Time-varying TAD boundary analysis
TimeCompare( cont_mats, resolution, z_thresh = 2, window_size = 15, gap_thresh = 0.2, groupings = NULL )
| 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 n 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 more stringent detection of 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 |
| groupings | Variable for defining groups of replicates at a given time point. Each group will be combined using consensus boundary scores. It should be a vector of equal length to cont_mats where each entry is a label corresponding to the group membership of the corresponding matrix. Default is NULL, implying one matrix per time point. |
A list containing consensus TAD boundaries and overall scores
TAD_Bounds - Data frame containing all regions with a TAD boundary at one or more time point. Coordinate corresponds to genomic region, sample columns correspond to individual boundary scores for each sample, Consensus_Score is the consensus boundary score across all samples. Category is the differential boundary type.
All_Bounds - Data frame containing consensus scores for all regions
Count_Plot - Plot containing the prevelance of each boundary type
Given a list of sparse 3 column, n x n, or n x (n+3) contact matrices representing different time points, TimeCompare identifies all TAD boundaries. Each TAD boundary is classified into six categories (Common, Dynamic, Early/Late Appearing and Early/Late Disappearing), based on how it changes over time.
# Read in data data("time_mats") # Find time varying TAD boundaries diff_list <- TimeCompare(time_mats, resolution = 50000)#>#>#>#>#>