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Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

Minghui Wang, Won-min Song, Chen Ming and 9 more (opens in a new tab)

Molecular Neurodegeneration · 2022

doi:10.1186/s13024-022-00517-z (opens in a new tab)

References 514

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    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  240. 240.
    Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference · 2018
    No notice
  241. 241.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  242. 242.
    A general and flexible method for signal extraction from single-cell RNA-seq data · 2018
    Corrected
  243. 243.
    Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications · 2018
    No notice
  244. 244.
    Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs · 2018
    No notice
  245. 245.

    This reference’s wording can’t be shown under the paper’s licence.

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  246. 246.

    This reference’s wording can’t be shown under the paper’s licence.

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  247. 247.

    This reference’s wording can’t be shown under the paper’s licence.

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  248. 248.

    This reference’s wording can’t be shown under the paper’s licence.

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  249. 249.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  250. 250.

    This reference’s wording can’t be shown under the paper’s licence.

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  251. 251.
    An accurate and robust imputation method scImpute for single-cell RNA-seq data · 2018
    No notice
  252. 252.

    This reference’s wording can’t be shown under the paper’s licence.

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  253. 253.

    This reference’s wording can’t be shown under the paper’s licence.

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  254. 254.

    This reference’s wording can’t be shown under the paper’s licence.

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  255. 255.

    This reference’s wording can’t be shown under the paper’s licence.

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  256. 256.

    This reference’s wording can’t be shown under the paper’s licence.

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  257. 257.

    This reference’s wording can’t be shown under the paper’s licence.

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  258. 258.

    This reference’s wording can’t be shown under the paper’s licence.

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  259. 259.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  260. 260.

    This reference’s wording can’t be shown under the paper’s licence.

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  261. 261.
    SCANPY: large-scale single-cell gene expression data analysis · 2018
    No notice
  262. 262.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  263. 263.

    This reference’s wording can’t be shown under the paper’s licence.

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  264. 264.

    This reference’s wording can’t be shown under the paper’s licence.

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  265. 265.

    This reference’s wording can’t be shown under the paper’s licence.

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  266. 266.

    This reference’s wording can’t be shown under the paper’s licence.

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  267. 267.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  268. 268.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  269. 269.
    Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor · 2018
    No notice
  270. 270.

    This reference’s wording can’t be shown under the paper’s licence.

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  271. 271.
    Brain Cell Type Specific Gene Expression and Co-expression Network Architectures · 2018
    Corrected
  272. 272.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  273. 273.
    Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity · 2018
    No notice
  274. 274.
    Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data · 2018
    No notice
  275. 275.

    This reference’s wording can’t be shown under the paper’s licence.

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  276. 276.

    This reference’s wording can’t be shown under the paper’s licence.

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  277. 277.

    This reference’s wording can’t be shown under the paper’s licence.

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  278. 278.

    This reference’s wording can’t be shown under the paper’s licence.

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  279. 279.

    This reference’s wording can’t be shown under the paper’s licence.

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  280. 280.

    This reference’s wording can’t be shown under the paper’s licence.

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  281. 281.

    This reference’s wording can’t be shown under the paper’s licence.

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  282. 282.

    This reference’s wording can’t be shown under the paper’s licence.

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  283. 283.

    This reference’s wording can’t be shown under the paper’s licence.

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  284. 284.

    This reference’s wording can’t be shown under the paper’s licence.

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  285. 285.

    This reference’s wording can’t be shown under the paper’s licence.

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  286. 286.

    This reference’s wording can’t be shown under the paper’s licence.

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  287. 287.

    This reference’s wording can’t be shown under the paper’s licence.

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  288. 288.
    PU.1 regulates Alzheimer’s disease-associated genes in primary human microglia · 2018
    No notice
  289. 289.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  290. 290.

    This reference’s wording can’t be shown under the paper’s licence.

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  291. 291.

    This reference’s wording can’t be shown under the paper’s licence.

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  292. 292.

    This reference’s wording can’t be shown under the paper’s licence.

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  293. 293.

    This reference’s wording can’t be shown under the paper’s licence.

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  294. 294.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  295. 295.
    False signals induced by single-cell imputation · 2019
    No notice
  296. 296.

    This reference’s wording can’t be shown under the paper’s licence.

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  297. 297.

    This reference’s wording can’t be shown under the paper’s licence.

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  298. 298.

    This reference’s wording can’t be shown under the paper’s licence.

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  299. 299.

    This reference’s wording can’t be shown under the paper’s licence.

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  300. 300.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  301. 301.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  302. 302.
    Single-nucleus and single-cell transcriptomes compared in matched cortical cell types · 2018
    No notice
  303. 303.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  304. 304.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  305. 305.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  306. 306.
    Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data · 2019
    No notice
  307. 307.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  308. 308.

    This reference’s wording can’t be shown under the paper’s licence.

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  309. 309.

    This reference’s wording can’t be shown under the paper’s licence.

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  310. 310.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  311. 311.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  312. 312.
    Cell-specific network constructed by single-cell RNA sequencing data · 2019
    No notice
  313. 313.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  314. 314.

    This reference’s wording can’t be shown under the paper’s licence.

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  315. 315.

    This reference’s wording can’t be shown under the paper’s licence.

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  316. 316.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  317. 317.
    PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data · 2019
    No notice
  318. 318.

    This reference’s wording can’t be shown under the paper’s licence.

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  319. 319.

    This reference’s wording can’t be shown under the paper’s licence.

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  320. 320.
    Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM · 2019
    No notice
  321. 321.

    This reference’s wording can’t be shown under the paper’s licence.

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  322. 322.

    This reference’s wording can’t be shown under the paper’s licence.

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  323. 323.

    This reference’s wording can’t be shown under the paper’s licence.

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  324. 324.

    This reference’s wording can’t be shown under the paper’s licence.

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  325. 325.

    This reference’s wording can’t be shown under the paper’s licence.

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  326. 326.

    This reference’s wording can’t be shown under the paper’s licence.

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  327. 327.

    This reference’s wording can’t be shown under the paper’s licence.

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  328. 328.

    This reference’s wording can’t be shown under the paper’s licence.

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  329. 329.

    This reference’s wording can’t be shown under the paper’s licence.

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  330. 330.

    This reference’s wording can’t be shown under the paper’s licence.

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  331. 331.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  332. 332.
    Current best practices in single‐cell RNA‐seq analysis: a tutorial · 2019
    No notice
  333. 333.
    Interpretable dimensionality reduction of single cell transcriptome data with deep generative models · 2018
    No notice
  334. 334.
    Neuropathological correlates and genetic architecture of microglial activation in elderly human brain · 2019
    No notice
  335. 335.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  336. 336.

    This reference’s wording can’t be shown under the paper’s licence.

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  337. 337.

    This reference’s wording can’t be shown under the paper’s licence.

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  338. 338.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  339. 339.
    PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells · 2019
    No notice
  340. 340.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  341. 341.
    A statistical approach for identifying differential distributions in single-cell RNA-seq experiments · 2016
    No notice
  342. 342.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  343. 343.

    This reference’s wording can’t be shown under the paper’s licence.

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  344. 344.
    Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R · 2017
    No notice
  345. 345.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  346. 346.
    Single-cell RNA-seq denoising using a deep count autoencoder · 2019
    No notice
  347. 347.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  348. 348.
    Massively parallel digital transcriptional profiling of single cells · 2017
    No notice
  349. 349.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  350. 350.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  351. 351.
    Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics · 2018
    No notice
  352. 352.
    SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles · 2017
    No notice
  353. 353.
    VASC: Dimension Reduction and Visualization of Single-Cell RNA-Seq Data by Deep Variational Autoencoder · 2018
    No notice
  354. 354.
    variancePartition: interpreting drivers of variation in complex gene expression studies · 2016
    No notice
  355. 355.
    Bulk tissue cell type deconvolution with multi-subject single-cell expression reference · 2019
    No notice
  356. 356.
    Single-cell transcriptomics unveils gene regulatory network plasticity · 2019
    No notice
  357. 357.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  358. 358.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  359. 359.
    SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation · 2017
    No notice
  360. 360.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  361. 361.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  362. 362.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  363. 363.
    Accurate estimation of cell-type composition from gene expression data · 2019
    No notice
  364. 364.
    A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys · 2019
    No notice
  365. 365.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  366. 366.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  367. 367.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  368. 368.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  369. 369.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  370. 370.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  371. 371.
    A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing · 2019
    Corrected
  372. 372.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  373. 373.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  374. 374.

    This reference’s wording can’t be shown under the paper’s licence.

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  375. 375.
    scds: computational annotation of doublets in single-cell RNA sequencing data · 2019
    No notice
  376. 376.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  377. 377.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  378. 378.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  379. 379.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  380. 380.
    SCALE method for single-cell ATAC-seq analysis via latent feature extraction · 2019
    No notice
  381. 381.
    MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions · 2019
    No notice
  382. 382.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  383. 383.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  384. 384.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  385. 385.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  386. 386.
    DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data · 2019
    No notice
  387. 387.
    Assessment of computational methods for the analysis of single-cell ATAC-seq data · 2019
    No notice
  388. 388.
    The art of using t-SNE for single-cell transcriptomics · 2019
    No notice
  389. 389.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  390. 390.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  391. 391.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  392. 392.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  393. 393.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  394. 394.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  395. 395.
    Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference · 2019
    No notice
  396. 396.
    CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data · 2020
    No notice
  397. 397.
    Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression · 2019
    No notice
  398. 398.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  399. 399.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  400. 400.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  401. 401.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  402. 402.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  403. 403.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  404. 404.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  405. 405.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  406. 406.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  407. 407.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  408. 408.
    Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression · 2020
    Corrected
  409. 409.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  410. 410.
    Single-nucleus RNA-seq identifies Huntington disease astrocyte states · 2020
    No notice
  411. 411.
    Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants · 2020
    Corrected
  412. 412.
    Trajectory-based differential expression analysis for single-cell sequencing data · 2020
    No notice
  413. 413.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  414. 414.
    The single-cell eQTLGen consortium · 2020
    No notice
  415. 415.

    This reference’s wording can’t be shown under the paper’s licence.

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  416. 416.
    Decontamination of ambient RNA in single-cell RNA-seq with DecontX · 2020
    No notice
  417. 417.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  418. 418.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  419. 419.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  420. 420.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  421. 421.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  422. 422.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  423. 423.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  424. 424.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  425. 425.
    Single-cell RNA sequencing reveals the heterogeneity of liver-resident immune cells in human · 2020
    No notice
  426. 426.
    Accurate estimation of cell composition in bulk expression through robust integration of single-cell information · 2020
    Corrected
  427. 427.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  428. 428.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  429. 429.
    Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis · 2020
    No notice
  430. 430.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  431. 431.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  432. 432.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  433. 433.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  434. 434.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  435. 435.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  436. 436.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  437. 437.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  438. 438.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  439. 439.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  440. 440.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  441. 441.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  442. 442.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  443. 443.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  444. 444.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  445. 445.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  446. 446.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  447. 447.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  448. 448.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  449. 449.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  450. 450.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  451. 451.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  452. 452.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  453. 453.
    APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease · 2020
    Corrected
  454. 454.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  455. 455.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  456. 456.
    Single-cell multiomics: technologies and data analysis methods · 2020
    No notice
  457. 457.
    Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease · 2020
    No notice
  458. 458.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  459. 459.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  460. 460.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  461. 461.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  462. 462.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  463. 463.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  464. 464.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  465. 465.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  466. 466.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  467. 467.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  468. 468.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  469. 469.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  470. 470.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  471. 471.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  472. 472.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  473. 473.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  474. 474.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  475. 475.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  476. 476.
    Single-cell network biology for resolving cellular heterogeneity in human diseases · 2020
    No notice
  477. 477.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  478. 478.
    Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease · 2020
    No notice
  479. 479.

    This reference’s wording can’t be shown under the paper’s licence.

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  480. 480.

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