# Extract the singlets pbmc.singlet <- subset(pbmc.hashtag, idents = "<em>Singlet</em>") # Select the top 1000...most variable features pbmc.singlet <- FindVariableFeatures(pbmc.singlet, selection.method = "mean.var.plot...(pbmc.singlet, features = VariableFeatures(pbmc.singlet)) # Run PCA pbmc.singlet <- RunPCA(pbmc.singlet...on PCElbowPlot pbmc.singlet <- FindNeighbors(pbmc.singlet, reduction = "pca", dims = 1:10) pbmc.singlet...<- FindClusters(pbmc.singlet, resolution = 0.6, verbose = FALSE) pbmc.singlet <- RunTSNE(pbmc.singlet
here for efficiency pbmc.singlet <- ScaleData(pbmc.singlet, features = VariableFeatures(pbmc.singlet...)) # Run PCA pbmc.singlet <- RunPCA(pbmc.singlet, features = VariableFeatures(pbmc.singlet)) # We select..., reduction = "pca", dims = 1:10) pbmc.singlet <- FindClusters(pbmc.singlet, resolution = 0.6, verbose...= FALSE) # pbmc.singlet <- RunTSNE(pbmc.singlet, reduction = "pca", dims = 1:10) pbmc.singlet <- RunUMAP...pbmc.singlet@misc$mk <- FindAllMarkers(pbmc.singlet,only.pos=T) Calculating cluster 0 |+++++++++++
计算实例见笔者在计算化学公社论坛上的回帖 http://bbs.keinsci.com/thread-29762-1-1.html 这里我们假设已经获得了.mdci.nat文件,接着执行 mv O_singlet_o.mdci.nat...O_singlet_CCSD.gbw # 实际上是个gbw文件 orca_2mkl O_singlet_CCSD -mkl # 产生mkl mkl2fch...O_singlet_CCSD.mkl O_singlet_CCSD.fch -nso # 产生fch文件 参数-nso意为将alpha、beta自旋自然轨道占据数写入生成的fch文件。...为获得UCCSD自然轨道,可启动Python,运行 from mokit.lib.lo import gen_no_from_nso gen_no_from_nso(fchname='O_singlet_CCSD.fch...') 产生UCCSD自然轨道文件O_singlet_CCSD_NO.fch,只含一套轨道。
4: 5: #include 6: 7: template 8: class SingleT...50: static T * p; 51: }; 52: 53: template 54: T * SingleT...Singleton.h" 6: 7: #include 8: 9: class SingletonTest : public SingleT
seu_kidney@meta.data$DF_hi.lo == "Doublet" & seu_kidney@meta.data$DF.classifications_0.25_0.09_473 == "Singlet...] <- "Doublet_hi" TSNEPlot(seu_kidney, group.by="DF_hi.lo", plot.order=c("Doublet_hi","Doublet_lo","Singlet
输入文件如下: %chk=BODIPY.chk #p pbe1pbe/6-31+G* td geom=allcheck guess=read 输出如下: Excited State 1: Singlet-A...chk=BODIPY.chk #p pbe1pbe/6-31+G* scrf=pcm geom=allcheck guess=read td 输出结果为 Excited State 1: Singlet-A...BODIPY.chk #p pbe1pbe/6-31+G* scrf=pcm geom=allcheck guess=read td=eqsolv 输出结果为 Excited State 1: Singlet-A
100 & nFeature_RNA < 10000 & percent.mt < 10) ob.list[[i]] <- subset(ob.list[[i]], subset = Doublet_Singlet...== "Singlet") ob.list[[i]] <- NormalizeData(ob.list[[i]]) ob.list[[i]] <- FindVariableFeatures(ob.list...1000 & nFeature_RNA < 10000 & percent.mt < 10) ob.list[[i]] <- subset(ob.list[[i]], subset = Doublet_Singlet...== "Singlet") ob.list[[i]] <- NormalizeData(ob.list[[i]]) ob.list[[i]] <- FindVariableFeatures(ob.list
G2M.Score Phase l1_AAACCTGAGCCAGAAC rep1-tx rep1-tx STAT2g2 Singlet...0.77130934 G1 l1_AAACCTGAGTGGACGT rep1-tx rep1-tx CAV1g4 Singlet...0.33260303 G1 l1_AAACCTGCATGAGCGA rep1-tx rep1-tx STAT1g2 Singlet...0.69441836 G1 l1_AAACCTGTCTTGTCAT rep1-tx rep1-tx CD86g1 Singlet...0.03781951 G1 l1_AAACGGGAGAACAACT rep1-tx rep1-tx IRF7g2 Singlet
string> rowCellValues) { List errCells = new List(); T singleT...= Activator.CreateInstance(); foreach (PropertyInfo pi in singleT.GetType().GetProperties
BODIPY-based Photodynamic Agents for Exclusively Generating Superoxide Radical over Singlet Oxygen.
S1态的输出为: Excited State 1: Singlet-A 4.4377 eV 279.39 nm f=0.0390 =0.000 38-...S2态的输出为: Excited State 2: Singlet-A 4.5032 eV 275.33 nm f=0.7058 =0.000 38-
doublet.calls) <- "Call"##dplyr::filter:数据过滤rna.dub <- dplyr::filter(doublet.calls, Call == "Doublet")rna.singlet...<- dplyr::filter(doublet.calls, Call == "<em>Singlet</em>")DF.doublets <- rownames(rna.dub)# Intersect doublet
{ // SingleObject object = new SingleObject(); // 编译时错误:构造函数 SingleObject() 是不可见的 Singleton SingleT...= Singleton.newinstance(); //获取唯一可用的对象 SingleT.dosomething(); } } 执行程序,输出结果: 一个女朋友就够了!!!
以单重态O2为例,Gaussian输入文件示例如下 %chk=O2_uhf.chk #p UHF/def2TZVP nosymm guess=mix stable=opt sp of singlet...自然轨道 仍以单重态O2为例,Gaussian输入文件示例如下 %chk=O2_uccsd.chk #p UHF/def2TZVP nosymm guess=mix stable=opt sp of singlet
这在传统的自旋单态(spin-singlet state)超导体中是不可能发生的。 具有自旋单态的绝大多数超导体,库珀对中的两个电子具有相反的自旋,而高磁场就可轻松破坏这种自旋单态超导体的超导性。
nstates=10) b3lyp/6-311G(d,p) guess=read geom=allcheck 此时,我们可以看到第一激发态的信息如下: Excited State 1: Singlet-A
若要指定电荷,使用charge=n,而指定多重度则直接写singlet、doublet、triplet等或通过MS=n来设定。 接下来便是结构优化信息: ?
$HTO_classification.global) pbmc.hashtag@assays table(pbmc.hashtag$hash.ID) # ## Doublet Negative Singlet
在本文中,作者提出了一种算法,该算法利用遗传变异 (eQTL) 来确定每个包含单个细胞的液滴 (singlet) 的遗传身份,并识别包含来自不同个体的两个细胞的液滴 (doublet)。
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