0.4491234 [3,] 3.455055 -0.2956536 0.4491234 [4,] 3.455055 -0.2956536 0.4491234 欧式距离是公式=sqrt(rowSums...((Data-均值矩阵)^2)) #各样本欧氏距离，每一行 x1=matrix(km$centers[1,], nrow = 940, ncol =3 , byrow = T) juli1=sqrt(rowSums...((Data-x1)^2)) x2=matrix(km$centers[2,], nrow = 940, ncol =3 , byrow = T) juli2=sqrt(rowSums((Data-x2...)^2)) x3=matrix(km$centers[3,], nrow = 940, ncol =3 , byrow = T) juli3=sqrt(rowSums((Data-x3)^2)) dist
首先是rowSums方法 这个是非常容易理解的，我在之前分享了：单细胞层面的表达量差异分析到底如何做，也是这样举例： 前面的 compSce是一个seurat对象 ，它里面的comp是表型是两个分组，然后...cbind,lapply(names(bs), function(x){ # x=names(bs)[[1]] kp =colnames(compSce) %in% bs[[x]] rowSums...lapply(levels(celltype), function(ct) { cells <- names(celltype)[celltype==ct] pseudobulk <- rowSums...label, data = meta_sub) mat_mm = GetAssayData(sc_sub, slot = 'counts') %*% mm keep_genes = rowSums...这个代码实在是太复杂了，我仅仅是节选部分给大家看看，因为它考虑到的各种因素非常多，但是本质上还是表达量矩阵的提取和加和，是rowSums方法。。。
row (feature) summaries rowSums(counts(tung)) # sum rowMeans(counts(tung)) # mean rowSds(counts(tung...cell_filter = 25000 # check一下 table(cell_filter) 10.2 过滤基因 gene_filter x # 对基因进行滤过，Total counts per gene greater than x....rowSums(counts(sce) > x) > y # 对基因进行过滤，Genes with at least y cells having counts greater than x. ---
4,1:4] 后续也可以对这样的TPM矩阵，进行一些过滤，一些归一化，可视化比如： 代码如下所示： # Removing genes that are not expressed table(rowSums...matrix with TRUEs and FALSEs head(keep.exprs) # Summary of how many TRUEs there are in each row table(rowSums...keep.exprs)) # we would like to keep genes that have at least 2 TRUES in each row of thresh # keep = round(dim(mat_tpm)[2]*0.1) # keep = 0 keep <- rowSums(keep.exprs
readerControl = list( language='UTF-8' ) ) #使用矩阵的方式计算 cosineDist <- function(x){ return(x%*%t(x)/(sqrt(rowSums...(x^2) %*% t(rowSums(x^2))))) } #字符串分隔函数 strsplits <- function(x, splits, ...) { for (split in splits
stopwordsCN() ) ) TF <- as.matrix(tdm) #使用矩阵的方式计算 cosineDist <- function(x){ return(x%*%t(x)/(sqrt(rowSums...(x^2) %*% t(rowSums(x^2))))) } cosSimilar <- cosineDist(TF) cosineDist <- function(textMatrix) {
require(gridExtra) # reorder 1 index <- order(rowSums(test), decreasing = TRUE) dend = reorder(as.dendrogram...pheatmap(test, cluster_rows = row_cluster, show_colnames = FALSE) # reorder 2 index <- order(rowSums
自己答案 class(VADeaths) dfv=as.data.frame(VADeaths) dfv$Total=rowSums(dfv) #rowsums是查找后得知的 #不会排序 标准答案 class...VADeaths) df <- as.data.frame(VADeaths) df$Total <- df[, 1] + df[, 2] + df[, 3] + df[, 4] df$Total <- rowSums...,'Infant.Mortality']=NA dfs2['Total',]=colSums(dfs2) newvariable=dfs2$Examination[1:(nrow(dfs2)-1)]/rowSums...分析 最后一个取比例，我自己是把简单的事情复杂化，因为想避开Total/Total这一项；另外Total examination可以直接用df"Total", "Examination"选取，没有必要用rowSums
show_col_types = F) %>% column_to_rownames(var = '#OTU ID') colSums(otu_dat) %>% sort() # 丰度最低的5个OTU rowSums...show_col_types = F) %>% column_to_rownames(var = '#OTU ID') colSums(rarefy_dat) %>% sort() # 丰度最低的5个OTU rowSums...rrarefy(t(otu_dat),50000) %>% t() %>% as.data.frame() colSums(vega_raryfy) %>% sort() # 丰度最低的5个OTU rowSums
other_cell_df_up <- up_df[target_cell_gene_up,setdiff(colnames(up_df),i)] other_cell_df_up$count_na <- rowSums...other_cell_df_down <- down_df[target_cell_gene_down,setdiff(colnames(down_df),i)] other_cell_df_down$count_na <- rowSums...show_rownames = F,cluster_rows = F, cluster_cols = F) 下调基因 然后是shared gene的可视化热图 up_df$count_na <- rowSums...(is.na(up_df)) up_shared_df <- up_df[up_df$count_na<(ncol(up_df)-2),] down_df$count_na <- rowSums(is.na
由于逻辑矩阵等效于 1 或 0 矩阵（1 = TRUE 和 0 = FALSE），我们可以使用 rowSums 函数在至少 2 个 Ch12 重复中提取峰。...ch12_HC_Peaks <- allPeaksSet_nR[rowSums(as.data.frame(mcols(allPeaksSet_nR)[, c("ch12_1", "ch12_2...ch12_HC_UniquePeaks <- allPeaksSet_nR[rowSums(as.data.frame(mcols(allPeaksSet_nR)[, c("ch12_1", "...ch12_2")])) >= 2 & rowSums(as.data.frame(mcols(allPeaksSet_nR)[, c("Mel_1", "Mel_2")])) == 0] export.bed
尽量转化为matrix格式 使用matrix dat = matrix(rnorm(1e4*1000),1e4,1000) dd = as.data.frame(dat) system.time( rowSums...(dd)) user system elapsed 0.05 0.00 0.05 使用data.frmae system.time( rowSums(dat)) user
, assays = "RNA") av=as.data.frame(av[[1]]) head(av) # 可以看到是整数矩阵 av=av[ rowSums..., assays = "RNA") av=as.data.frame(av[[1]]) head(av) # 可以看到是整数矩阵 av=av[ rowSums...), assays = "RNA") av=as.data.frame(av[[1]]) head(av) # 可以看到是整数矩阵 av=av[ rowSums
,c(2,3)] m[2,] m[,2] m[2]#第2行，第1列 m[-1,2] m["R1","C1"] 4.4 矩阵的运算 m+1#矩阵m中每一个元素都加1 colSums(m)#每一列的总和 rowSums...2.54) transform(women, cm = height*2.54)#新增一列，列名为cm worldphones <- as.data.frame(WorldPhones) rs <- rowSums...a)#测试向量a里面含元素NA吗 sum(a,na.rm = TRUE) mean(a,na.rm = TRUE)#按49个数来计算 colSums(is.na(sleep))#计算每一列缺失值数目 rowSums
加载R函数 source("corrr.R") df % column_to_rownames(var="id") %>% filter(rowSums...#788FCE", TRUE ~ "gray" ), width = abs(weight) * 2.5 ) 构建点数据 node_sizes % rowSums
dag3S.t <- t.data.frame(dag3S) dag3S.t.pa <- dag3S.t dag3S.t.pa[dag3S.t.pa > 0] <- 1 dag3S.t.pa.rs <- rowSums...dag3G.t <- t.data.frame(dag3G) dag3G.t.pa <- dag3G.t dag3G.t.pa[dag3G.t.pa > 0] <- 1 dag3G.t.pa.rs <- rowSums
nrow(cm) # number of classes diag = diag(cm) # number of correctly classified instances per class rowsums...) # number of instances per class colsums = apply(cm, 2, sum) # number of predictions per class p = rowsums...classes ## Accuracy accuracy = sum(diag) / n accuracy precision = diag / colsums recall = diag / rowsums
statistics_table <- otutable[, 0] statistics_table$total <- rowSums(otutable) # calculating spread...(number of presences (samples with 1+ read) pr OTU) statistics_table$spread ..., "\n"), file = log_con) } } #输出结果 close(log_con) total_abundances <- rowSums(otutable)
cell.type == i)) rna.counts.sub <- rna.counts[,colnames(rna.counts) %in% cells] rna.counts.bulk <- rowSums...tfsee.group2hist(diff.tfsee)colnames(diff.tfsee) <- "difference.tfsee"total <- diff.tfseetotal$sum <- rowSums...tfsee.group2hist(diff.tfsee)colnames(diff.tfsee) <- "difference.tfsee"total <- diff.tfseetotal$sum <- rowSums...tfsee.group2hist(diff.tfsee)colnames(diff.tfsee) <- "difference.tfsee"total <- diff.tfseetotal$sum <- rowSums...tfsee.group2hist(diff.tfsee)colnames(diff.tfsee) <- "difference.tfsee"total <- diff.tfseetotal$sum <- rowSums
<- unclass(cellInfo.tb[,table(meta.cluster,loc)])[,loc.avai.vec] freq.dist <- sweep(count.dist,1,rowSums...OR.dist.mtx) } } test.dist.table <- function(count.dist,min.rowSum=0) { count.dist =min.rowSum,,drop=F] sum.col <- colSums(count.dist) sum.row <- rowSums(count.dist)
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