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MIT License 57 | 58 | Copyright (c) 2021 Buenrostro Lab 59 | 60 | Permission is hereby granted, free of charge, to any person obtaining a copy 61 | of this software and associated documentation files (the "Software"), to deal 62 | in the Software without restriction, including without limitation the rights 63 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 64 | copies of the Software, and to permit persons to whom the Software is 65 | furnished to do so, subject to the following conditions: 66 | 67 | The above copyright notice and this permission notice shall be included in all 68 | copies or substantial portions of the Software. 69 | 70 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 71 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 72 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 73 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 74 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 75 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 76 | SOFTWARE. 77 |78 | 79 |
Vinay Kartha. Author, maintainer. 60 |
61 |Jason D. Buenrostro. Author. 64 |
65 |Source: inst/CITATION
Kartha, VK. et al. Functional Inference of Gene Regulation using Single-Cell Multi-Omics. BioRxiv (2021)
73 |@Article{,
74 | title = {Functional Inference of Gene Regulation using Single-Cell Multi-Omics},
75 | author = {Vinay Kartha et al.},
76 | journal = {BiorXiv preprint},
77 | year = {2021},
78 | url = {https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1},
79 | }
80 | Function to center raw read counts for scATAC-seq data based on mean reads in features/rows (e.g. peaks) per cell
59 |Either a RangedSummarizedExperiment-class or dgeMatrix-class object of the single-cell ATAC data (peaks x cells) to center raw data for
boolean value whether or not to score cells in chunks (useful for large scATAC datasets of > 5,000 cells). If TRUE, cells are centered sequentially in chunks of size=1000 cells at a time to avoid memory limitations, and eventually merged (column-wise). Overriden and set to TRUE if > 10,000 cells in dataset
numeric specifying the number of cells to perform centering for at once, if running in chunks (to save memory). Default is 1000
Either a RangedSummarizedExperiment-class or dgeMatrix-class object with counts centered by mean reads in peaks per cell
This function fetches TF names from full motif IDs (see https://github.com/GreenleafLab/chromVARmotifs)
59 |vector of full motif IDs that are part of the original human or mouse pwms matrix (see https://github.com/GreenleafLab/chromVARmotifs)
R/DORCs.R
54 | getDORCScores.RdFunction to compute single cell DORC scores for each gene identified as a DORC (see runGenePeakcorr for determining DORCs through peak-gene correlations)
getDORCScores(
64 | ATAC.se,
65 | dorcTab,
66 | normalizeATACmat = TRUE,
67 | geneList = NULL,
68 | nCores = 4
69 | )SummarizedExperiment object of the scATAC-seq reads in peak counts.
data.frame object containing significant peak-gene pairs using which DORC scores will be computed. Must be a filtered set returned from runGenePeakcorr. IMPORTANT: Make sure the exact same scATAC SE (peak set) was used when determining DORCs that is used here to get corresponding DORC peak counts
boolean indicating whether or not to normalize the counts present in the ATAC.se object prior to summing peak counts per gene. Default is TRUE (i.e. assumes peak counts are raw).
character vector specifying a subset of genes to compute scores for. Useful if you already have a set list of DORCs and you only want to compute scores for those genes/peaks.
numeric indicating the number of cores to use if parallelizing tasks
Wrapper function to assess a specific set of peaks for enrichment with respect to TF binding motifs using a Z test after adjusting for GC bias and Tn5 accessibility
59 |vector of peak indices you wish to test for motif enrichment
background peaks matrix returned by getBackgroundPeaks function in chromVAR
binary match sparseMatrix object of (reference) peaks by TF motifs returned by matchMotifs function
Wrapper function for running ATAC/RNA pairing in cell chunks using co-embedding components
59 |combined co-embedding components matrix of the ATAC cells (e.g. CCA). Must have valid ATAC cell barcode names as the rownames.
same as `ATAC`, but for RNA cells. Must have valid RNA cell barcode names as the rownames (unique from ATAC cell barcodes), and have the same number of components (columns) as the `ATAC` matrix
boolean indicating whether or not to remove any resulting many-to-many ATAC-RNA paired cells returned by the geodesic pairing method (depending on what the `max_multimatch` parameter is set to in FigR[cell_pairing], which defaults to 5). Default is FALSE, meaning in the returned pairing, there can a single cell in the larger dataset paired to upto `max_multimatch` cells in the smaller dataset. See FigR[cell_pairing] for more details. If set to TRUE, we further filter pairs and return for each cell in the larger dataset the best match in the smaller dataset (among the multiple options, if any) based on the min euclidean distances between pair options (in the supplied reduced dimension space). Useful for RNA to ATAC cell cluster label transfer (since each ATAC cell can only acquire a single cluster identity based on the best RNA cell match), but not required for downstream analyses (peak-gene correlations and TF-DORC associations)
additional parameters passed to cell_pairing. Useful parameters for enabling a search solution include `search_range` and `min_subgraph_size`, which dictates how cells are potentially excluded from being paired
Scatter plot visualization of filtered TF-DORC associations based on the enrichment of each motif among the queried DORC's peaks and the correlation of the TF RNA to the DORC accessibility score
59 |data.frame of results returned by runFigRGRN)
character specifying a valid DORC gene to restrict TF drivers for
numeric specifying the absolute regulation score to threshold TF-DORC connections on. Default is 1
boolean indicating whether or not to add text labels for TF drivers passing score filter
Function to plot ATAC-RNA pairs in a joint embedding space (.e.g 2D UMAP of both ATAC and RNA cells)
59 |character vector of the ATAC barcodes returned in the resulting pairing data frame returned by the cell_pairing function
character vector of the RNA barcodes returned in the resulting pairing data frame returned by the cell_pairing function
data.frame that has the ATAC/RNA co-embedding UMAP 1/2 coordinates in the first and second columns, respectively. Must have valid ATAC and RNA barcodes as row names, which will be used to subset pairs
numeric specifying the maximum number of pairs to limit plotting to. Default is 300 (choose fewer for better visibility if the UMAP is crowded)
numeric specifying a random seed to set for sampling pairs (for reproducibility). Default is 123
scatter plot point size. Default is 1
Heatmap visualization of TF-DORC associations based on the regulation scores inferred by FigR
59 |data.frame of results returned by runFigRGRN).
numeric specifying the absolute regulation score to threshold TF-DORC connections on. Default is 1
character specifying valid DORC gene symbols to subset heatmap to. Default is NULL (no subsetting)
character specifying valid TF gene symbols to subset heatmap to. Default is NULL (no subsetting)
additional parameters passed to the Heatmap)
a TF-DORC filtered Heatmap generatd using Heatmap)
Network visualization of TF-DORC associations based on the regulation scores inferred by FigR
59 |plotfigRNetwork(
64 | figR.d,
65 | score.cut = 1,
66 | DORCs = NULL,
67 | TFs = NULL,
68 | weight.edges = FALSE
69 | )data.frame of results returned by runFigRGRN)
numeric specifying the absolute regulation score to threshold TF-DORC connections on. Default is 1
character specifying valid DORC gene symbols to subset heatmap to. Default is NULL (no subsetting)
character specifying valid TF gene symbols to subset heatmap to. Default is NULL (no subsetting)
boolean specifying whether or not to weight edges by FigR regulation score. Default is FALSE
Ranked plot of TF activators and repressors based on their inferred regulation score
59 |rankDrivers(
64 | figR.d,
65 | rankBy = c("meanScore", "nTargets"),
66 | myLabels = NULL,
67 | score.cut = NULL,
68 | interactive = FALSE
69 | )data.frame of results returned by runFigRGRN)
character specifying one of "meanScore" or "nTargets" to either rank TFs by the mean regulation score across all genes, or by the total number of inferred activated or repressed targets passing a specified (absolute) regulation score, respectively
character vector specifying the subset of TFs to highlight on the plot, if rankBy is set to "meanScore". Useful if you want to see where your TFs of interest lie. If NULL (Default), we label the top and bottom 95 percentile TFs
numeric specifying the absolute regulation score to threshold TF-DORC connections on, only if rankBy is set to "nTargets". Default is 1 if "nTargets" and no custom cut-off is specified
boolean indicating whether or not to allow interactive hover-over utility for more label information (useful if visualizing too many TFs and labels are hard to distinguish). Default is FALSE