Consensus boundary identification
ConsensusTADs( cont_mats, resolution, z_thresh = 3, window_size = 15, gap_thresh = 0.2 )
| cont_mats | List of contact matrices in either sparse 3 column, n x n or n x (n+3) form where the first three columns are coordinates in BED format. See "Input_Data" vignette for more information. If an x n matrix is used, the column names must correspond to the start point of the corresponding bin. Required. |
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| resolution | Resolution of the data. Used to assign TAD boundaries to genomic regions. If not provided, resolution will be estimated from column names of the first matrix. Default is "auto" |
| z_thresh | Threshold for boundary score. Higher values result in a higher threshold for differential TADs. Default is 3. |
| window_size | Size of sliding window for TAD detection, measured in bins. Results should be consistent Default is 15. |
| gap_thresh | Required % of non-zero entries before a region will be considered non-informative and excluded. Default is .2 |
A list containing consensus TAD boundaries and overall scores
Consensus - Data frame containing location of all consensus boundaries. Coordinate is the region of the genome, Sample columns correspond to individual boundary scores. Consensus_Score is consensus boundary score.
All_Regions - Data frame containing consensus scores for all regions. All columns are identiical to the Consensus object.
Given a list of sparse 3 column, n x n , or n x (n+3) contact matrices, ConsensusTADs provides the set of consensus TAD boundaries across them. Consensus TADs are defined by the consensus boundary score, a score measuring TAD boundary likelihood across all matrices.
# Read in data data("time_mats") # Find consensus TAD boundaries diff_list <- ConsensusTADs(time_mats, resolution = 50000)#>#>#>#>#>