filterCounts(tweeDEseq)
filterCounts()所属R语言包:tweeDEseq
Count data filtering
统计数据过滤
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Filter count data to remove lowly expressed genes.
过滤计算数据,以消除低表达的基因。
用法----------Usage----------
filterCounts(counts, cpm.cutoff=0.5, n.samples.cutoff=2)
参数----------Arguments----------
参数:counts
numeric data.frame or matrix containing the count data.
数值数据框或矩阵包含计数数据。
参数:cpm.cutoff
expression level cutoff defined as the minimum number of counts per million. By default this is set to 0.5 counts per million.
表达水平的截止定义为每百万计数的最低数量。默认情况下,这是数以百万计的0.5%。
参数:n.samples.cutoff
minimum number of samples where a gene should meet the counts per million cutoff (cpm.cutoff) in order to be kept as part of the count data matrix.
最低样本数的基因应该满足每百万截止计数(cpm.cutoff)以保持计数数据矩阵的一部分。
Details
详情----------Details----------
This function removes genes with very low expression level defined in terms of a minimum number of coutns per million occurring in a minimum number of samples. Such a policy was described by Davis McCarthy in a message at the bioc-sig-sequencing mailing list. By default, this function keeps genes that are expressed at a level of 0.5 counts per million or greater in at least two samples.
此功能删除定义最低的每百万coutns发生在最低数量的样本数量非常低表达水平的基因。这样的政策被描述在消息bioc-SIG测序邮件列表由戴维斯麦卡锡。此功能默认情况下,保持在0.5每百万计数或至少在两个样品的水平基因表达。
值----------Value----------
A matrix of filtered genes.
一个基因的筛选矩阵。
作者(S)----------Author(s)----------
J.R. Gonzalez and R. Castelo
参考文献----------References----------
参见----------See Also----------
normalizeCounts
normalizeCounts
举例----------Examples----------
# Generate a random matrix of counts[生成一个随机数矩阵]
counts <- matrix(rPT(n=1000, a=0.5, mu=10, D=5), ncol = 40)
dim(counts)
# Filter genes with requiring the minimum expression level on every sample[要求对每一个样本的最小表达水平的筛选基因]
filteredCounts <- filterCounts(counts, n.samples.cutoff=dim(counts)[2])
dim(filteredCounts)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
注1:为了方便大家学习,本文档为生物统计家园网机器人LoveR翻译而成,仅供个人R语言学习参考使用,生物统计家园保留版权。
注2:由于是机器人自动翻译,难免有不准确之处,使用时仔细对照中、英文内容进行反复理解,可以帮助R语言的学习。
注3:如遇到不准确之处,请在本贴的后面进行回帖,我们会逐渐进行修订。
|