hello，大家好，這次我們來分享一下做軌跡分析的軟件----CytoTRACE,文章在Single-cell transcriptional diversity is a hallmark of developmental potential,2020年1月表達于science，相當牛了，跟URD有一拼。當然，關于軌跡分析的方法之前分享過很多了，比如單細胞數(shù)據(jù)擬時分析之VIA（我的優(yōu)勢你們比不了）,10X單細胞軌跡分析之回顧,擬時分析軟件Palantir，以及空間轉錄組軌跡分析的方法10X空間轉錄組的軌跡分析，今天我們來看看這個軟件有什么不同。

Abstract

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging（這個顯而易見）. Here, we demonstrate a simple, yet robust, determinant（決定條件） of developmental potential—the number of expressed genes per cell（基因表達的數(shù)量）—and leverage this measure of transcriptional diversity to develop a computational framework（依據(jù)基因表達的數(shù)量進行發(fā)育軌跡的推斷？？牛啊） (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories（背景倒是很豐厚）. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.（看來這個方法有很多值得一看的地方）。

introduction

Inmulticellular organisms, tissues are hierarchically organized into distinct cell types and cellular stateswith intrinsic differences in function and developmental potential。當然，目前已經(jīng)有了很多新的方法，但是 Though powerful, these technologies cannot be applied to human tissues in vivo and generally require prior knowledge of cell type–specific genetic markers（做軌跡分析必須先進行細胞定義，否則都是耍流氓）。These limitations have made it difficult to study the developmental organization of primary human tissues under physiological and pathological conditions。（不知道大家擬時分析的時候，研究的有多深）。
Single-cell RNA-sequencing (scRNA-seq) has emerged as a promising approach to study cellular differentiation trajectories at high resolution in primary tissue specimens（單細胞確實是一個劃時代的技術），目前大多數(shù)軌跡分析的軟件需要：
（1）a priori knowledge of the starting point (and thus, direction) of the inferred biological process（先驗知識，不進行細胞定義直接做軌跡分析就是耍流氓）。
（2）the presence of intermediate cell states to reconstruct the trajectory（含有細胞分化的中間態(tài)，理論上是這樣）。
These requirements can be challenging to satisfy in certain contexts, such as human cancer development（研究腫瘤樣本單細胞數(shù)據(jù)的童鞋是不是深有體會？）。
目前的方法還有一個缺點：
with existing in silico approaches, it is difficult to distinguish quiescent（靜止的） (noncycling) adult stem cells that have long-term regenerative potential frommore specialized cells（這種情況其實在我們研究單細胞數(shù)據(jù)的情況下非常少見），而且gene expression–basedmodels utility across diverse developmental systems and single-cell sequencing technologies is still unclear.
Here,we systematically evaluated RNA-based features, including nearly 19,000 annotated gene sets, to identify factors that accurately predict cellular differentiation status independently of tissue type, species, and platform.（開始夸自己的軟件了），我們來看一下這個軟件的理論和運用吧

Result1 RNA-based correlates of single-cell differentiation states（最關鍵的地方）

Our initial goal was to identify robust, RNAbased determinants of developmental potential potential without the need for a priori knowledge of developmental direction or intermediate cell states marking cell fate transitions.（沒有先驗知識的前提下識別發(fā)育的方向和細胞的轉變），Using scRNA-seq data, we evaluated ~19,000 potential correlates of cell potency, including all available gene sets in the Molecular Signatures Database。896 gene sets covering transcription factor binding sites from ENCODE (17) and ChEA (18), an mRNA expression–derived stemness index (mRNAsi) (15), and three computational techniques that infer stemness as a measure of transcriptional entropy（這個地方了解一下就可以了），We also explored the utility of “gene counts,” or the number of detectably expressed genes
per cell. Although anecdotally observed to correlate with differentiation status in a limited number of settings（這也是文章的重點，基因數(shù)量和發(fā)育的關系），the reliability of this association and whether it reflects a general property of cellular ontogeny are unknown.
To assess these RNA-based features, we compiled a training cohort consisting of nine gold standard scRNA-seq datasets with experimentally confirmed differentiation trajectories.These datasets were selected to prioritize commonly used benchmarking datasets from earlier studies and to ensure a broad sampling of developmental states from the mammalian zygote to terminally differentiated cells（這才是真正的發(fā)育軌跡）。Overall, the training cohort encompassed 3174 single cells spanning 49 phenotypes, six biological systems, and three scRNA-seq platforms（種類很齊全）。To evaluate performance, we used Spearman correlation to compare each RNA-based feature, averaged by phenotype, against known differentiation states。We then averaged the results across the nine training datasets to yield a final score and rank for every feature（相關性檢驗）。
This systematic screen revealedmany known and unexpected correlates of differentiation status

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接下來是對染色體可及性和發(fā)育關系的研究
tested whether single-cell gene counts are ultimately a surrogate for global chromatin accessibility, which has been shown to decrease with differentiation in certain contexts，genome-wide chromatin accessibility was observed to progressively decrease with differentiation of hESCs into paraxial mesoderm and lateral mesoderm lineages（這個結果都能猜到）

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Result2 Development of CytoTRACE

The number of expressed genes per cell generally showed consistent performance with respect to key technical parameters and was generally correlated with mRNA content(這個自然)，However, in some datasets, such as that for in vitro differentiation of hESCs into the gastrulation layers, the number of expressed genes per cell exhibited considerable intraphenotypic variation（表型的部分其實單細胞用到的相對還少一點，但是ATAC的內容也相當重要）

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Result3 Performance evaluation across tissues, species, and platforms（多種來源的數(shù)據(jù)，這部分我們簡單看一下）

When assessed at the single-cell level, CytoTRACE outperformed all evaluated RNAbased features in the validation cohort，

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achieving a substantial gain in performance over the second-highest-ranking approach

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Similar improvements were observed acrossmany complex systems, including bone marrow differentiation

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In addition, CytoTRACE results were positively correlated with the direction of differentiation in 88% of datasets（已知發(fā)育軌跡的數(shù)據(jù)來驗證軟件的準確性，當然都不錯）。
Moreover, no significant biases in performance were observed in relation to tissue type, species, the number of cells analyzed, time series experiments versus snapshots of developmental states, or
plate-based versus droplet-based technologies（bias很小，這個不錯）。
接下來還和RNA velocyto的結果進行比較，當然，cytoTrace的結果相當不錯

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Result 4 Stem cell–related genes and hierarchies

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Result5 Application to neoplastic disease

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接下來看看示例代碼

Running CytoTRACE

Load CytoTRACE in R with library(CytoTRACE). The package contains the following contents:

Cytotrace(): function to run CytoTRACE on a custom scRNA-seq dataset
iCytoTRACE: function to run CytoTRACE across multiple, heterogeneous scRNA-seq batches/dataset
plotCytoTRACE: function to generate 2D visualizations of CytoTRACE, phenotypes, and gene expression
Two bone marrow differentiation scRNA-seq datasets (marrow_10x_expr and marrow_plate_expr) with corresponding phenotype labels (marrow_10x_pheno and marrow_plate_pheno)

Example I: Run CytoTRACE on a custom scRNA-seq dataset

Use the bone marrow 10x scRNA-seq dataset to run CytoTRACE

results <- CytoTRACE(marrow_10x_expr)

CytoTRACE will automatically run on fast-mode, a subsampling approach used to reduce runtime and memory usage, when the number of cells in the dataset exceeds 3,000. Users can additionally multi-thread using 'ncores' (default = 1) or indicate subsampling size using 'subsamplingsize' (default = 1,000 cells). Run the following dataset on fast mode using 8 cores and subsample size of 1,000.

results <- CytoTRACE(marrow_10x_expr, ncores = 8, subsamplesize = 1000)

The ouput is a list object containing numeric values for CytoTRACE (values ranging from 0 (more differentiated) to 1 (less differentiated)), ranked CytoTRACE, GCS, and gene counts, a numeric vector of the Pearson correlation between each gene and CytoTRACE, a numeric vector of the Pearson correlation between each gene and gene counts, the IDs of filtered cells, and a normalized gene expression table (see package documentation for more details).
Example II: Run iCytoTRACE on multiple scRNA-seq batches/datasets

Run iCytoTRACE on a list containing two bone marrow scRNA-seq datasets profiled on different platforms, 10x and Smart-seq2

datasets <- list(marrow_10x_expr, marrow_plate_expr)
results <- iCytoTRACE(datasets)

The ouput is a list object containing numeric values for the merged CytoTRACE (values ranging from 0 (more differentiated) to 1 (less differentiated)), ranked CytoTRACE, GCS, gene counts, the Scanorama-corrected gene expression matrix, the merged low dimensional embedding, and the IDs of filtered cells (see package documentation for more details).
Example III: Plot CytoTRACE and iCytoTRACE results
Visualizing CytoTRACE results

Generate 2D plots and tables to visualize CytoTRACE, known phenotypes, and gene expression. The current implementation uses t-SNE for dimensional reduction but users can also input their own embeddings. At minimum, the plotCytoTRACE function takes as input a list object generated by either the CytoTRACE or iCytoTRACE functions. Users can also optionally provide phenotype labels or gene names to generate additional plots. Boxplots of CytoTRACE by phenotype labels are automatically generated when phenotype labels are provided.

plotCytoTRACE(results, phenotype = marrow_10x_pheno, gene = "Kit")

The function saves two files to disk: -a pdf of 2D embedded plots colored by CytoTRACE, and, if provided, phenotype labels, and gene expression. -a tab-delimited text file containing a table of CytoTRACE values t-SNE embeddings, and, if provided, phenotype labels and gene expression values.
Visualizing genes associated with CytoTRACE

Generate a bar plot to visualize genes associated with CytoTRACE. At minimum, the plotCytoGenes function takes as input a list object generated by either the CytoTRACE or iCytoTRACE functions. Users can also indicate the number of genes and colors to display.

plotCytoGenes(results, numOfGenes = 10)

The function saves one file to disk:

a pdf of bar plots indicating the genes associated with least and most differentiated cells based on correlation with CytoTRACE.

參考網(wǎng)址在CytoTRCAE

代碼相當簡單，大家自己試一下吧，不過從結果看，人為監(jiān)督必不可少

生活很好，有你更好