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Wslam company should already have analyzed is't it so? I think you jsut need to interpret

Wslam company should already have analyzed is't it so? I think you jsut need to interpret

Thanks for message Muhammad Afzal, stay in touch

Sure Sir

If you’re encountering memory issues while downloading and processing large datasets in R, there are several strategies you can try to handle large data more effectively: 1. Use Data.table for Efficient Data Handling: data.table is designed for high-performance data manipulation. Make sure you’re using it correctly. library(data.table) # Read the data in chunks fread("large_file.csv", select = c("column1", "column2"), nrows = 100000) 2. Increase Memory Limit: R has a memory limit that you can increase. On Windows, you can use: memory.limit(size = 56000) # Set memory limit to 56 GB 2. On Linux, you might need to adjust ulimit: ulimit -n 4096 # Increase file descriptor limit 3. Use Data.table’s fread with Specific Parameters: fread is optimized for speed and memory usage. You can specify data types and skip rows to reduce memory load. dt <- fread("large_file.csv", select = c("column1", "column2"), na.strings = c("", "NA"), colClasses = c("integer", "character")) 4. Process Data in Chunks: If the dataset is too large to load into memory all at once, process it in chunks: chunk_size <- 100000 # Define chunk size data_list <- list() con <- file("large_file.csv", open = "r") while (length(chunk <- read.csv(con, nrows = chunk_size)) > 0) { data_list[[length(data_list) + 1]] <- chunk } close(con) large_data <- do.call(rbind, data_list) 5. Consider Using Data.table’s fread for Large Files: fread is efficient and can handle large files well. Example: library(data.table) dt <- fread("large_file.csv") 6. Use Database Systems for Large Datasets: For very large datasets, consider using a database system like SQLite, MySQL, or PostgreSQL, and use R’s DBI package to query data directly. library(DBI) con <- dbConnect(RSQLite::SQLite(), dbname = "large_data.sqlite") data <- dbGetQuery(con, "SELECT * FROM large_table") dbDisconnect(con) 7. Use Parallel Processing: Utilize parallel processing to handle large datasets more efficiently. Use the parallel or future package. library(future) plan(multisession) # Use multiple cores results <- future_lapply(1:10, function(i) { # Your processing code here fread("large_file.csv", skip = (i-1)*100000, nrows = 100000) }) Try these strategies based on the nature of your data and the specific memory limitations you’re facing. Adjust the chunk sizes and memory limits according to your system’s capacity.

Don’t upload the panels separately one by one like a b and c, rather you can combined all panels into ppt first export a combine figure and add it into attachments then you can write a note to editorial staff they will help you deal with this.

All you can mention them in email or in note

They will help you to deal with this

@@asifmolbio The figures are already combined into one figure with all the subparts. But the problem is they have put it right after the first result i.e. subpart a, whereas, I want it to be at the end of all the three results i.e. a b,c.

@@asifmolbio thanks. sure i'll try.

Glad you like it

Thanks

Can try

Glad you like it

It will be in ncbi website, your account will have

Welcome

Thanks for suggesting a good topic, i will make a video on it IA, stay tunned

@@HariSharma-yl2ji glad if its good

Hi, It seems there might be some confusion. Let’s clarify the concepts of enrichment factor and the number of enriched genes: 1. Enrichment Factor: This is a measure used in gene enrichment analysis to determine how significantly a set of genes is overrepresented in a specific biological pathway or process compared to what would be expected by chance. A higher enrichment factor indicates that the genes are more overrepresented. 2. Number of Enriched Genes: This is simply the count of genes in your set that are found in a particular pathway or process. In a typical enrichment analysis bubble plot: • Bubble Size: Often represents the number of enriched genes. • Bubble Position: X and Y axes can represent different parameters such as p-value, enrichment factor, or other statistical measures. Clarifying the Statement A low enrichment factor does not necessarily mean a higher number of enriched genes. Instead: • Low Enrichment Factor: Indicates that the genes are not significantly overrepresented in the pathway, even if a considerable number of genes are involved. • High Enrichment Factor: Indicates that the genes are significantly overrepresented in the pathway, even if the number of genes might be smaller. Interpreting the Diagram If you see bubbles with a low enrichment factor and a low number of genes, it means that those genes are not significantly overrepresented in that particular pathway. Conversely, bubbles with a high enrichment factor, even with a lower number of genes, indicate a significant overrepresentation. In summary, a low enrichment factor does not correlate with a high number of enriched genes. Instead, it suggests a lesser significance of those genes in the specific pathway being analyzed. Feel free to share the diagram if you need a more specific explanation based on the visual data.

Thank you for for the explanation

How to perform Gene Ontology enrichment analysis using Gene IDs and logFC only czcams.com/video/XVYBxgfwpZ0/video.html

Check this out

I am not able to make data in excel for drawing Sankey diagram and Go chord plots, can you please show me how can I make such data, or where from can I get data for plotting sankey and go chord plots please

Glad if it’s helping

asifalikalas

here is my we chat ID

Sure will help you later

Welcome

Thanks for message, glad it its helpful

Thanks for message, glad it its helpful

Dear sir, Could you please tell me how you get or design the "sample information sheet". Could you please guide me about the previous procedure of microbiome analysis. I am working on the related to this. Looking for you reply.

Yes, here’s a bit more detail on how it works: ### BioProject - **Purpose**: BioProject is a higher-level organizational unit in the NCBI database that groups together related datasets. It encompasses various types of data, including genomic, transcriptomic, and metagenomic sequencing projects. - **Data Inclusion**: A BioProject can include multiple types of data, such as raw sequencing reads, assembled sequences, and metadata. It serves as an umbrella project that links to other related records in NCBI, such as BioSample, SRA, and GenBank records. ### BioSample - **Purpose**: BioSample provides detailed information about the biological material used in a study, including the sample’s source, characteristics, and treatment. - **ID Conversion**: When you submit a BioSample to NCBI, it receives a BioSample ID. This ID is then used to associate specific datasets, like sequencing reads, with the sample. ### Sequence Read Archive (SRA) - **Purpose**: SRA stores raw sequencing data. This includes reads from sequencing machines and their associated metadata. - **ID Conversion**: When you submit raw sequencing data to NCBI, the BioSample ID is linked to an SRA accession ID. This link is typically created during the submission process. The sequencing reads are associated with a specific BioSample through this ID. ### Workflow 1. **Create a BioProject**: This serves as the main project identifier. 2. **Submit BioSamples**: For each biological sample, submit detailed metadata to receive BioSample IDs. 3. **Submit Sequencing Data**: Upload your raw sequencing data to SRA, referencing the BioSample IDs. This links the sequencing data with the corresponding samples. 4. **NCBI Processing**: NCBI will process and associate your submissions, creating SRA IDs for the sequencing data linked to your BioSample IDs. ### Accessing Data - **BioProject Page**: The BioProject page will provide links to all associated BioSamples, SRA datasets, and other relevant records. - **BioSample Page**: The BioSample page will link to the SRA records that contain the sequencing data derived from that sample. - **SRA Page**: The SRA record will detail the raw sequencing reads, linked back to the BioSample and BioProject records. ### Example Workflow: 1. **Submit BioProject**: Create a new BioProject and receive a BioProject ID. 2. **Submit BioSamples**: Upload information about each biological sample and get BioSample IDs. 3. **Submit to SRA**: Upload raw sequencing data, specifying the BioSample IDs. NCBI will assign SRA IDs to these datasets and link them to the corresponding BioSample and BioProject. This structured approach ensures that all data associated with your sequencing project is well-organized and easily accessible through NCBI’s databases.

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Yes many more are coming soon

Thanks for comment, stay tuned for more

Glad if it’s helping

❤️

Glad if it’s helping

@@myideology4123 glad if it’s helping

Which data?

@@asifmolbio after doing crispr when we grow plants and then send T0 generation for cas9 sequencing to company for seeing which samples has gene knock out. That data

Can i discuss with you? Any contact ?

asifalikalas@foxmail.com

Office 2019

I have added your suggestion to upcoming list

Thanks for suggesting a potential topic

Glad you like it

Sure can you clarify what specifically you want me to address?

What’s error

@@asifmolbio there is a warning message ""The code execution cannot proceed because xnmhn458.dII was not found. Reinstalling the program may fix this problem". This is the message what I got.

Its already there search video on iDEP on my channel

Can you further clarify your question?

drive.google.com/file/d/1giD8389NzgZgssX4upT6wkTbrIXEb7Pj/view?usp=drive_link

Here you go

Yes right