Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Genomics > Single Cell Lectures > Flashcards

Single Cell Lectures Flashcards

1
Q

Encode project

A

where all the parts are and what they do

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Gtex

A

after encode
variants and relate what it does

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

How do we understand the genotype effect

A

GWAS Catalog

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

The basic unit of life

A

the cell

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Hooke in 1665

A

first look at dead plant cells

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Leeuwenhoek in 1675

A

first look at a live cell

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Most diversity is in which organ tissue

A

brain-lots of different jobs

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

From all cells come

A

cells

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Human Cell Atlas Project

A

sequencing individual cells to find function and variants

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

How many tissues are in the human cell atlas project

A

1-2,000

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Bulk RNA sequencing

A

analyze gene expression change in a mixture of cell types

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Single cell RNA sequencing

A

analyzing gene expression in a single cell or nuclei

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Key advancements in single cell RNA seq

A

integrated fluidic circuits
nanodroplets
In situ barcoding

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

What drives scRNAseq technology adoption

A

cost
ease of the technique
data robustness
experimental objectives
personnel bias

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Splicing occurs in nuclei in

A

pre mRNA

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Live cell isolation technique

A

proteases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Nuclei isolation technique

A

detergents

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

Live cells give what type of RNA

A

mRNA

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

Nuclei gives what type of RNA

A

pre-mRNA- contains introns

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

Nuclei use advantages

A

sample processing logistics
pre-mRNA processing can be measured
less stress and mitochondrial signal
cell state is more accurately captured

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

Cell Use advantages

A

more complete transcriptome
detection sensitivity
better connection with translation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

What are the keys to single cell transcript identification

A

10x barcode- what cell
UMI- unique material id-unique material

30
Q

Major steps in single-cell or nuclei data processing

A

filtering noise
normalization
neighbor networks- dimension reduction and clustering

31
Q

A good droplet should include

A

barcode bead
cell

32
Q

Doublet

A

droplet with two cells

33
Q

Ambient RNA

A

relating to the immediate surroundings of something- RNA

34
Q

Which type-Nucleic or cell contain more ambient RNA

A

Nucleic because you have to pop the cell to get to the nucleus- allows ambient RNA in

35
Q

Normalization

A

the practice of organizing data entries to ensure they appear similar across all fields and records

36
Q

Why do we normalize the data

A

cells can have different numbers of gene counts owing to differences in mRNA containing volume (cell size) or purely randomly during sequencing

37
Q

What are batch effects

A

sequencing depth
technologies
sample quality
technician
cell cycle

38
Q

Two types of batch effects

A

technical and biological

39
Q

example of biological batch effects

A

cell cycle

40
Q

Principle components

A

when a collection of points in a real coordinate space are a sequence of unit vectors

41
Q

Principal components analysis

A

a process of computing the principle components and using them to perform a change of basis on the data

42
Q

Data integration is important because

A

it allows us to compare data
gets rid of bias

43
Q

Best practice for batch correction algorithm

A

Harmony

44
Q

Data integration steps

A
  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
Q

Two types of neighbor networks

A

dimension reduction
clustering

46
Q

Why do we use dimensionality reduction “Feature selection”

A

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
Q

PCA

A

principal components analysis projects a set of possibly correlated variables into a set of linear orthogonal variables

48
Q

t-SNE

A

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
Q

UMAP

A

uniform manifold approximation and projection. A UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology

50
Q

Dimensionality reduction is

A

highly variable

51
Q

Clustering

A

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
Q

Nearest neighbor graph

A

directed graph defined for a set of points in a metric space KNN

53
Q

K-means

A

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
Q

Hierarchical

A

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
Q

Community

A

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
Q

Steps for clustering

A

KNN graph
- find communities
initial partition
-refine
-aggregate network
-refine
Final partition

57
Q

Underlying concept of mapping cell clusters to cell identities

A

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
Q

discovery of differentially expressed genes steps

A

aligned dataset
integrated analysis
compare composition
compare expression for aligned cells

59
Q

Underlying concept for differential gene expression by cell type: bulk RNAseq principles

A

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
Q

Pseudo bulk analysis

A

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
Q

DEG process

A

the sample view aggregates counts per sample-label combination to create pseudobulks

62
Q

sc or snRNA Data Analysis Summary

A

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
Q

Deconvolution

A

a process of resolving something into its constituent elements or removing complication in order to clarify it

64
Q

Goal of deconvolution

A

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
Q

Trajectory

A

the curve that a body describes in space; a path, progression, or line of development resembling a physical trajectory

66
Q

Single-cell or trajectory Analysis

A

a collection of cells is a snapshot of their transcriptomes that are each at distinct points in their dynamic state of being

67
Q

Cell trajectory analysis

A

allocation of cells to lineages and then ordering them based on pseudotime values within lineages

68
Q

Pseudotime

A

the distance along the trajectory form its position back to the beginning

69
Q

Trajectory analyses outcomes

A

discover unique cell linages
estimate differences between differentially expressed genes between linages
determine which genes are potentially driving cell differentiation

70
Q

Trajectory goatl

A

estimate how gene expression levels change along cells or nuclei placed in a continuous path

71
Q

Transcription factors

A

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)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions