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Single Cell Lectures Flashcards

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1
Q

Encode project

A

where all the parts are and what they do

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2
Q

Gtex

A

after encode
variants and relate what it does

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3
Q

How do we understand the genotype effect

A

GWAS Catalog

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4
Q

The basic unit of life

A

the cell

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5
Q

Hooke in 1665

A

first look at dead plant cells

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6
Q

Leeuwenhoek in 1675

A

first look at a live cell

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7
Q

Most diversity is in which organ tissue

A

brain-lots of different jobs

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8
Q

From all cells come

A

cells

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9
Q

Human Cell Atlas Project

A

sequencing individual cells to find function and variants

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10
Q

How many tissues are in the human cell atlas project

A

1-2,000

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11
Q

Bulk RNA sequencing

A

analyze gene expression change in a mixture of cell types

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12
Q

Single cell RNA sequencing

A

analyzing gene expression in a single cell or nuclei

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13
Q

Why use a single cell perspective

A

basic tenet of biological variation
- within organ systems individual cell types or their subtypes vary proportionally and behave transcriptionally different depending on their environment

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14
Q

Why sc/snRNAseq approach can help solve biological problems

A

multiple hypothesis testing with cell types
- cell type composition often changes in an organ over time or upon perturbation
-cell to cell communication through altered gene expression is dynamic between cell types
-individual gene expression by cell type can vary within an organ across individuals and disease vs. healthy
- gene expression changes in one cell type can alter the fate of differentiation of other cells

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15
Q

Key advancements in single cell RNA seq

A

integrated fluidic circuits
nanodroplets
In situ barcoding

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16
Q

What drives scRNAseq technology adoption

A

cost
ease of the technique
data robustness
experimental objectives
personnel bias

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17
Q

Major steps to sc/snRNAseq data generation

A
  1. lipid encapsulation of beads, cells and transcription enzyme mix
  2. cell lysis and mRNA binding to the capture beads
  3. cDNA synthesis with reverse transcriptase
  4. pooling all multi-barcoded cDNA and sequencing
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18
Q

Splicing occurs in nuclei in

A

pre mRNA

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19
Q

What do we gain form cell atlas data

A

molecular profiles that define cell type and their subtypes
unique cell types by tissue
gene markers that define cell type
the general transcriptional behavior of cell types

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20
Q

Tissue preparation

A
  1. dissect tissues-> live cells use enzymatic digestion
  2. filter out everything except cells
  3. FACS/MACS sorting of cells
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21
Q

Cell/Nuclei isolation points

A

Tissue source will dictate isolation protocols
cell liberation conditions highly variable by tissue source
cell lysis conditions highly variable by tissue source for nuclei preparation

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22
Q

Live cell isolation technique

A

proteases

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23
Q

Nuclei isolation technique

A

detergents

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24
Q

A mammalian diploid cell has

A

10-30 pg total RNA and <0.1 pg mRNA
nuclear RNA is 10-20% of total RNA

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25
Live cells give what type of RNA
mRNA
26
Nuclei gives what type of RNA
pre-mRNA- contains introns
27
Nuclei use advantages
sample processing logistics pre-mRNA processing can be measured less stress and mitochondrial signal cell state is more accurately captured
28
Cell Use advantages
more complete transcriptome detection sensitivity better connection with translation
29
What are the keys to single cell transcript identification
10x barcode- what cell UMI- unique material id-unique material
30
Major steps in single-cell or nuclei data processing
filtering noise normalization neighbor networks- dimension reduction and clustering
31
A good droplet should include
barcode bead cell
32
Doublet
droplet with two cells
33
Ambient RNA
relating to the immediate surroundings of something- RNA
34
Which type-Nucleic or cell contain more ambient RNA
Nucleic because you have to pop the cell to get to the nucleus- allows ambient RNA in
35
Normalization
the practice of organizing data entries to ensure they appear similar across all fields and records
36
Why do we normalize the data
cells can have different numbers of gene counts owing to differences in mRNA containing volume (cell size) or purely randomly during sequencing
37
What are batch effects
sequencing depth technologies sample quality technician cell cycle
38
Two types of batch effects
technical and biological
39
example of biological batch effects
cell cycle
40
Principle components
when a collection of points in a real coordinate space are a sequence of unit vectors
41
Principal components analysis
a process of computing the principle components and using them to perform a change of basis on the data
42
Data integration is important because
it allows us to compare data gets rid of bias
43
Best practice for batch correction algorithm
Harmony
44
Data integration steps
1. soft assign cells to clusters, favoring mixed dataset representation 2. get cluster centroids for each data set 3. get dataset correction factors for each cluster 4. move cells based on soft cluster membership
45
Two types of neighbor networks
dimension reduction clustering
46
Why do we use dimensionality reduction "Feature selection"
with thousands of individual cells and genes per cell for each sample it is necessary to reduce the complexity of the data for visual inspection and to facilitate downstream clustering
47
PCA
principal components analysis projects a set of possibly correlated variables into a set of linear orthogonal variables
48
t-SNE
t-distributed stochastic neighbor embedding creates a probability distribution using the Gaussian distribution that defines the relationships between the points in high-dimensional space
49
UMAP
uniform manifold approximation and projection. A UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology
50
Dimensionality reduction is
highly variable
51
Clustering
grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups
52
Nearest neighbor graph
directed graph defined for a set of points in a metric space KNN
53
K-means
interactively finds a predefined number of k cluster centers (centroids) by minimizing the sum of the squared Euclidean distance between each cell and its closest centroid
54
Hierarchical
two types 1) agglomerative- individual cells are progressively merged into clusters according to distance measures 2) divisive- each cell is split into small groups recursively until individual data level
55
Community
nodes refer to cells and cell-cell pairwise distances are applied in the Leiden algorithm Optimizing graph modularity locally on all nodes, then each small community is grouped into one node and the first step is repeated
56
Steps for clustering
KNN graph - find communities initial partition -refine -aggregate network -refine Final partition
57
Underlying concept of mapping cell clusters to cell identities
a set of genes within a cluster of cells or nuclei will be significantly different in their level of expression compared to all other clusters of cells or nuclei
58
discovery of differentially expressed genes steps
aligned dataset integrated analysis compare composition compare expression for aligned cells
59
Underlying concept for differential gene expression by cell type: bulk RNAseq principles
single cell data sets are negative binomial distributed that is define as a discrete probability distribution that models the number of failures in a sequence of independent and identically distributed trials before a specified number of successes occur
60
Pseudo bulk analysis
the method applies generalized linear mixed models with random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual
61
DEG process
the sample view aggregates counts per sample-label combination to create pseudobulks
62
sc or snRNA Data Analysis Summary
sequence reads generate count matrix filter cells using quality metrics normalize data and regress out unwanted variation- integration clustering marker identification 1) trajectory analysis 2) DE of cell types or genes between sample groups 3) custom analyses
63
Deconvolution
a process of resolving something into its constituent elements or removing complication in order to clarify it
64
Goal of deconvolution
estimate the proportion of a cell type present among a heterogenous mixture of cells using expressed marker genes that define a specific cell type
65
Trajectory
the curve that a body describes in space; a path, progression, or line of development resembling a physical trajectory
66
Single-cell or trajectory Analysis
a collection of cells is a snapshot of their transcriptomes that are each at distinct points in their dynamic state of being
67
Cell trajectory analysis
allocation of cells to lineages and then ordering them based on pseudotime values within lineages
68
Pseudotime
the distance along the trajectory form its position back to the beginning
69
Trajectory analyses outcomes
discover unique cell linages estimate differences between differentially expressed genes between linages determine which genes are potentially driving cell differentiation
70
Trajectory goatl
estimate how gene expression levels change along cells or nuclei placed in a continuous path
71
Transcription factors
a protein that controls the rate of transcription of genetic information from DNA to messenger RNA, by binding to a specific DNA sequence

Genomics (11 decks)

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