我借助python中的CAI模块实现 github.com/Benjamin-Lee 使用pip直接安装 pip install CAI 计算RSCU值 from CAI import RSCU from Bio import SeqIO seqs = [rec.seq for rec in SeqIO.parse('codon_usage_example.fasta','fasta')] rscu = RSCU(seqs) rscu是一个字典,密码子是键,对应的RSCU是值 写一...
研究密码子偏向性的论文通常都会分析RSCU值,论文中通常会用堆积柱形图来展示RSCU的值,之前在论文里也看到过下面这幅图的形式展示RSCU分析的结果
ax = dfp.plot(kind="barh", stacked=True, ax=ax) for c in ax.containers: # customize the label to account for cases when there might not be a bar section labels = [f'{w*100:.0f}%' if (w := v.get_width()) > 0 else '' for v in c ] # set the bar label ax.bar_la...
首先是计算RSCU值 我借助python中的CAI模块实现 使用pip直接安装 AI检测代码解析 pip install CAI 1. 计算RSCU值 AI检测代码解析 from CAI import RSCU from Bio import SeqIO seqs = [rec.seq for rec in SeqIO.parse('codon_usage_example.fasta','fasta')] rscu = R...
(UMAP based on harmony batch correction in this example).(b) Dual-gene plothighlights cells express SYT1 and GAD1 (green SYT1 only, red GAD1 only, yellow co-expression of STY1 and GAD1), expression cutoff 2.2.(c) Stacked barplotdemonstrates the fraction of each major cell type across ...
Stacked and grouped bar chart with ggplot in rm<-melt(housing, id.vars = "household", measure.vars = c("ELI", "VLI","LI","MI")) m$household <- factor(m$household, levels = c("Extremely low income", "Very low income", "Low income", "Middle income")) ggplot(data = m, aes...
我借助python中的CAI模块实现 https://github.com/Benjamin-Lee/CodonAdaptationIndex 使用pip直接安装 pip install CAI 计算RSCU值 fromCAIimportRSCUfromBioimportSeqIO seqs=[rec.seqforrecinSeqIO.parse('codon_usage_example.fasta','fasta')]rscu=RSCU(seqs) ...