932 |
Sentence |
denotes |
Then we added a column with the unique id (integer) cell barcode to the overlaps object and fed this into a sparseMatrix in R. |
| T438 |
933-1114 |
Sentence |
denotes |
To calculate the fraction of reads/insertions in peaks, we used the colSums of the sparseMatrix and divided it by the number of insertions for each cell id barcode using table in R. |
