02:52

scRNA-seq -Integrated analysis: Introduction, preprocessing and combining samples (Seurat v5)

06:14

scRNA-seq -Integrated analysis: Aligning samples and clustering

05:03

scRNA-seq -Integrated analysis: Conserved markers and differentially expressed genes (Seurat v5)

04:00

scRNAseq: Pseudobulk analysis

05:02

8 Visium data (2024): Integration with single-cell RNA-seq reference data

01:14

7 Visium data (2024): Subsetting out anatomical regions

05:56

6 Visium data (2024): Visualizing gene expression and identifying spatially variable genes

04:27

9 Visium data (2024): Analysis with multiple samples

03:16

5 Visium data (2024): Clustering

04:10

4 Visium data (2024): Normalization and PCA

01:12

3 Visium data (2024): Filtering

03:19

2 Visium data (2024): Setup and quality control

11:44

scRNA-seq: Remove background contamination with CellBender

04:38

8 Visium data: Subset and integrate with single-cell data (update, August 2023)

11:19

5b. Trimming and filtering single-end reads (Ion Torrent data)

15:56

scRNA-seq: Quality control and filtering cells (update, July 2023)

03:57

5a. Filter contigs and remove identical sequences

01:58

4b. Converting VSEARCH contigs for Mothur analysis

03:47

4a. Expected error filtering with VSEARCH

07:25

3. Combine paired reads to contigs with VSEARCH

05:36

scRNA-seq: Normalize gene expression values with SCTransform

04:06

scRNA-seq: SingleR annotations

07:06

scRNA-seq: Extract information from Seurat object

15:05

scRNA-seq: Quality control and filtering cells (update, June 2023)

05:31

9 Visium data: Identifying cell types using deconvolution

07:51

8 Make a phyloseq object for ASV data in Chipster

08:25

6 Make an ASV table and remove chimeras with DADA2

06:07

7 Assign Taxonomy with DADA2

13:51

4 Sample (ASV) inference with DADA2

04:28

5 Combine paired reads to contigs with DADA2

11:07

3 Trim primers and adapters with Cutadapt

10:45

2 Filter and trim reads with DADA2

05:30

1 Introduction to amplicon sequence variant (ASV) analysis

13:06

3 Microbiome package ecosystem

16:22

2 Microbiome data containers in R/Bioconductor

13:48

1 Microbiome data science workflow

05:05

1 Visium data: Introduction

03:33

6 Visium data: PCA and clustering

01:20

5 Visium data: Combine multiple samples

01:17

4 Visium data: Visualize gene expression

02:36

7 Visium data: Identify spatially variable genes

02:03

2 Visium data: Set up Seurat object and perform QC

01:59

3 Visium data: Filter low quality spots and normalize

07:43

Analysis of QuantSeq FWD UMI 3' RNA-seq data

27:22

Intro to spatial transcriptomics and SSAM by Naveed Ishaque

04:47

scRNA-seq: Update inc R-PCA and reference based integration for large datasets

03:06

scRNA-seq: Updates inc SCTransform and annotating clusters with SingleR

03:12

Define Samples to Enable “Run for Each Sample” Option

11:59

scRNA-seq: Introduction to single cell RNA-seq data analysis

10:19

scRNA-seq: Introduction to single cell RNA-seq data analysis

13:10

scRNA-seq: Detecting cluster marker genes

05:01

scRNA-seq -Integrated analysis: Conserved markers and differentially expressed genes

06:01

Directionality and paired-end reads in RNA-seq

02:07

Convert to Chipster format and how to create a phenodata file

07:59

1. Introduction to microbial community analysis

13:35

2. Quality control of raw reads

06:53

(3. Combine paired reads to contigs with Mothur)

06:08

6. Align sequences to reference template

04:35

7. Filter sequences and trim the alignment

05:45

8. Remove sequencing errors and chimeras