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Merge pull request #144 from MacoskoLab/new_functions
Adding several new functionalities
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,7 @@ | ||
| Package: liger | ||
| Type: Package | ||
| Title: Linked Inference of Genomic Experimental Relationships | ||
| Version: 0.4.2 | ||
| Version: 0.5.0.9000 | ||
| Date: 2018-04-09 | ||
| Author: Joshua Welch | ||
| Maintainer: Joshua Welch <[email protected]>, | ||
|
|
@@ -15,7 +15,6 @@ Imports: Rcpp (>= 0.12.10), | |
| cowplot, | ||
| FNN, | ||
| dplyr, | ||
| RANN.L1, | ||
| grid, | ||
| ggrepel, | ||
| irlba, | ||
|
|
@@ -29,21 +28,32 @@ Imports: Rcpp (>= 0.12.10), | |
| doSNOW, | ||
| mclust, | ||
| patchwork, | ||
| stats | ||
| Remotes: thomasp85/patchwork | ||
| LinkingTo: Rcpp, RcppArmadillo | ||
| stats, | ||
| psych, | ||
| RcppEigen, | ||
| RANN | ||
| Remotes: thomasp85/patchwork, | ||
| jefferis/RANN@master-L1, | ||
| linxihui/NNLM | ||
| biocViews: | ||
| LazyData: true | ||
| LinkingTo: Rcpp, RcppArmadillo, RcppEigen | ||
| Depends: | ||
| cowplot, | ||
| Matrix, | ||
| methods, | ||
| patchwork | ||
| RoxygenNote: 6.1.1 | ||
| RoxygenNote: 7.0.2 | ||
| Encoding: UTF-8 | ||
| Suggests: | ||
| Seurat, | ||
| knitr, | ||
| reticulate, | ||
| rmarkdown, | ||
| NNLM, | ||
| testthat | ||
| testthat, | ||
| GenomicRanges, | ||
| S4Vectors, | ||
| IRanges, | ||
| RANN.L1 | ||
| VignetteBuilder: knitr | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -4,97 +4,6 @@ NULL | |
| # These are deprecated functions likely to be removed in future versions. | ||
| # Documentation for these functions is incomplete. | ||
|
|
||
| #' Quantile normalizes cell factors | ||
| #' | ||
| #' @param object analogizer object. Should run optimizeALS before calling. | ||
| #' @param quantiles Number of quantiles to use for quantile normalization | ||
| #' @param ref_dataset Name of dataset to use as a "reference" for normalization. By default, | ||
| #' the dataset with the largest number of cells is used. | ||
| #' | ||
| #' @return analogizer object | ||
| #' @importFrom FNN get.knn | ||
| #' @export | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' Y = matrix(c(1,2,3,4,5,6,7,8,9,10,11,12),nrow=4,byrow=T) | ||
| #' Z = matrix(c(1,2,3,4,5,6,7,6,5,4,3,2),nrow=4,byrow=T) | ||
| #' analogy = Analogizer(list(Y,Z)) | ||
| #' [email protected] = c(1,2,3,4) | ||
| #' analogy = scaleNotCenter(analogy) | ||
| #' } | ||
| quantile_norm = function(object,quantiles=50,ref_dataset=NULL,min_cells=2) | ||
| { | ||
| if (is.null(ref_dataset)) | ||
| { | ||
| ns = sapply(object@scale.data,nrow) | ||
| ref_dataset = names(object@scale.data)[which.max(ns)] | ||
| } | ||
|
|
||
| Hs_scaled = object@H | ||
| for (i in 1:ncol(Hs_scaled[[1]])) | ||
| { | ||
| for (j in 1:length(Hs_scaled)){ | ||
| #Hs_scaled[[j]][,i] = object@H[[j]][,i]/sqrt(sum(object@H[[j]][,i]^2)) | ||
| Hs_scaled[[j]] = scale(Hs_scaled[[j]],scale=T,center=T) | ||
| } | ||
| } | ||
| labels = list() | ||
| for(i in 1:length(Hs_scaled)){ | ||
| #knn_k=15 | ||
| #knn = get.knn(object@H[[i]],knn_k) | ||
| pct1 = apply(object@H[[i]],2,sum)/sum(apply(object@H[[i]],2,sum)) | ||
| pct2 = apply(object@H[[ref_dataset]],2,sum)/sum(apply(object@H[[ref_dataset]],2,sum)) | ||
| if (names(object@H)[i]==ref_dataset) | ||
| { | ||
| pct1 = apply(object@H[[i]],2,sum)/sum(apply(object@H[[i]],2,sum)) | ||
| pct2 = apply(object@H[[2]],2,sum)/sum(apply(object@H[[2]],2,sum)) | ||
| } | ||
| use_these_factors = 1:ncol(object@H[[i]])#which(log(pct1/pct2) > -2) | ||
|
|
||
| labels[[i]] = as.factor(use_these_factors[apply(Hs_scaled[[i]][,use_these_factors],1,which.max)]) | ||
| #labels[[i]] = as.factor(t(apply(knn$nn.index,1,function(x){which.max(table(labels[[i]][x]))}))[1,]) | ||
| } | ||
|
|
||
| object@clusters = as.factor(unlist(lapply(labels,as.character))) | ||
| names(object@clusters)=unlist(lapply(object@scale.data,rownames)) | ||
|
|
||
| clusters = labels | ||
| names(clusters)=names(object@H) | ||
| dims = ncol(object@H[[ref_dataset]]) | ||
|
|
||
| Hs = object@H | ||
| num_clusters = dims | ||
| for (k in 1:length(Hs)) | ||
| { | ||
| for (i in 1:dims) | ||
| { | ||
| for (j in 1:num_clusters) | ||
| { | ||
| if (sum(clusters[[ref_dataset]]==j) < min_cells | sum(clusters[[k]]==j) < min_cells){next} | ||
| if (sum(clusters[[k]]==j)==1){ | ||
| Hs[[k]][clusters[[k]]==j,i] = mean(Hs[[ref_dataset]][clusters[[ref_dataset]]==j,i]) | ||
| next | ||
| } | ||
| q2 = quantile(Hs[[k]][clusters[[k]]==j,i],seq(0,1,by=1/quantiles)) | ||
| q1 = quantile(Hs[[ref_dataset]][clusters[[ref_dataset]]==j,i],seq(0,1,by=1/quantiles)) | ||
| if (sum(q1)==0 | sum(q2)==0 | length(unique(q1)) < 2 | length(unique(q2)) < 2) | ||
| { | ||
| new_vals = rep(0,sum(clusters[[k]]==j)) | ||
| } | ||
| else | ||
| { | ||
| warp_func = approxfun(q2,q1) | ||
| new_vals = warp_func(Hs[[k]][clusters[[k]]==j,i]) | ||
| } | ||
|
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| Hs[[k]][clusters[[k]]==j,i] = new_vals | ||
| } | ||
| } | ||
| } | ||
| object@H.norm = Reduce(rbind,Hs) | ||
| return(object) | ||
| } | ||
|
|
||
| #' Calculate alignment metric per factor. | ||
| #' | ||
| #' @param object Analogizer object. Should run quantile_align_SNF before calling. | ||
|
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