FL , Full-length reads, 全长转录本。从raw data 到 ROI , 在从ROI 去除 artifacts reads 之后,我们就得到了用于后续分析的clean reads。clean reads 就已经是转录本的序列了,我们首先看一下clean reads 当中,哪些是全长转录本;哪些不是全长转录本。 全长转录本的示意图,如图8: 图8. 全长转录本的定义示...
FL , Full-length reads, 全长转录本。从raw data 到 ROI , 在从ROI 去除 artifacts reads 之后,我们就得到了用于后续分析的clean reads。clean reads 就已经是转录本的序列了,我们首先看一下clean reads 当中,哪些是全长转录本;哪些不是全长转录本。 全长转录本的示意图,如图8: 图8. 全长转录本的定义示...
seq_inputs_length) # attention_mechanism = tf.contrib.seq2seq.LuongAttention(num_units=config.hidden_dim, memory=encoder_outputs, memory_sequence_length=self.seq_inputs_length) decoder_cell = tf.contrib.seq2seq.AttentionWrapper(decoder_cell, attention_mechanism) decoder_initial_state = decoder_...
宁可调高阈值（比如p.adj < 0.2），也不要用raw p。因为组学数据分析存在多重假设检验问题（不懂的...
# 源语言输入文件SRC_TRAIN_DATA = root_path +"en.number"# 目标语言输入文件TRG_TRAIN_DATA = root_path +"zh.number"# checkpoint保存路径CHECKPOINT_PATH = root_path +"seq2seq_ckpt"# LSTM的隐藏层规模HIDDEN_SIZE =1024# 深层循环神经网络中LSTM结构的层数NUM_LAYERS =2# 源语言词汇表大小SRC_VOCAB...
也称数据清洗,在fastQC结果不好的情况下进行去接头处理。我们的试验数据集质量挺好的,所以此步略过,具体操作参见博客http://blog.csdn.net/lixiangyong123/article/details/52062323?locationNum=13&fps=1 6使用Hisat2对Reads进行Mapping ⑴下载人类参考基因组和...
= ' ': out += ' ' out += char return out with open('/home/kesci/input/fraeng6506/fra.txt', 'r') as f: raw_text = f.read() text = preprocess_raw(raw_text) # Tokenize source, target = [], [] for i, line in enumerate(text.split('\n')): if i >= num_examples: ...
# cells from 2+ cell embryoshave_duplications<-ct$num_cells>1# cells with values <= 28normal_vals<- apply(exprs(ct), 2,function(smp)all(smp<=28)) 使用这两个过滤指标组合来排除非分裂细胞和含有高于基线的Ct值,并将结果存储为cleaned_...
data feature. See the PBMC dataset tutorial for an example of how to generate the Scanpy object from the data provided by 10X. Because Scanpy uses sparse matrices by default, the .h5ad data structure can take up much less memory than the raw counts matrix and can be much faster to load...
data feature. See the PBMC dataset tutorial for an example of how to generate the Scanpy object from the data provided by 10X. Because Scanpy uses sparse matrices by default, the .h5ad data structure can take up much less memory than the raw counts matrix and can be much faster to load...